This study leveraged the rapid evolution of female pheromones across the Drosophila genus to gain insight into how males coordinately adapt their detection and interpretation of these chemical cues to hone their mating strategies. While in some Drosophila species females produce unique pheromones that act to attract and arouse their conspecific males, the pheromones of most species are sexually monomorphic such that females possess no distinguishing chemosensory signatures that males can use for mate recognition. By comparing several close and distantly-related Drosophila species, it was revealed that D. yakuba males have evolved the distinct ability to use a sexually-monomorphic pheromone, 7-tricosene (7-T), as an excitatory cue to promote courtship, a sensory innovation that enables D. yakuba males to court in the dark thereby expanding their reproductive opportunities. To gain insight into the neural adaptations that enable 7-T to act as an excitatory cue, the functional properties were compared of two key nodes within the pheromone circuits of D. yakuba and a subset of its closest relatives. The instructive role of 7-T in D. yakuba arises from concurrent peripheral and central circuit changes: a distinct subpopulation of sensory neurons has acquired sensitivity to 7-T which in turn selectively signals to a distinct subset of P1 neurons in the central brain that trigger courtship behaviors. Such a modular circuit organization, in which different sensory inputs can independently couple to multiple parallel courtship control nodes, may facilitate the evolution of mate recognition systems by allowing males to take advantage of novel sensory modalities to become aroused. Together, these findings suggest how peripheral and central circuit adaptations can be flexibly linked to underlie the rapid evolution of mate recognition and courtship strategies across species (Coleman, 2023).

Sequenced behaviours, including locomotion, reaching and vocalization, are patterned differently in different contexts, enabling animals to adjust to their environments. How contextual information shapes neural activity to flexibly alter the patterning of actions is not fully understood. Previous work has indicated that this could be achieved via parallel motor circuits, with differing sensitivities to context. This study demonstrated that a single pathway operates in two regimes dependent on recent sensory history. The Drosophila song production system was leveraged to investigate the role of several neuron types in song patterning near versus far from the female fly. Male flies sing 'simple' trains of only one mode far from the female fly but complex song sequences comprising alternations between modes when near her. Ventral nerve cord (VNC) circuits are shaped by mutual inhibition and rebound excitability between nodes driving the two song modes. Brief sensory input to a direct brain-to-VNC excitatory pathway drives simple song far from the female, whereas prolonged input enables complex song production via simultaneous recruitment of functional disinhibition of VNC circuitry. Thus, female proximity unlocks motor circuit dynamics in the correct context. A compact circuit model was constructed to demonstrate that the identified mechanisms suffice to replicate natural song dynamics. These results highlight how canonical circuit motifs can be combined to enable circuit flexibility required for dynamic communication (Roemschied, 2023).

Accepting or rejecting a mate is one of the most crucial decisions a female will make, especially when faced with food shortage. Previous studies have identified the core neural circuity from sensing male courtship or mating status to decision-making for sexual receptivity in Drosophila females, but how hunger and satiety states modulate female receptivity is poorly understood. This study identified the neural circuit and its neuromodulation underlying the hunger modulation of female receptivity. Adipokinetic hormone receptor (AkhR)-expressing neurons inhibit sexual receptivity in a starvation-dependent manner. AkhR neurons are octopaminergic and act on a subset of Octβ1R-expressing LH421 neurons. Knocking down Octβ1R expression in LH421 neurons eliminates starvation-induced suppression of female receptivity. It was further found that LH421 neurons inhibit the sex-promoting pC1 neurons via GABA-resistant to dieldrin (Rdl) signaling. pC1 neurons also integrate courtship stimulation and mating status and thus serve as a common integrator of multiple internal and external cues for decision-making (Sun, 2023).

Vision is critical for the regulation of mating behaviors in many species. This study discovered that the Drosophila ortholog of human GABA (A) -receptor-associated protein (GABARAP; Atg8a) is required to fine-tune male courtship by modulating the activity of visual feedback neurons, lamina tangential cells (Lat). GABARAP is a ubiquitin-like protein that regulates cell-surface levels of GABA (A) receptors. Knocking down GABARAP or GABA (A) receptors in Lat neurons or hyperactivating them induces male courtship toward other males. Inhibiting Lat neurons, on the other hand, delays copulation by impairing the ability of males to follow females. Remarkably, the human ortholog of Drosophila GABARAP restores function in Lat neurons. Using in vivo two-photon imaging and optogenetics, Lat neurons were shown to be functionally connected to neural circuits that mediate visually-guided courtship pursuits in males. This work reveals a novel physiological role for GABARAP in fine-tuning the activity of a visual circuit that tracks a mating partner during courtship (Mabuchi, 2023).

Female sexual behavior as an innate behavior is of prominent biological importance for survival and reproduction. However, molecular and circuit mechanisms underlying female sexual behavior is not well understood. This study identified the Cholecystokinin-like peptide Drosulfakinin (DSK) to promote female sexual behavior in Drosophila. Loss of DSK function reduces female receptivity while overexpressing DSK enhances female receptivity. Two pairs of Dsk-expressing neurons in the central brain were found to promote female receptivity. DSK peptide acts through one of its receptors, CCKLR-17D3, to modulate female receptivity. Manipulation of CCKLR-17D3 and its expressing neurons alters female receptivity. It was further revealed that the two pairs of Dsk-expressing neurons receive input signal from pC1 neurons that integrate sex-related cues and mating status. These results demonstrate how a neuropeptide pathway interacts with a central neural node in the female sex circuitry to modulate sexual receptivity (Wang, 2022).

This study systematically investigated DSK-mediated neuromodulation of female sexual receptivity. At the molecular level, it was revealed that DSK neuropeptide and its receptor CCKLR-17D3 are crucial for modulating female sexual receptivity. At the neuronal circuit level, it was identified that DSK neurons are the immediate downstream targets of sex-promoting pC1 neurons in controlling female sexual receptivity. Moreover, intersectional tools were employed to subdivide DSK neurons into medial DSK neurons (DSK-MP1) and lateral DSK neurons (DSK-MP3) and uncovered that DSK-MP1 neurons rather than DSK-MP3 neurons play essential roles in modulating female receptivity. Collectively, these findings illuminate a pC1-DSK-MP1-CCKLR-17D3 pathway that modulates female sexual behaviors in Drosophila (Wang, 2022).

The female sexual behavior is a complex innate behavior. The decision for the female to accept a courting male or not depends on not only sensory stimulation but also internal states. If the female is willing to mate, she slows down, pauses, and opens her vaginal plates to accept a courting male, if not, she extrudes her ovipositor to deter a courting male or flies away. Tbe results of this study show that DSK signaling is crucial for virgin female receptivity but has no effect on ovipositor extrusion behavior. How exactly does the DSK signaling regulate virgin female receptivity is still not clear. One possibility is that DSK signaling regulates pausing behavior in response to male courtship, as DSK receptor CCKLR-17D3 expresses in the central complex that has been found to be crucial for locomotor behaviors. However, no change was observed in locomotor activity in DSK or CCKLR-17D3 mutant females. Note only locomotor behavior was studied in single females but not in females paired with courting males due to technical limit for analysis, and it is possible that the DSK signaling does not affect general locomotor behavior but regulates courtship-stimulated pausing behavior (Wang, 2022).

The four pairs of DSK neurons are classified into two types (DSK-MP1 and DSK-MP3) based on the location of the cell bodies, and DSK-MP1 neurons extend descending fibers to ventral nerve cord. In this study, it was also found that activating DSK-MP1 neurons enhance female receptivity whereas inactivating DSK-MP1 neurons reduce female receptivity. Silencing adult Abd-B neurons and SAG neurons located in the abdominal ganglion inhibits female sexual receptivity. It has been found that Abd-B neurons control female pausing behavior, and it would be interesting to further investigate whether DSK-MP1 neurons relay information from Abd-B neurons to regulate pausing and receptivity in females. It is also noted that DSK-MP1 neurons extend projections to suboesophageal ganglion (SOG), and the SOG is the terminus of ascending projections from a subset of female reproductive tract sensory neurons labeled by pickpocket (ppk), fruitless (fru), and doublesex (dsx). It is also possible that DSK-MP1 neurons may integrate information directly from these sensory neurons to regulate female receptivity. Another study further classified the DSK-MP1 neurons into two types (MP1a and MP1b) based on the morphology of their neuritis. Future studies would further build genetic tools to uncover the function of each subset of DSK neurons in regulating distinct innate behaviors, such as male courtship, aggression, and female sexual behavior (Wang, 2022).

Previous studies have revealed that pC1 neurons extend projections to lateral protocerebral complex (LPC) and this neural cluster responds to courtship song and cVA. Moreover, recent works have shown that DSK-MP1 neurons project to the region of LPC. This study used GRASP, trans-Tango, and patch-clamp techniques and revealed that DSK-MP1 neurons are direct downstream target of R71G01-GAL4 neurons that include pC1. EM reconstruction revealed that pC1 neurons have intense synaptic input on MP1b but not MP1a neurons, suggesting a crucial role of the single pair of MP1b neurons in female receptivity. Based on these findings, it is propose that: (1) pC1 neurons act as a central node for female sexual receptivity by integrating sex-related sensory cues (courtship song and cVA) and mating status; and (2) DSK-MP1 neurons may integrate internal states and pC1-encoded information to modulate female sexual behavior. Thus, it is of prime importance to further investigate how a neuropeptide pathway modulate a core neural node in the sex circuitry to fine-tune the female's willingness for sexual behavior in the future (Wang, 2022).

Newborns and hatchlings can perform incredibly sophisticated behaviors, but many animals abstain from sexual activity at the beginning of life. Hormonal changes have long been known to drive both physical and behavioral changes during adolescence, leading to the largely untested assumption that sexuality emerges from organizational changes to neuronal circuitry. This study shows that the transition to sexuality in male Drosophila is controlled by hormonal changes, but this regulation is functional rather than structural. In very young males, a broadly acting hormone directly inhibits the activity of three courtship-motivating circuit elements, ensuring the complete suppression of sexual motivation and behavior. Blocking or overriding these inhibitory mechanisms evokes immediate and robust sexual behavior from very young and otherwise asexual males. Similarities to mammalian adolescence suggest a general principle in which hormonal changes gate the transition to sexuality not by constructing new circuitry but by permitting activity in otherwise latent motivational circuit elements (Zhang, 2021).

The identification and evaluation of a potential mating partner require multisensory integration, but, in the experimental paradigm used in this study, the decision to court is ultimately triggered by a male tapping a female with his pheromone receptor-bearing leg. The tap delivers parallel excitatory and inhibitory inputs to the male's P1 courtship command neurons, which initiate and maintain courtship when sufficiently stimulated. The sensitivity of P1 neurons to the inhibitory input from a tap is decreased by a local dopamine signal, which, in mature males, is tuned to reflect recent mating history. If a male has not mated for several days, the dopamine tone is high, increasing the probability that a tap will lead to courtship. Once courtship has commenced, the same dopamine signal maintains courtship bouts for the tens of seconds to several minutes required before mating starts. This motivating dopaminergic activity is maintained for days in the absence of females, is decremented by each mating, and slowly recovers over 3 to 4 days (Zhang, 2021).

Matings promote satiety by activating a set of copulation reporting neurons (CRNs). These neurons, as a population, project dendrites to the external genitalia and send axons to the brain, where they reduce the activity of fruitless-positive neuropeptide F (NPF) producing neurons. This decrease is relayed through the NPF receptor to the motivation-promoting dopamine neurons, reducing their activity. After a few matings, substantial satiety is induced, and reproductive motivation remains low for several days. Mating drive has an intrinsic tendency to recover because of recurrent excitation: NPF neurons excite and are excited by Doublesex-positive pCd neurons, forming a loop that holds and gradually accumulates activity during periods of abstinence. Loop activity is prevented from immediately rebounding by the activity-dependent transcription factor CREB2, which, during the period of high motivation that precedes mating, transcribes inhibitory genes (e.g., potassium channels) that will sustain the decremented activity state, forcing recovery to proceed on a biochemical, rather than electrical, time scale (Zhang, 2021).

This circuit architecture suggests several hypotheses for the prevention of courtship in newly eclosed males. For example, a juvenile male might not recognize or tap females; the CRNs may be constantly active, inducing overwhelming satiety; or, as in the organizational hypothesis, some or all of the courtship circuitry may not be fully developed. This study presents evidence refuting all these hypotheses. Instead, this study found that a spike in juvenile hormone levels at eclosion directly and selectively imposes long-lasting activity suppression on all known motivation-promoting circuit elements: both populations of loop neurons and the downstream dopaminergic neurons. Overriding any of these repressive mechanisms evokes robust courtship from extremely young males, providing clear evidence that much of the reproductive circuitry is developed and functional but lays dormant in juveniles. The multitiered suppression of motivational circuitry appears to be the key difference between the complete inactivation of mating drive in early life and the fluctuations experienced in adulthood (Zhang, 2021).

The story of Eden indicates that the diverse behavioral changes coinciding with reproductive maturity had been fascinating and frustrating humans thousands of years before they were known to involve the brain. The hormonal signaling pathways that trigger these changes in mammals are now understood, but little is known about their mechanistic impact on behavioral and motivational circuitry. Teleological explanations for delays in the onset of sexual behavior likely vary across species, but the pervasiveness of this phenomenon suggests the possibility of a core mechanistic conservation that transcends idiosyncrasies of duration, purpose, and even the molecular nature of the hormonal trigger. The system described in this paper for analyzing the transition to sexuality allows rapid insight, with principles that suggest molecular and circuit hypotheses in other animals and other late-emerging brain functions (Zhang. 2021).

One obvious but likely superficial difference in the control of behavioral maturation between Drosophila and mammals is the sign of the regulation by circulating hormones. Although some gonadal hormones can suppress mating drive, in juvenile male rodents, experimental elevation of testosterone causes precocious mating behavior, whereas this study found that juvenile hormone suppresses mating behavior. This discordance is reconciled early in signal transduction, as loss of the Met receptor for juvenile hormone decreases sexual behavior, similar to the effects of androgen (testosterone) receptor antagonists in humans. The receptors for testosterone, estrogen, and progesterone are all transcription factors, as are the Met and Gce receptors for juvenile hormone. Unlike ablating the source of juvenile hormone or overriding its action at the level of neuronal activity or CREB activation, removing the suppressive receptor, Gce, does not cause substantial juvenile courtship. This points to the likely existence of yet another juvenile hormone receptor, the identification of which will provide a deeper understanding of the suppressive mechanisms used to completely, selectively, and transiently inactivate this fundamental drive (Zhang. 2021).

In mature animals, the recurrent loop that drives the male to court also primes itself for satiety through activity-induced, CREB-mediated production of suppressive TASK7-containing potassium channel complexes. This motivation-suppressing module is also used in juveniles, with hormonal signaling causing CREB2 activation. Although additional suppressive effectors have not yet been identified in juveniles, evidence is seen of fast-acting suppression by juvenile hormone and direct suppression of the dopaminergic neurons, neither of which can be accounted for by CREB activity in the pCd/NPF loop. Parallel fast and slow responses have also been found in the response to mammalian gonadal hormones. Multiple repressive mechanisms are likely necessary to completely inactivate mating drive, since individually removing or overriding individual suppressive effectors allows juvenile courtship. This is again similar to findings in mammals, where administering testosterone to either the preoptic area of the hypothalamus or to the medial amygdala suffices to restore mating behaviors in castrated males (Zhang. 2021).

A second clear difference between Drosophila and vertebrate juvenile stages is the time scale. The fly system requires suppression of sexual behavior for days, not months or years [even 150 years as suggested in Greenland sharks]. However, several nonneuronal structures are still maturing in juvenile flies, such as the abdominal musculature, the cuticle, and the ejaculatory bulb, indicating that the neuronal circuitry that will eventually animate these features may require structural development as well. Although the kind of detailed anatomical analysis required to argue for or against fine-scale structural rearrangements, courtship circuitry appears grossly normal in juveniles, and the stimulation of several circuit elements (P1, dopaminergic, NPF, and pCd) can rapidly drive coherent courtship. While it is not yet known whether the courtship performed by juvenile males is identical to that of mature males (e.g., in song production),the current findings serve as strong evidence against a strict developmental ontogeny for the posteclosion emergence of reproductive motivation and behavior (Zhang. 2021).

A recent report found structural changes in a key hypothalamic population during estrus and showed that these neurons could not drive sexual behaviors when activated in ovariectomized mice. It is noted that it was not possible to restore sexual behavior in male flies if subpopulations of individually necessary dopaminergic neurons are stimulated, a consideration that leaves open the possibility that the motivational and behavioral circuitry may be at least partially functional, but suppressed, in nonestrus female mice. The most parsimonious explanation may be that both structural and activational changes take place between asexual and sexual periods of life. Increased connectivity in motivational circuitry could be a result, rather than a cause, of increased activity, as has been reported in a growing number of systems. No reports were found of neuronal stimulation eliciting juvenile sexual behavior in the mammalian literature, but stimulation of hypothalamic neurons can produce coherent and robust parenting behaviors from otherwise nonparental mice, demonstrating latent but functional circuitry for these late-emerging behaviors (Zhang. 2021).

Recently, new functions for pCd and NPF neurons have been reported in male-specific behaviors. In the Anderson laboratory, pCd neurons were found to sustain sexual and aggressive behaviors after P1 activation. This sustained excitation is remarkably similar to the role of pCd in the accrual and maintenance of sexual motivation in mature male flies. Since P1 activity is constantly required to sustain courtship, it is suggested that subsets of the eight pCd neurons may be specialized for maintaining recurrent activity over minutes (together with P1 neurons in sustaining courtship and aggression) and over days (together with NPF neurons in sustaining reproductive motivation). In work from the Montell laboratory, male-specific NPF neurons were reported to decrease sexual motivation generally and to prevent male-male courtship in particular. These conclusions appear to be in direct contrast with the courtship-promoting roles for these same NPF neurons described in earlier work. The results obtained across these studies are derived from a variety of assays and genetic manipulations, complicating direct comparisons, although they clearly point to multiple roles for NPF in courtship target selection and motivation. While several experiments have argued for various forms of hypersexuality resulting from decreased NPF signaling, the lone NPF neuronal stimulation experiment to yield a reduction in courtship behavior assessed courtship toward decapitated females and the effect was modest. The robust effects seen in this study make the authors confident that increasing the output of NPF neurons promotes a net increase in sexual motivation in otherwise asexual juveniles and in satiated males recovering from ad libitum access to virgin females (Zhang. 2021).

These results provide an alternative explanation for what has been the strongest evidence in favor of the organizational hypothesis for the maturation of reproductive behaviors in mammals: the delay between hormonal changes and the behaviors they induce. In the fly, the long-lasting, CREB-imposed effects of an earlier suppressive hormonal state must decay away to allow loop activity to ramp up and promote sexual behavior. Enduring and distributed suppressive effects may explain why the awakening of sexuality triggered by hormonal changes is gradual and halting in both flies and mammals (Zhang. 2021).

Acoustic communication signals diversify even on short evolutionary time scales. To understand how the auditory system underlying acoustic communication could evolve, this study conducted a systematic comparison of the early stages of the auditory neural circuit involved in song information processing between closely-related fruit-fly species. Male Drosophila melanogaster and D. simulans produce different sound signals during mating rituals, known as courtship songs. Female flies from these species selectively increase their receptivity when they hear songs with conspecific temporal patterns. This study firstly confirmed interspecific differences in temporal pattern preferences; D. simulans preferred pulse songs with longer intervals than D. melanogaster. Primary and secondary song-relay neurons, JO neurons and AMMC-B1 neurons, shared similar morphology and neurotransmitters between species. The temporal pattern preferences of AMMC-B1 neurons were also relatively similar between species, with slight but significant differences in their band-pass properties. Although the shift direction of the response property matched that of the behavior, these differences are not large enough to explain behavioral differences in song preferences. This study enhances understanding of the conservation and diversification of the architecture of the early-stage neural circuit which processes acoustic communication signals (Ohashi, 2023).

Animals perform a series of actions in a fixed order during ritualistic innate behaviors. Although command neurons and sensory pathways responding to external stimuli that trigger these behaviors have been identified, how each action is induced in a fixed order in response to multimodal sensory stimuli remains unclear. In this study, the sexually dimorphic lateral antennal lobe tract projection neuron 4 (lPN4) in male Drosophila melanogaster mediates the expression of a fixed behavioral action pattern at the beginning of the courtship ritual, in which a male taps a female body and then extends a wing unilaterally to produce a courtship song. Blocking the synaptic output of lPN4 caused an increase in the ratio of male flies that extended a wing unilaterally without tapping the female body, whereas excitation of lPN4 suppressed the transition from the tapping phase to the unilateral wing extension phase. Real-time calcium imaging showed that lPN4 is activated by a volatile pheromone, palmitoleic acid, whose responses were inhibited by simultaneous gustatory stimulation with female cuticular hydrocarbons, showing the existence of an "AND-gate" for multimodal sensory inputs during male courtship behaviors. These results suggest that the function of lPN4 is to suppress unilateral wing extension while responding to a female smell, which is released by appropriate contact chemosensory inputs received when tapping a female. As the female smell also promotes male courtship behaviors, the olfactory system is ready to simultaneously promote and suppress the progress of courtship actions while responding to a female smell (Tanaka, 2021).

Choosing a mate is one of the most consequential decisions a female will make during her lifetime. A female fly signals her willingness to mate by opening her vaginal plates, allowing a courting male to copulate. Vaginal plate opening (VPO) occurs in response to the male courtship song and is dependent on the mating status of the female. How these exteroceptive (song) and interoceptive (mating status) inputs are integrated to regulate VPO remains unknown. This study characterize the neural circuitry that implements mating decisions in the brain of female Drosophila melanogaster. VPO was shown to be controlled by a pair of female-specific descending neurons (vpoDNs). The vpoDNs receive excitatory input from auditory neurons (vpoENs), which are tuned to specific features of the D. melanogaster song, and from pC1 neurons, which encode the mating status of the female. The song responses of vpoDNs, but not vpoENs, are attenuated upon mating, accounting for the reduced receptivity of mated females. This modulation is mediated by pC1 neurons. The vpoDNs thus directly integrate the external and internal signals that control the mating decisions of Drosophila females (Wang, 2020).

Drosophila males woo potential mates by vibrating their wings to produce a species-specific courtship song. The male song induces deflections of the female aristae, thereby activating auditory sensory neurons that project to the central brain. Several types of song-responsive neurons have been identified in the female brain, but it is unknown whether and how these neurons regulate sexual receptivity. How a female responds to the song of a male is highly dependent on whether or not she has previously mated. Once mated, females store sperm for days to weeks, and during this time are reluctant to mate again. A male seminal fluid peptide (Sex peptide) binds to sperm and signals the presence of sperm in the female reproductive tract through an ascending pathway from the sex peptide sensory neurons (SPSNs) in the uterus via the sex peptide abdominal ganglion (SAG) neurons in the ventral nerve cord to the pC1 neurons in the brain. Sex peptide attenuates neuronal activity in the SPSN, SAG and pC1 neurons, thereby reducing sexual receptivity after mating. This study sought to investigate how these distinct external and internal signals are integrated in the female brain to control VPO, the motor output that signals the willingness of the female to mate (Wang, 2020).

Female receptivity is impaired by blocking the activity of the approximately 2,000 neurons that express either of the two sex-determination genes, fruitless (fru) or doublesex (dsx). This class of neurons includes the fru+ dsx+ SPSNs, the dsx+ SAGs13 and the dsx+ pC1 cells3. To search for other fru+ or dsx+ neurons that contribute to female receptivity, a collection of 234 sparse driver lines specific for various fru+ or dsx+ cell types were screened. These driver lines were used to genetically silence each of these cell types, and virgin females were assayed for their frequency of copulation within 10 min of being individually paired with naive wild-type males. Of the seven lines with the strongest reduction in receptivity, two labelled the SPSNs, one labelled the SAGs and one labelled the pC1 cells. The other three lines targeted a pair of female-specific descending neurons, which were named vpoDNs. These neurons are dsx+, fru- and cholinergic. Their dendrites arborize primarily in the lateral protocerebrum and their axons project to multiple regions of the ventral nerve cord, including the abdominal ganglion (Wang, 2020).

Acute optogenetic silencing or genetic ablation of the vpoDNs rendered virgin females unreceptive, markedly reducing the frequency of VPO but not the intensity of male courtship. Conversely, photoactivation of vpoDNs reliably triggered VPO in isolated virgin females. No peripheral expression was detected that was driven by the vpoDN lines, and by severing the abdominal nerve, it was confirmed that the VPO response is indeed due to activation of central neurons (Wang, 2020).

Mated females are less receptive than virgins, which was found to correlate with a lack of VPO. To assess whether the failure to perform VPO accounts for the low receptivity of mated females, the vpoDNs were photoactivated in mated females as they were being courted by wild-type males. Whereas control females never copulated during a 1-h assay, approximately 30-50% of the vpoDN-activated females did remate. A similar remating frequency was observed upon vpoDN activation in mated females paired with wingless males, which court but cannot sing. Thus, direct activation of vpoDNs bypasses the need for both the internal state (virginity) and the external cue (song) that normally combine to elicit VPO (Wang, 2020).

Wing extension was found to be the most frequent male action just before female VPO, and both VPO and copulation rates were found to be reduced if males are muted by removing their wings or females deafened by removing their aristae. These results suggested that the vpoDNs might be activated by male song. Indeed, in two-photon calcium-imaging experiments, a robust increase of calcium levels was detected in the neurites of vpoDNs in virgin females upon playback of male courtship song, but not in response to white noise. The response to courtship song was lost when the aristae were immobilized to deafen the female. Song responses have also been reported for the pMN2 neurons, which are morphologically similar to vpoDNs and also dsx+, although their reported functions differ (Wang, 2020).

The vpoDN dendrites lie mostly in the superior lateral protocerebrum, with no obvious arborizations within the antennal mechanosensory centre (AMMC), the primary auditory neuropil, or in the wedge region, a secondary auditory neuropil known to include song-responsive neuron. Therefore attempts were made to trace potential pathways from these auditory centres to the vpoDNs within the electron microscopy volume of a full adult female brain (FAFB). A single vpoDN was detected in each hemisphere and the vpoDN was extensively traced in the right hemisphere as well as its presynaptic partners, identifying a total of 45 neurons with at least 10 synapses impinging onto vpoDN (Wang, 2020).

None of the vpoDN input neurons innervate the AMMC, but at least two cell types have extensive arborizations within the wedge. Multiple split-GAL4 driver lines specific for these two cell types WERE obtained. Fluorescence in situ hybridization predicted, and whole-cell recording confirmed, that one of these cell types is excitatory and the other is inhibitory. Accordingly, these two cell types were named the vpoENs and vpoINs, respectively. Within FAFB there are two vpoEN cells and 14 vpoIN cells in each hemisphere (Wang, 2020).

Optogenetic silencing and activation experiments were performed to examine the roles of vpoENs and vpoINs in VPO and receptivity. Acute inhibition of the vpoENs significantly reduced the frequency of copulation and VPO when virgin females were paired with males. Conversely, strong optogenetic activation of vpoENs elicited VPO in isolated females, mimicking activation of vpoDNs. In virgin females paired with males, activating vpoINs suppressed mating and VPO, whereas silencing vpoINs had no effect. Thus, vpoENs and vpoINs promote and suppress, respectively, both VPO and receptivity (Wang, 2020).

Using two-photon calcium imaging, it was found that both vpoENs and vpoINs, as with vpoDNs, responded to playback of male courtship songs. The courtship song varies considerably between different Drosophila species and is the primary cue the female uses for species recognition. To test whether the vpoDNs, vpoENs and vpoINs are specifically tuned to the D. melanogaster courtship song, natural courtship songs were presented from seven other Drosophila species, selecting two representative audio clips from each species. The vpoDNs showed little or no response to any of these songs, the vpoENs responded to one or two clips from five species, and the vpoINs responded to all but one clip from one species (Wang, 2020).

Artificial pulse songs were generated for each of the other species, again systematically altering the IPI from 10 ms to 300 ms. Notably, the vpoDNs responded to the pulse songs of five other species once their IPI was shifted to match that of the D. melanogaster song. Together, these data establish that the vpoDNs are finely tuned to the D. melanogaster pulse song, owing to their selectivity for an IPI of about 35 ms. This narrow tuning may arise through a combination of strong excitation from highly selective vpoENs and weak inhibition from broadly responsive vpoINs (Wang, 2020).

Having determined how auditory input controls VPO and sexual receptivity, how this response is modulated by the mating status of the female was examined. VPO may be attenuated after mating either because the vpoENs and vpoDNs are less potent at eliciting VPO, or because they are less excited by song. In optogenetic activation and calcium-imaging experiments, it was found that vpoDNs are equally potent in mated and virgin females, whereas both the basal calcium levels and the response to courtship song were lower in mated females than in virgins. By contrast, the vpoENs were significantly less potent at eliciting VPO in mated than in virgin females. Although basal fluorescence of vpoENs was slightly higher in mated females than in virgins, their song responses were indistinguishable. Calcium levels were imaged in vpoINs, and it was found that the basal fluorescence and song responses of these cells were similar in mated and virgin females. Thus, these data show that vpoENs have a similar response to song in mated females as they do in virgins, but they are less able to excite vpoDNs in mated females (Wang, 2020).

It is concluded that the decision of the female fly to mate or not to mate is largely determined by how the vpoDNs integrate signals from two direct synaptic inputs: the vpoENs, which are selectively tuned to the conspecific male courtship song, and the pC1 cells, which encode the mating status of the female. When the male sings, female vpoENs are activated; whether or not this leads to vpoDN activation and hence VPO depends on the level of pC1 activity, which is higher in virgins than in mated females. The neural computation that underlies this state-dependent sensorimotor transformation remains to be determined; this will require methods for simultaneously manipulating and recording from all three cell types. One possibility is that the pC1 inputs gate the vpoEN inputs in a nonlinear fashion. No obvious spatial segregation of vpoEN and pC1 synapses onto the vpoDN dendrites was noted, as might be expected if these inputs are indeed processed hierarchically. Alternatively, vpoDNs might simply use a sum-to-threshold mechanism, in which the combined input from vpoENs and pC1s must exceed a certain level to elicit action potentials in vpoDNs. In this scenario, the lower pC1 activity after mating would necessitate a stronger vpoEN input to activate the vpoDNs. This model may account for the observation that wild-caught females are often multiply mated, consistent with the prediction from evolutionary theory that a mated female would increase her reproductive fitness by remating when she is courted by a male of higher quality than her first partner (Wang, 2020).

The many other, as yet uncharacterized, inputs to pC1, vpoEN and vpoDN cells may convey additional signals that modulate female receptivity. For example, pC1 cells are reported to respond to a male pheromone, which may serve to enhance the receptivity of both virgin and mated females. The persistent enhancement of vpoDN song responses upon transient activation of pC1 cells resembles the persistent state of courtship arousal induced in males by transient activation of the male pC1 counterparts. The female pC1 cells may therefore encode both mating status and, as with their male counterparts, a lasting state of mating arousal induced by sensory cues from potential mates. The ensuing interaction between the two sexes involves a coordinated sequence of signals and responses, as exemplified by the male singing to elicit female VPO. In both sexes, these sensorimotor transformations may not be directly mediated by pC1 cells, as commonly thought, but rather modulated by the arousal states they encode. The neural architecture reported in this study for the control of Drosophila female sexual receptivity may thus also serve as a paradigm for understanding male sexual behaviour, and perhaps more generally for the state-dependent signal processing that underlies behavioural decisions across a range of species (Wang, 2020).

Endocrine state is an important determinant of learning and memory in animals. In Drosophila, rejection of male courtship overtures by mated females leads to an aversive response manifested as courtship memory. This study reports that ecdysis triggering hormone (ETH) is an obligatory enabler of long-term courtship memory (LTM). ETH deficiency suppresses LTM, whereas augmented ETH release reduces the minimum training period required for LTM induction. ETH receptor knockdown either in the mushroom body (MB) γ lobe or in octopaminergic dorsal-anterior-lateral (DAL) neurons impairs memory performance, indicating its direct action in these brain areas. Consistent with these findings, brain exposure to ETH mobilizes calcium in MB γ lobe neuropils and DAL neurons. ETH receptor (ETHR) knockdown in the corpus allatum (CA) to create juvenile hormone (JH) deficiency also suppresses LTM, as does knockdown of the JH receptor Met in the MB γ lobe, indicating a convergence of ETH and JH signaling in this region of the brain. These findings identify endocrine-enabled neural circuit components in the brain that are critical for persistent behavioral changes resulting from aversive social experience (Lee, 2021).

Innate behaviors involve both reflexive motor programs and enduring internal states, but how these responses are coordinated by the brain is not clear. In Drosophila, male-specific P1 interneurons promote courtship song, as well as a persistent internal state that prolongs courtship and enhances aggressiveness. However, P1 neurons themselves are not persistently active. This study identified pCd neurons as persistently active, indirect P1 targets that are required for P1-evoked persistent courtship and aggression. Acute activation of pCd neurons alone is inefficacious but enhances and prolongs courtship or aggression promoted by female cues. Brief female exposure induces a persistent increase in male aggressiveness, an effect abrogated by interruption of pCd activity. pCd activity is not sufficient but necessary for persistent physiological activity, implying an essential role in a persistence network. Thus, P1 neurons coordinate both command-like control of courtship song and a persistent internal state of social arousal mediated by pCd neurons (Jung, 2020).

Neuromodulators such as monoamines are often expressed in neurons that also release at least one fast-acting neurotransmitter. The release of a combination of transmitters provides both "classical" and "modulatory" signals that could produce diverse and/or complementary effects in associated circuits. This study establish that the majority of Drosophila octopamine (OA) neurons are also glutamatergic and identify the individual contributions of each neurotransmitter on sex-specific behaviors. Males without OA display low levels of aggression and high levels of inter-male courtship. Males deficient for dVGLUT solely in OA-glutamate neurons (OGNs) also exhibit a reduction in aggression, but without a concurrent increase in inter-male courtship. Within OGNs, a portion of VMAT and dVGLUT puncta differ in localization suggesting spatial differences in OA signaling. These findings establish a previously undetermined role for dVGLUT in OA neurons and suggests that glutamate uncouples aggression from OA-dependent courtship-related behavior. These results indicate that dual neurotransmission can increase the efficacy of individual neurotransmitters while maintaining unique functions within a multi-functional social behavior neuronal network (Sherer, 2020).

Addressing the functional complexities of 'one neuron, multiple transmitters' is critical to understanding how neuron communication, circuit computation, and behavior can be regulated by a single neuron. Over many decades, significant progress has been made elucidating the functional properties of neurons co-expressing neuropeptides and small molecule neurotransmitters, where the neuropeptide acts as a co-transmitter and modulates the action of the neurotransmitter. Only recently have studies begun to examine the functional significance of co-transmission by a fast-acting neurotransmitter and a slow-acting monoamine (Sherer, 2020).

This study has demonstrated that OA neurons express dVGLUT and has utilized a new genetic tool to remove dVGLUT in OA-glutamate neurons. Quantifying changes in the complex social behaviors of aggression and courtship revealed that dVGLUT in brain OGNs is required to promote aggressive behavior and a specific behavioral pattern, the lunge. In contrast, males deficient for dVGLUT function do not exhibit an increase in inter-male courtship. These results establish a previously undetermined role for dVGLUT in OA neurons located in the adult brain and reveal glutamate uncouples aggression from inter-male courtship. It has been suggested that classical neurotransmitters and monoamines present in the same neuron modulate each other's packaging into synaptic vesicles or after release via autoreceptors. For example, a reduction of dVGLUT in DA-glutamate neurons resulted in decreased AMPH-stimulated hyperlocomotion in Drosophila and mice suggesting a key function of dVGLUT is the mediation of vesicular DA content. In this study, the independent behavioral changes suggests enhancing the packaging of OA into vesicles is not the sole function of dVGLUT co-expression and suggests differences in signaling by OA from OGNs on courtship-related circuitry (Sherer, 2020).

Co-transmission can generate distinct circuit-level effects via multiple mechanisms. One mechanism includes spatial segregation; the release of two neurotransmitters or a neurotransmitter and monoamine from a single neuron occurring at different axon terminals or presynaptic zones. Recent studies examining this possible mechanism have described; (i) the release of GLU and DA from different synaptic vesicles in midbrain dopamine neurons and (ii) the presence of VMAT and VGLUT microdomains in a subset of rodent mesoaccumbens DA neurons. This study expressed a new conditionally expressed epitope-tagged version of VMAT in OGNs and visualized endogenous dVGLUT via antibody labeling. Within OGNs, the colocalization of VMAT and dVGLUT puncta was not complete suggesting the observed behavioral phenotype differences may be due to spatial differences in OA signaling (Sherer, 2020).

A second mechanism by which co-transmission may generate unique functional properties relies on activating distinct postsynaptic receptors. In Drosophila, recent work has identified a small population of male-specific neurons that express the alpha-like adrenergic receptor, OAMB, as aggression-promoting circuit-level neuronal targets of OA modulation independent of any effect on arousal and separately knockdown of the Rdl GABAa receptor in a specific doublesex+ population stimulated male aggression. Future experiments identifying downstream targets that express both glutamate and octopamine receptors would be informative, as well as using additional split-Gal4 lines to determine if segregation of transporters is a hallmark of the majority of OGNs. Finally, a third possible mechanism is Glu may be co-released from OGNs and act on autoreceptors to regulate presynaptic OA release (Sherer, 2020).

Deciphering the signaling complexity that allows neural networks to integrate external stimuli with internal states to generate context-appropriate social behavior is a challenging endeavor. Neuromodulators including monoamines are released to signal changes in an animal's environment and positively or negatively reinforce network output. In invertebrates, a role for OA in responding to external chemosensory cues as well as promoting aggression has been well-established. In terms of identifying specific aggression circuit-components that utilize OA, previous results determined OA neurons directly receive male-specific pheromone information and the aSP2 neurons serve as a hub through which OA can bias output from a multi-functional social behavior network towards aggression. The ability of OA to bias behavioral decisions based on positive and negative reinforcement was also recently described for food odors. In vertebrates, it has been proposed that DA-GLU cotransmission in the NAc medial shell might facilitate behavioral switching. The finding that the majority of OA neurons are glutamatergic, suggests that the complex social behavior of aggression may rely on small subsets of neurons that both signal the rapid temporal coding of critical external stimuli as well as the frequency coding of such stimuli resulting in the enhancement of this behavioral network. One implication of the finding regarding the separable OA-dependent inhibition of inter-male courtship is the possibility of identifying specific synapses or axon terminals that when activated gate two different behavioral outcomes. A second implication is that aggressive behavior in other systems may be modified by targeting GLU function in monoamine neurons (Sherer, 2020).

Finally, monoamine-expressing neurons play key roles in human behavior including aggression and illnesses that have an aggressive component such as depression, addiction, anxiety, and Alzheimer's. While progress is being made in addressing the functional complexities of dual transmission, the possible pathological implications of glutamate co-release by monoamine neurons remains virtually unknown. Analyzing the synaptic vesicle and release properties of monoamine-glutamate neurons could offer new possibilities for therapeutic interventions aimed at controlling out-of-context aggression (Sherer, 2020).

Diffuse neuromodulatory systems such as norepinephrine (NE) control brain-wide states such as arousal, but whether they control complex social behaviors more specifically is not clear. Octopamine (OA), the insect homolog of NE, is known to promote both arousal and aggression. A systematic, unbiased screen identified OA receptor-expressing neurons (OARNs) that control aggression in Drosophila. The results uncover a tiny population of male-specific aSP2 neurons that mediate a specific influence of OA on aggression, independent of any effect on arousal. Unexpectedly, these neurons receive convergent input from OA neurons and P1 neurons, a population of FruM(+) neurons that promotes male courtship behavior. Behavioral epistasis experiments suggest that aSP2 neurons may constitute an integration node at which OAergic neuromodulation can bias the output of P1 neurons to favor aggression over inter-male courtship. These results have potential implications for thinking about the role of related neuromodulatory systems in mammals (Watanabe, 2017).

A rich behavioral literature has implicated OA in the control of invertebrate aggression, although the direction of its effects differs between species. Classic studies in lobsters have shown that injection of OA into the hemolymph promotes a subordinate-like posture, while injection of serotonin (5HT) produces a dominant-like posture. In contrast, hemolymph injections of OA in crickets restore aggressiveness to subordinated animals, mimicking the arousing effects of episodes of free flight. OA has also been suggested to play a role in aggressive motivation restored to defeated crickets by residency in a shelter. In Drosophila, null mutations of TβH strongly suppressed aggressiveness, suggesting a positive-acting role for OA in flies as in crickets. Interestingly, intra-hypothalamic infusion of NE in mammals can also enhance aggression. However, little is known about the neurons on which these amines act directly to influence aggression, in any organism (Watanabe, 2017).

This study applied a novel, unbiased approach to identify OARNs relevant to aggression in Drosophila. Importantly this screen was based not on mutations in OAR genes, but rather upon genetic silencing of neurons that express GAL4 under the control of different OAR gene cis-regulatory modules (CRMs). This screen was agnostic with respect to which OAR gene is involved, or in which neurons that OAR is expressed. It yielded a small population of male-specific, FruM+ OA-sensitive neurons, called aSP2, the activity of which isrequired for normal levels of aggressiveness. No significant change in UWEs (male-male courtship) was observed when these neurons were activated or silenced. Nevertheless, neuronal silencing in the parental R47A04-GAL4 line increased male-male courtship, perhaps reflecting an inhibitory role for non-aSP2 neurons in that line. Therefore, while it is not possible to completely exclude a role for aSP2 neurons to suppress male-male courtship, the evidence does not strongly support it (Watanabe, 2017).

Multiple lines of evidence suggest that R47A04^aSP2 neurons are indeed OA responsive, likely via OAMB. First, these neurons are labeled by a CRM from the Oamb gene. Second, RNAi-mediated knockdown of Oamb in R47A04 neurons reduced aggression, phenocopying the effects of an Oamb null allele. (However, knockdown using the split-GAL4 R47A04^aSP2driver only yielded a trend to reduced aggression that did not reach significance, perhaps reflecting a floor effect in this assay.) Third, overexpression of Oamb cDNAs in these neurons using R47A04-GAL4 rescued the Oamb null mutant and enhanced the effect of OA feeding to promote aggression. Fourth,R47A04^aSP2 neurons were activated by bath-applied OA in brain explants, and this effect was also blocked by RNAi-mediated knockdown of Oamb. Taken together, these data strongly suggest that aSP2 neurons respond directly to OA to mediate its effects on aggression, although they do not exclude a role for other OA-responsive non-aSP2 neurons in line R47A04. While it was not possible to definitively establish which of the 27 different classes of OANs in Drosophila provide functional OA input to aSP2 cells, some candidate OA neurons labeled in retrograde PA-GFP experiments (VUM and VPM) have previously been implicated in aggression (Watanabe, 2017).

In Drosophila OA, like NE in vertebrates, is thought to promote arousal. Consistent with such a function, OAergic fibers are broadly distributed across the entire Drosophila CNS, as are NE fibers in vertebrates. Thus OARNs could enhance aggression by increasing arousal, and there is evidence for such a function in crickets. However, manipulations of R47A04^aSP2neurons that increased or decreased aggression did not affect locomotion, circadian activity, or sleep. This suggests that these neurons influence aggression directly and specifically, rather than by increasing generalized arousal. Other classes of OARNs not investigated in this study have been implicated in sleep-wake arousal (Watanabe, 2017).

Does OA promote aggression in a permissive or instructive manner? While it is clear that OA synthesis and release are required for aggression in Drosophila, whether increasing OA suffices to promote aggression is less clear. It was reported that NaChBac-mediated activation of Tdc2-GAL4 neurons enhanced aggression, but the current study neither this manipulation, nor activation of Tdc2 neurons using dTrpA1 or Chrimson, yielded consistent effects. Thus, while OA is essential for normal levels of aggression, it is not clear whether it plays an instructive role to promote this behavior (Watanabe, 2017).

In principle, OA RNs could act directly in command-like neurons that mediate aggression, or rather in cells that play a modulatory role. It was found that aggression was increased by tonically enhancing the excitability of R47A04^aSP2 neurons using NaChBac, but not by phasically activating them optogenetically, arguing against a command-like function. Furthermore, the influence of TK FruM neurons, which do promote aggression when phasically activated, was not dependent on the activity of R47A04^aSP2 neurons, indicating that the latter are not functionally downstream of the former. Together, these data argue against a role for R47A04^aSP2 cells as command-like neurons, or as direct outputs of command neurons, for aggression. Rather, these cells exert a modulatory influence on agonistic behavior (Watanabe, 2017).

In searching for neurons that may interact with R47A04^aSP2 cells in their modulatory capacity, P1 neurons, a FruM+ population of 20 neurons/hemibrain was identified that controls male courtship, but which can also promote aggression when activated. It has been argued that this aggression-promoting effect is due to a subset of FruM neurons in the GAL4 line used in these studies, R71G01-GAL4. However, this study shows that conditional expression of FLP-ON Chrimson in a subset of neurons within the R71G01-GAL4 population using Fru-FLP. Nevertheless, these data do not exclude that the aggression-promoting neurons in the P1 cluster expressed Fru-FLP only transiently during development, nor do they exclude the possibility that different subpopulations of neurons within line R71G01 control courtship versus aggression; further studies will be required to resolve these issues (Watanabe, 2017).

The P1 cluster is known to project to downstream cells that are specific for courtship . The present study provides the first evidence that cells in this cluster also functionally activate (and physically contact) aggression-specific neurons. However, due to limitations of the genetic reagents employed, it is not certain that the behavioral, physiological, and anatomical interactions with aSP2 cells demonstrated in this study are all mediated by the same subset of neurons in the P1 cluster. With this caveat in mind, these data suggest that aSP2 neurons are functionally downstream of both a subset(s) of P1 neurons, as well as of OA neurons (Watanabe, 2017).

Feeding flies OA potentiated the activation of R47A04^aSP2 neurons by P1 neuron stimulation, in brain explants. Furthermore, activation of aggression by P1 stimulation was enhanced and suppressed by pharmacologically increasing or decreasing OA signaling, respectively. While some off-target effects of the drugs, or an action on non-aSP2 neurons expressing OARs, cannot be excluded these pharmacologic effects were overridden by opposite-direction genetic manipulations of R47A04^aSP2neuronal activity. Whether P1 neurons and OANs normally activate aSP2 neurons in vivo, simultaneously or sequentially, is not yet clear. Nevertheless it is striking that P1 and Tdc2 putative inputs occupy adjacent regions of aSP2 dendrites. Taken together, these findings suggest that aSP2 cells may serve as a node through which OA can bias output from a multifunctional social behavior network involving P1 neurons, in a manner that favors aggression. However, aSP2 neurons themselves do not appearto control directly the choice between mating and fighting (Watanabe, 2017).

Male-specific cuticular hydrocarbons such as 7-tricosene (7-T) are known to be required for aggression. Interestingly, it has recently been shown that gustatory neurons expressing Gr32a, which encodes a putative 7-T receptor, innervate OANs in the SEZ; these OANs are activated by 7-T in a Gr32a-dependent manner. SEZ-innervating OANs include the VPM/VUM subsets seen in PA-GFP retrograde labeling experiments. These data raise the possibility that R47A04^aSP2 neurons might be targets of VPM/VUM OANs activated by 7-T. If so, then they could provide a potential link between the influence of male-specific pheromones, OA, and central aggression circuitry. Studies of NE neurons in vertebrates have led to a prevailing view that this neuromodulator is released in a diffuse, 'sprinkler system'-like manner to control brain-wide states like arousal. Recent studies in Drosophila indicate that the broad, brain-wide distribution of OAergic fibers reflects the superposition of close to 30 anatomically distinct subclasses of OANs). The data presented in this study reveal a high level of circuit specificity for OARNs that mediate the effects of OA on aggression, mirroring the anatomical and functional specificity of OANs reported to control this behavior. If this anatomical logic is conserved, then such circuit specificity may underlie the actions of NE in mammals to a greater extent than is generally assumed (Watanabe, 2017).

How brains are hardwired to produce aggressive behavior, and how aggression circuits are related to those that mediate courtship, is not well understood. This large-scale screen for aggression-promoting neurons in Drosophila identifies several independent hits that enhance both inter-male aggression and courtship. Genetic intersections reveal that P1 interneurons, previously thought to exclusively control male courtship, are responsible for both phenotypes. The aggression phenotype is fly-intrinsic, and requires male-specific chemosensory cues on the opponent. Optogenetic experiments indicate that P1 activation promotes aggression vs. wing extension at low vs. high thresholds, respectively. High frequency photostimulation promotes wing extension and aggression in an inverse manner, during light ON and OFF, respectively. P1 activation enhances aggression by promoting a persistent internal state, which could endure for minutes prior to social contact. Thus P1 neurons promote an internal state that facilitates both aggression and courtship, and can control these social behaviors in a threshold-dependent manner (Hoopfer, 2015).

This study describes the first large-scale neuronal activation screen for aggression neurons in Drosophila. Using the thermosensitive ion channel dTrpA1, a collection of over 3,000 GAL4 lines was screened for flies that exhibited increased fighting following thermogenetic neuronal activation. Among ~20 hits obtained, three exhibited both increased aggression and male-male courtship behavior. Intersectional refinement of expression patterns using split-GAL4 indicated that both social behaviors are controlled, in all three hits, by a subpopulation of ~8-10 P1 neurons per hemibrain. P1 cells are male-specific, Fru^M+ interneurons that integrate pheromonal and visual cues to promote male courtship behavior. The results indicate, surprisingly, that at least a subset of P1 neurons, previously thought to control exclusively courtship, can promote male aggression as well. Moreover, it was shown that they exert this influence by inducing a persistent fly-intrinsic state, lasting for minutes, that enhances these behaviors. These data define a sexually dimorphic neural circuit node that may link internal states to the control of mating and fighting, and identify a potentially conserved circuit 'motif' for the control of social behaviors (Hoopfer, 2015).

In insects and mammals, olfactory experience in early life alters olfactory behavior and function in later life. In the vinegar fly Drosophila, flies chronically exposed to a high concentration of a monomolecular odor exhibit reduced behavioral aversion to the familiar odor when it is reencountered. This change in olfactory behavior has been attributed to selective decreases in the sensitivity of second-order olfactory projection neurons (PNs) in the antennal lobe that respond to the overrepresented odor. However, since odorant compounds do not occur at similarly high concentrations in natural sources, the role of odor experience-dependent plasticity in natural environments is unclear. This study investigated olfactory plasticity in the antennal lobe of flies chronically exposed to odors at concentrations that are typically encountered in natural odor sources. These stimuli were chosen to each strongly and selectively excite a single class of primary olfactory receptor neuron (ORN), thus facilitating a rigorous assessment of the selectivity of olfactory plasticity for PNs directly excited by overrepresented stimuli. Unexpectedly, it was found that chronic exposure to three such odors did not result in decreased PN sensitivity but rather mildly increased responses to weak stimuli in most PN types. Odor-evoked PN activity in response to stronger stimuli was mostly unaffected by odor experience. When present, plasticity was observed broadly in multiple PN types and thus was not selective for PNs receiving direct input from the chronically active ORNs. The DL5 olfactory coding channel was further investigated and it was found that chronic odor-mediated excitation of its input ORNs did not affect PN intrinsic properties, local inhibitory innervation, ORN responses or ORN-PN synaptic strength; however, broad-acting lateral excitation evoked by some odors was increased. These results show that PN odor coding is only mildly affected by strong persistent activation of a single olfactory input, highlighting the stability of early stages of insect olfactory processing to significant perturbations in the sensory environment (Gugel, 2023).

In Drosophila, a dedicated olfactory channel senses a male pheromone, cis-vaccenyl acetate (cVA), promoting female courtship while repelling males. This study shows that separate cVA-processing streams extract qualitative and positional information. cVA sensory neurons respond to concentration differences in a 5-mm range around a male. Second-order projection neurons encode the angular position of a male by detecting inter-antennal differences in cVA concentration, which are amplified through contralateral inhibition. At the third circuit layer, 47 cell types were identified with diverse input-output connectivity. One population responds tonically to male flies, a second is tuned to olfactory looming, while a third integrates cVA and taste to coincidentally promote female mating. The separation of olfactory features resembles the mammalian what and where visual streams; together with multisensory integration, this enables behavioral responses appropriate to specific ethological contexts (Taisz, 2023).

Johnston's organ (Jo) acts as an antennal wind-sensitive and/or auditory organ across a spectrum of insect species and its axons universally project to the brain. In the locust, this pathway is already present at mid-embryogenesis but the process of fasciculation involved in its construction has not been investigated. Terminal projections into the fine neuropilar organization of the brain also remain unresolved, information essential not only for understanding the neural circuitry mediating Jo-mediated behavior but also for providing comparative data offering insights into its evolution. In this study, neuron-specific, axon-specific, and epithelial domain labels were employed to show that the pathway to the brain of the locust is built in a stepwise manner during early embryogenesis as processes from Jo cell clusters in the pedicel fasciculate first with one another, and then with the two tracts constituting the pioneer axon scaffold of the antenna. A comparison of fasciculation patterns confirms that projections from cell clusters of Jo stereotypically associate with only one axon tract according to their location in the pedicellar epithelium, consistent with a topographic plan. At the molecular level, all neuronal elements of the Jo pathway to the brain express the lipocalin Lazarillo, a cell surface epitope that regulates axogenesis in the primary axon scaffold itself, and putatively during fasciculation of the Jo projections to the brain. Central projections from Jo first contact the primary axon scaffold of the deutocerebral brain at mid-embryogenesis, and in the adult traverse mechanosensory/motor neuropils similar to those in Drosophila. These axons then terminate among protocerebral commissures containing premotor interneurons known to regulate flight behavior (Boyan, 2023).

Animals communicate using sounds in a wide range of contexts, and auditory systems must encode behaviorally relevant acoustic features to drive appropriate reactions. How feature detection emerges along auditory pathways has been difficult to solve due to challenges in mapping the underlying circuits and characterizing responses to behaviorally relevant features. This paper describes auditory activity in the Drosophila melanogaster brain and investigated feature selectivity for the two main modes of fly courtship song, sinusoids and pulse trains. Twenty-four new cell types of the intermediate layers of the auditory pathway were identified, and using a new connectomic resource, FlyWire, all synaptic connections between these cell types, in addition to connections to known early and higher-order auditory neurons were mapped-this represents the first circuit-level map of the auditory pathway. The sign (excitatory or inhibitory) was determined of most synapses in this auditory connectome. Auditory neurons were found to display a continuum of preferences for courtship song modes and that neurons with different song-mode preferences and response timescales are highly interconnected in a network that lacks hierarchical structure. Nonetheless, this study found that the response properties of individual cell types within the connectome are predictable from their inputs. This study thus provides new insights into the organization of auditory coding within the Drosophila brain (Baker, 2022).

Sensory pathways are typically studied by starting at receptor neurons and following postsynaptic neurons into the brain. However, this leads to a bias in analyses of activity toward the earliest layers of processing. This paper presents new methods for volumetric neural imaging with precise across-brain registration to characterize auditory activity throughout the entire central brain of Drosophila and make comparisons across trials, individuals and sexes. It was discovered that auditory activity is present in most central brain regions and in neurons responsive to other modalities. Auditory responses are temporally diverse, but the majority of activity is tuned to courtship song features. Auditory responses are stereotyped across trials and animals in early mechanosensory regions, becoming more variable at higher layers of the putative pathway, and this variability is largely independent of ongoing movements. This study highlights the power of using an unbiased, brain-wide approach for mapping the functional organization of sensory activity (Pacheco, 2021).

Flies detect sound using a feathery appendage of the antenna, the arista, which vibrates in response to near-field sounds. Antennal displacements activate mechanosensory Johnston's organ neurons (JONs) housed within the antenna. Three major populations of JONs (A, B and D) respond to vibratory stimuli at frequencies found in natural courtship song. These neurons project to distinct areas of the antennal mechanosensory and motor center (AMMC) in the central brain. Recent studies suggest that the auditory pathway continues from the AMMC to the wedge (WED), then to the ventrolateral protocerebrum (VLP) and to the lateral protocerebral complex (LPC). However, knowledge of the fly auditory pathway remains incomplete, and the functional organization of regions downstream of the AMMC and WED are largely unexplored. Moreover, nearly all studies of auditory coding in Drosophila have been performed using female brains, even though both males and females process courtship song information (Pacheco, 2021).

To address these issues, methods were developed to investigate the representation of behaviorally relevant auditory signals throughout the central brain of Drosophila and to make comparisons across animals. Two-photon microscopy was used to sequentially target the entirety of the Drosophila central brain in vivo, combined with fully automated segmentation of regions of interest (ROIs). In contrast to recent brain-wide imaging studies of Drosophila, temporal speed for was traded off for enhanced spatial resolution. Imaging at high spatial resolution facilitates automated ROI segmentation, with each ROI covering subneuropil structures, including cell bodies and neurites. ROIs were accurately registered into an in vivo template brain to compare activity across trials, individuals and sexes, and to build comprehensive maps of auditory activity throughout the central brain. The results reveal that the representation of auditory signals is broadly distributed throughout 33 out of 36 major brain regions, including in regions known to process other sensory modalities, such as all levels of the olfactory pathway, or to drive various motor behaviors, such as the central complex. The representation of auditory stimuli is diverse across brain regions, but focused on conspecific features of courtship song. Auditory activity is more stereotyped (across trials and individuals) at early stages of the putative mechanosensory pathway, becoming more variable and more selective for particular aspects of the courtship song at higher stages. This variability cannot be explained by simultaneous measurements of ongoing fly behavior. Meanwhile, auditory maps are largely similar between male and female brains, despite extensive sexual dimorphisms in neuronal number and morphology. These findings provide the first brain-wide description of sensory processing and feature tuning in Drosophila (Pacheco, 2021).

Sensory systems are typically studied starting from the periphery and continuing to downstream partners guided by anatomy. This has limited the understanding of sensory processing to early stages of a given sensory pathway. This study used a brain-wide imaging method to unbiasedly screen for auditory responses beyond the periphery and, via precise registration of recorded activity, to compare auditory representations across brain regions, individuals and sexes. Auditory activity was found to be widespread, extending well beyond the canonical mechanosensory pathway, and is present in brain regions and tracts known to process other sensory modalities (that is, olfaction and vision) or to drive motor behaviors. The representation of auditory stimuli diversified, in terms of both temporal responses to stimuli and tuning for stimulus features, from the AMMC to later stages of the putative pathway, becoming more selective for particular aspects of courtship song (that is, sine or pulse song, and their characteristic spectrotemporal patterns). Auditory representations were more stereotypic across trials and individuals in early stages of mechanosensory processing, and more variable at later stages. By recording neural activity in behaving flies, this study found that fly movements accounted for only a small fraction of the variance in neural activity, which suggests that across-trial auditory response variability stems from other sources. These results have important implications for how the brain processes auditory information to extract salient features and guide behavior (Pacheco, 2021).

Understanding of the Drosophila auditory circuit thus far has been built up from targeted studies of neural cell types that innervate particular brain regions close to the auditory periphery. Altogether, these studies have delineated a pathway that starts in the Johnston's organ and extends from the AMMC to the WED, the VLP and the LPC. By imaging pan-neuronally, widespread auditory responses that spanned brain regions beyond the canonical pathway, which suggests that auditory processing was found to be more distributed. However, for neuropil signals, it was challenging to determine the number of neurons that contribute to the described ROI responses. Although the diverse set of temporal and tuning types sampled per neuropil suggests that many neurons per neuropil, restricting GCaMP to spatially restricted genetic enhancer lines will assist with linking broad functional maps with the cell types constituting them (Pacheco, 2021).

Findings of widespread auditory activity are likely not unique to audition. So far, in adult Drosophila, only taste processing has been broadly surveyed. While that study did not map activity onto neuropils and tracts, nor did it make comparisons across individuals, it suggested that taste processing was distributed throughout the brain. Similarly, in vertebrates, widespread responses to visual and nociceptive stimuli have been observed throughout the brain. The findings are consistent with anatomical studies that found connections from the AMMC and the WED to several other brain regions, and with the analysis of the hemibrain connectome. While it is not yet known what role this widespread auditory activity plays in behavior, this study shows that ROIs that respond to auditory stimuli do so with mostly excitatory (depolarizing) responses and that activity throughout the brain is predominantly tuned to features of the courtship song. During courtship, flies evaluate multiple sensory cues (olfactory, auditory, gustatory and visual) to inform mating decisions and to modulate their mating drive. Although integration of multiple sensory modalities has been described in higher-order brain regions, the results suggest that song representations are integrated with olfactory and visual information at earlier stages. In addition, song information may modulate the processing of non-courtship stimuli. Song representations in the MB may be useful for learning associations between song and general olfactory, gustatory or visual cues, while diverse auditory activity throughout all regions of the LH may indicate an interaction between song processing and innate olfactory behaviors. Finally, auditory activity was found in brain regions involved in locomotion and navigation (the central and lateral complex, and the superior and ventromedial neuropils). Auditory activity in these regions is diverse, which suggests that pre-motor circuits receive information about courtship song patterns and could therefore underlie stimulus-specific locomotor responses (Pacheco, 2021).

D. melanogaster songs are composed of pulses and sines that differ in their spectral and temporal properties; however, it is unclear how and where selectivity for the different song modes arises in the brain. Since neurons in the LPC are tuned for pulse song across all time scales that define that mode of song, neurons upstream must carry the relevant information to generate such tuning. This study found many ROIs that are selective for either sine or pulse stimuli throughout the entire central brain. A more detailed systematic examination of tuning in the neuropils that carry the most auditory activity (the AMMC, the SAD, the WED, the AVLP and PVLP, the PLP and the LH) revealed that most ROIs are tuned to either pulse or sine features, with few ROIs possessing intermediate tuning. This suggests that sine and pulse information splits early in the pathway. It was also found that sine-selective responses dominate throughout the brain. Although some of this selectivity may simply reflect preference for continuous versus pulsatile stimuli, this investigation of feature tuning revealed that many of these ROIs preferred frequencies that are specifically present in courtship songs. Previous studies indicated that pulse song is more important for mating decisions, with sine song purported to play a role in only priming females. However the results, in combination with the fact that males spend a greater proportion of time in courtship singing sine versus pulse song, suggest a need for reevaluation of the importance and role of sine song in mating decisions. This study therefore lays the foundation for exploring how song selectivity arises in the brain (Pacheco, 2021).

The results also revealed that early mechanosensory brain areas contain ROIs with less variable auditory activity across trials and animals. The results for across-animal variability have parallels to the Drosophila olfactory pathway, whereby third-order MB neurons are not stereotyped, while presynaptic neurons in the AL, the PNs, are. Similarly, a lack of stereotypy beyond early mechanosensory brain areas may reflect stochasticity in synaptic wiring. The amount of variation observed in some brain areas was large, and follow-up experiments with sparser driver lines will be needed to validate whether what is reported here applies to variation across individual identifiable neurons (Pacheco, 2021).

A wide range of across-trial variability was observed throughout neuropils with auditory activity. Imaging from a subset of brain regions in behaving flies revealed that trial-to-trial variance in auditory responses is not explained by spontaneous movements, which suggests that variance is driven by internal dynamics. This result differs from recent findings in the mouse brain, which showed that a large fraction of activity in sensory cortices corresponds to non-task-related or spontaneous movements. This may indicate an important difference between invertebrate and vertebrate brains and the degree to which ongoing movements shape activity across different brains. However, it should be pointed out that while motor activity is known to affect sensory activity in flies, this modulation is tied to movements that are informative for either optomotor responses or steering. In these experiments, although flies walked abundantly, they did not produce reliable responses to auditory stimuli, although playback of the same auditory stimuli can reliably change walking speed in freely behaving flies. Adjusting the paradigm to drive such responses might uncover behavioral modulation of auditory activity. Alternatively, behavioral modulation of auditory responses may occur primarily in motor areas, such as the central complex, or areas containing projections of descending neurons. Further dissection of the sources of this variability would require the simultaneous capture of more brain activity in behaving animals while not significantly compromising spatial resolution (Pacheco, 2021).

This paper provides tools for characterizing sensory activity registered in common atlas coordinates for comparisons across trials, individuals and sexes. By producing maps for additional modalities and stimulus combinations and by combining these maps with information on connectivity between and within brain regions, the logic of how the brain represents the myriad stimuli and their combinations present in the world should emerge (Pacheco, 2021).

Several features of the adult nervous systems develop in a "critical period," (CP) during which high levels of plasticity allow neural circuits to be tuned for optimal performance. Through an analysis of long-term olfactory habituation (LTH) in female Drosophila, this study provides new insight into mechanisms by which CPs are regulated in vivo LTH manifests as a persistently reduced behavioural response to an odorant encountered for four continuous days and occurs together with the growth of specific, odorant-responsive glomeruli in the antennal lobe. The CP for behavioral and structural plasticity induced by ethyl butyrate (EB) or carbon dioxide (CO(2)) closes within 48 hours after eclosion. The elaboration of excitatory projection neuron (PN) processes likely contribute to glomerular volume increases: both occur together and similarly require cAMP signalling in the antennal lobe inhibitory local interneurons (iLNs). Further, the CP for structural plasticity could be extended beyond 48 hours if EB- or CO(2)-responsive olfactory sensory neurons (OSNs) are silenced after eclosion; thus, OSN activity is required for closing the CP. Strikingly, silencing of glomerulus-selective OSNs extends the CP for structural plasticity only in respective target glomeruli. This indicates existence of a local, short-range mechanism for regulating CP closure (Chodankar, 2020).

Many animals use acoustic signals to attract a potential mating partner. In fruit flies (Drosophila melanogaster), the courtship pulse song has a species-specific interpulse interval (IPI) that activates mating. Although a series of auditory neurons in the fly brain exhibit different tuning patterns to IPIs, it is unclear how the response of each neuron is tuned. This study examined the neural circuitry regulating the activity of antennal mechanosensory and motor center (AMMC)-B1 neurons, key secondary auditory neurons in the excitatory neural pathway that relay song information. By performing Ca(2+) imaging in female flies, it as found that the IPI selectivity observed in AMMC-B1 neurons differs from that of upstream auditory sensory neurons [Johnston's organ (JO)-B]. Selective knock-down of a GABAA receptor subunit in AMMC-B1 neurons increased their response to short IPIs, suggesting that GABA suppresses AMMC-B1 activity at these IPIs. Connection mapping identified two GABAergic local interneurons that synapse with AMMC-B1 and JO-B. Ca(2+) imaging combined with neuronal silencing revealed that these local interneurons, AMMC-LN and AMMC-B2, shape the response pattern of AMMC-B1 neurons at a 15 ms IPI. Neuronal silencing studies further suggested that both GABAergic local interneurons suppress the behavioral response to artificial pulse songs in flies, particularly those with a 15 ms IPI. Altogether, this study identified a circuit containing two GABAergic local interneurons that affects the temporal tuning of AMMC-B1 neurons in the song relay pathway and the behavioral response to the courtship song. These findings suggest that feedforward inhibitory pathways adjust the behavioral response to courtship pulse songs in female flies (Yamada, 2018).

Acoustic communication between insects serves as an excellent model system for analyzing the neuronal mechanisms underlying auditory information processing. To understand the central auditory pathways a large-scale analysis was performed of the interneurons associated with the primary Drosophila auditory center. By screening expression driver strains and performing single-cell labeling of these strains, 44 types of interneurons were identified innervating the primary auditory center - 5 types were local interneurons whereas the other 39 types were projection interneurons connecting the primary auditory center with other brain regions. The projection neurons comprised three frequency-selective pathways and two frequency-embracive pathways. Mapping of their connection targets revealed that five neuropils in the brain - the wedge, anterior ventrolateral protocerebrum, posterior ventrolateral protocerebrum (PVLP), saddle (SAD), and gnathal ganglia (GNG) - were intensively connected with the primary auditory center. In addition, several other neuropils, including visual and olfactory centers in the brain, were directly connected to the primary auditory center. The distribution patterns of the spines and boutons of the identified neurons suggest that auditory information is sent mainly from the primary auditory center to the PVLP, WED, SAD, GNG, and the thoracico-abdominal ganglia. Based on these findings, this study has established the first comprehensive map of secondary auditory interneurons, which indicates the downstream information flow to parallel ascending pathways, multimodal pathways, and descending pathways (Matsuo, 2016).

Animals perform many stereotyped movements, but how nervous systems are organized for controlling specific movements remains unclear. This study used anatomical, optogenetic, behavioral, and physiological techniques to identify a circuit in Drosophila melanogaster that can elicit stereotyped leg movements that groom the antennae. Mechanosensory chordotonal neurons detect displacements of the antennae and excite three different classes of functionally connected interneurons, which include two classes of brain interneurons and different parallel descending neurons. This multilayered circuit is organized such that neurons within each layer are sufficient to specifically elicit antennal grooming. However, differences were found in the durations of antennal grooming elicited by neurons in the different layers, suggesting that the circuit is organized to both command antennal grooming and control its duration. As similar features underlie stimulus-induced movements in other animals, there is a possibility of a common circuit organization for movement control that can be dissected in Drosophila (Hampel, 2015).

Sleep is essential, but animals may forgo sleep to engage in other critical behaviors, such as feeding and reproduction. Previous studies have shown that female flies exhibit decreased sleep after mating, but understanding of the process is limited. This study reports that postmating nighttime sleep loss is modulated by diet and sleep deprivation, demonstrating a complex interaction among sleep, reproduction, and diet. It was also found that female-specific pC1 neurons and sleep-promoting dorsal fan-shaped body (dFB) neurons are required for postmating sleep plasticity. Activating pC1 neurons leads to sleep suppression on standard fly culture media but has little sleep effect on sucrose-only food. Published connectome data suggest indirect, inhibitory connections among pC1 subtypes. Using calcium imaging, it was shown that activating the pC1e subtype inhibits dFB neurons. It is proposed that pC1 and dFB neurons integrate the mating status, food context, and sleep drive to modulate postmating sleep plasticity (Duhart, 2023).

Sleep is required to maintain physiological functions and is widely conserved across species. To understand the sleep-regulatory mechanisms, sleep-regulating genes and neuronal circuits are studied in various animal species. In the sleep-regulatory neuronal circuits in Drosophila melanogaster, the dorsal fan-shaped body (dFB) is a major sleep-promoting region. However, other sleep-regulating neuronal circuits were not well identified. It was recently found that arousal-promoting T1 dopamine neurons, interneurons of protocerebral bridge (PB) neurons, and PB neurons innervating the ventral part of the FB form a sleep-regulatory circuit, which was named "the PB-FB pathway". In the exploration of other sleep-regulatory circuits, it was found that activation of FB interneurons, also known as pontine neurons, promoted arousal. FB interneurons had possible connections with the PB-FB pathway and dFB neurons. Ca2+ imaging revealed that FB interneurons received excitatory signals from the PB-FB pathway. The possible role of FB interneurons to regulate dFB neurons was demonstrated. These results suggested the role of FB interneurons in sleep regulation (Kato, 2022).

This study reports a novel sleep-regulatory pathway that promotes arousal. This study first focused on FB interneurons and found that cholinergic FB interneurons promoted arousal. The arousal-promoting effect of FB interneurons was confirmed by using more specific drivers. These drivers label FB interneurons which receive input from P-FN neurons (that project from the protocerebral bridge to the ventral FB and the NO) and send output to dFB neurons. There should be other FB interneurons that do not have a connection with P-FN neurons or dFB neurons. It means that FB interneurons labeled by two split drivers are only a part of FB interneurons. Therefore we considered that the weaker effects were due to the smaller number of neurons labeled by split-Gal4 lines than NP2320, not the effect of neurons other than FB interneurons. No aclear sleep rebound was observed after neuronal activation. In the previous study, R52B10-Gal4 was used which is reported to drive sleep rebound in the female fly. A clear sleep rebound was observed in female flies but not in male flies. These results indicated that there is a sex difference in the regulation of sleep rebound, at least, in R52B10 neurons. The current study used only male flies and this could be one of the reasons why no clear sleep rebound was observed. It was next asked about the relationship between FB interneurons and known sleep-regulatory circuits. GRASP and Ca2+ experiments were performed, and FB interneurons and R52B10 neurons which label the output neurons of the PB-FB pathway were shown to be anatomically and functionally connected . Although the possibility of other pathways downstream to R52B10 cannot be excluded, this study demonstrated clearly that FB interneurons are one of the downstream to R52B10. Further study will show the impact size of the connection between them in sleep regulation. To investigate the postsynaptic partners of FB interneurons, a GRASP experiment was conducted. FB interneurons and dFB neurons labeled by R23E10 were found to form close associations. Besides, according to the connectome paper and connectome dataset, vDeltaB, C, D, and hDeltaC receive input from P-FN neurons and send output to FB tangential neurons which arborize in layers 6 and 7. This information also supported the idea that R52B10 neurons including P-FN neurons, FB interneurons like vDeltaB, C, D, and hDeltaC neurons, and dFB neurons that are consisted of FB tangential neurons which arborize in layers 6 and 7 form a neuronal circuit. Future study will clarify their functional connection and the role of this circuit in sleep regulation. Furthermore, a previous study showed that neurons that project to the ventral FB (vFB neurons) promote sleep and mediate consolidation of long-term memory. Since axon terminals and dendrites of FB interneurons arborize in both the dorsal and ventral FB, there would be interactions between dFB and vFB neurons via FB interneurons. Further research will clarify the functional relationship between these neurons (Kato, 2022).

According to previous reports, FB interneurons regulate optomotor behavior and express tachykinin, a neuropeptide that regulates aggression. Additionally, T1 dopamine neurons, which are upstream of R52B10 neurons, regulate aggression as well. Besides, P2 neurons, which include FB interneurons, regulate chronic isolation evoked sleep loss. Moreover, courtship-regulator P1 neurons activate T1 neurons and modulate sleep/courtship balance based on the nutritional status. Taking all the information mentioned above into account, it is considerd that arousal signals related to aggression, courtship, nutrition, and vision converge into the PB-FB pathway including FB interneurons to regulate arousal. Further studies should clarify the role of these arousal signals on the PB-FB pathway and FB interneurons in sleep regulation (Kato, 2022).

In conclusion, the results provided possible sleep-regulatory neurons that may connect with the PB-FB pathway and dFB neurons. It is hypothesized that arousal signals are sent from the PB-FB pathway to FB interneurons, inhibit dFB neurons via inhibitory signals, and regulate sleep (Kato, 2022).

Understanding how neural circuits underlie behaviour is challenging even in the connectome era because it requires a combination of anatomical and functional analyses. This is exemplified in the circuit underlying the light avoidance behaviour displayed by Drosophila melanogaster larvae. While this behaviour is robust and the nervous system relatively simple, the circuit is only partially delineated with some contradictions among studies. This study devised trans-Tango MkII, an offshoot of the transsynaptic circuit tracing tool trans-Tango, and implement it in anatomical tracing together with functional analysis. Neuronal inhibition was used to test necessity of particular neuronal types in light avoidance and selective neuronal activation to examine sufficiency in rescuing light avoidance deficiencies exhibited by photoreceptor mutants. These studies reveal a four-order circuit for light avoidance connecting the light-detecting photoreceptors with a pair of neuroendocrine cells via two types of clock neurons. This approach can be readily expanded to studying other circuits (Sorkac, 2022).

The study revealed a circuit consisting of four orders of neurons that connect the Rh5 photoreceptors to PTTH neurons via the 5th-LaN and DN2s. While this circuit mediates the response to bright light, the observation that DN1s are necessary for photophobic response only to low light intensity indicates the existence of an additional pathway for dim light. It is noteworthy that a third, independent system has been reported in which a gustatory receptor mediates photophobic response to high-intensity light in class IV multidendritic neurons. This study has no information as to at which level these pathways might meet, if at all, upstream of the motor neurons (Sorkac, 2022).

The results clarify several earlier studies regarding the role of Pdf-LaNs in light avoidance. In the current experiments, Pdf-LaNs are dispensable for light avoidance, yet their activation is attractive. A potential explanation is that Pdf-LaNs may modulate larval photophobia via inhibition, especially since adult Pdf-LaNs are glycinergic. In addition, the results contradict a previous study reporting that Pdf-LaNs are presynaptic to PTTH neurons. This study relied on a version of GRASP that is in fact not synaptic. Thus, the proposed connection could have been the result of a non-synaptic reconstitution of GFP due to proximity especially since reconstituted GFP was not observed using a synaptic version of GRASP. This result, however, does not rule out a Pdf-LaN-mediated inhibition of the light avoidance circuit from the Rh5 photoreceptors to PTTH neurons. It is conceivable that, alongside their roles in alternative circuits that mediate this behaviour, Pdf-LaNs play inhibitory roles in this circuit as well. Indeed, ablating Pdf-LaNs increases the activity in PTTH neurons as revealed by the GCaMP signal (Sorkac, 2022).

Our analysis of the robust light avoidance response in larvae exemplifies the importance of employing a comprehensive approach combining circuit tracing together with neuronal inhibition and activation to test necessity and sufficiency. Circuit epistasis analysis was made possible by trans-Tango MkII, a new version of trans-Tango that allows researchers to trace and manipulate neural circuits in Drosophila larvae. The combination of a robust and user-friendly genetic tool such as trans-Tango MkII with careful functional analysis constitutes a powerful approach that can be readily expanded to studying other circuits and behaviours (Sorkac, 2022).

Sleep is a universally conserved physiological state which contributes toward basic organismal functions, including cognitive operations such as learning and memory. Intriguingly, organisms can sometimes form memory even without sleep, such that Drosophila display sleep-dependent and sleep-independent memory in an olfactory appetitive training paradigm. Sleep-dependent memory can be elicited by the perception of sweet taste, and this study now shows that a mixed-sex population of flies maintained on sorbitol, a tasteless but nutritive substance, do not require sleep for memory consolidation. Consistent with this, silencing sugar-sensing gustatory receptor neurons in fed flies triggers a switch to sleep-independent memory consolidation, whereas activating sugar-sensing gustatory receptor neurons results in the formation of sleep-dependent memory in starved flies. Sleep-dependent and sleep-independent memory relies on distinct subsets of reward signaling protocerebral anterior medial dopaminergic neurons (PAM DANs) such that PAM-β'2mp DANs mediate memory in fed flies whereas PAM-α1 DANs are required in starved flies. Correspondingly, a feeding-dependent calcium increase was observed in PAM-β'2mp DANs, but not in PAM-α1 DANs. Following training, the presence of sweet sugars recruits PAM-β'2mp DANs, whereas tasteless medium increases calcium in PAM-α1 DANs. Together, this work identifies mechanistic underpinnings of sleep-dependent memory consolidation, in particular demonstrating a role for the processing of sweet taste reward signals (Chouhan, 2022).

Circadian clocks align various behaviors such as locomotor activity, sleep/wake, feeding, and mating to times of day that are most adaptive. How rhythmic information in pacemaker circuits is translated to neuronal outputs is not well understood. This study used brain-wide, 24-h in vivo calcium imaging in the Drosophila brain and searched for circadian rhythmic activity among identified clusters of dopaminergic (DA) and peptidergic neurosecretory (NS) neurons. Such rhythms were widespread and imposed by the PERIOD-dependent clock activity within the ∼150-cell circadian pacemaker network. The rhythms displayed either a morning (M), evening (E), or mid-day (MD) phase. Different subgroups of circadian pacemakers imposed neural activity rhythms onto different downstream non-clock neurons. Outputs from the canonical M and E pacemakers converged to regulate DA-PPM3 and DA-PAL neurons. E pacemakers regulate the evening-active DA-PPL1 neurons. In addition to these canonical M and E oscillators, evidence is for a third dedicated phase occurring at mid-day: the l-LNv pacemakers present the MD activity peak, and they regulate the MD-active DA-PPM1/2 neurons and three distinct NS cell types. Thus, the Drosophila circadian pacemaker network is a polyphasic rhythm generator. It presents dedicated M, E, and MD phases that are functionally transduced as neuronal outputs to organize diverse daily activity patterns in downstream circuits (Liang, 2023).

Drosophila's lateral posterior neurons (LPNs) belong to a small group of circadian clock neurons that is so far not characterized in detail. Thanks to a new highly specific split-Gal4 line, this study describes LPNs' morphology in fine detail, their synaptic connections, daily bimodal expression of neuropeptides, and a putative role of this cluster in controlling daily activity and sleep patterns is proposed. The three LPNs were found to be heterogeneous. Two of the neurons with similar morphology arborize in the superior medial and lateral protocerebrum and most likely promote sleep. One unique, possibly wakefulness-promoting, neuron with wider arborizations extends from the superior lateral protocerebrum toward the anterior optic tubercle. Both LPN types exhibit manifold connections with the other circadian clock neurons, especially with those that control the flies' morning and evening activity (M- and E-neurons, respectively). In addition, they form synaptic connections with neurons of the mushroom bodies, the fan-shaped body, and with many additional still unidentified neurons. Both LPN types rhythmically express three neuropeptides, Allostatin A, Allostatin C, and Diuretic Hormone 31 with maxima in the morning and the evening. The three LPN neuropeptides may, furthermore, signal to the insect hormonal center in the pars intercerebralis and contribute to rhythmic modulation of metabolism, feeding, and reproduction. These findings are discussed in the light of anatomical details gained by the recently published hemibrain of a single female fly on the electron microscopic level and of previous functional studies concerning the LPN.

Animal circadian rhythms persist in constant darkness and are driven by intracellular transcription-translation feedback loops. Although these cellular oscillators communicate, isolated mammalian cellular clocks continue to tick away in darkness without intercellular communication. To investigate these issues in Drosophila, behavior as well as molecular rhythms were assayed within individual brain clock neurons while blocking communication within the ca. 150 neuron clock network. CRISPR-mediated neuron-specific circadian clock knockouts were also generated. The results point to two key clock neuron groups: loss of the clock within both regions but neither one alone has a strong behavioral phenotype in darkness; communication between these regions also contributes to circadian period determination. Under these dark conditions, the clock within one region persists without network communication. The clock within the famous PDF-expressing s-LNv neurons however was strongly dependent on network communication, likely because clock gene expression within these vulnerable sLNvs depends on neuronal firing or light (Schlichting, 2019).

Neuronal networks make myriad contributions to behavior and physiology. By definition, individual neurons within a network interact, and different networks also interact to coordinate specialized functions. For example, the visual cortex and motor output centers must coordinate to react properly to environmental changes. In a less immediate fashion, sleep centers and circadian clocks are intertwined to properly orchestrate animal physiology. The brain clock is of special interest: it not only times and coordinates physiology within neuronal tissues but also sends signals to the body to keep the entire organism in sync with the cycling external environment (Schlichting, 2019).

The small, circumscribed Drosophila clock network is ideal to address circadian communication issues. The comparable region in mammals, the suprachiasmatic nucleus, is composed of thousands of cells depending on the species. There are in contrast only 75 clock neurons per hemisphere in Drosophila. These different clock neurons can be divided into several subgroups according to their location within the fly brain. There are 4 lateral and three dorsal neuron clusters, which have different functions in controlling fly physiology (Schlichting, 2019).

The four small ventro-lateral neurons (sLNvs) are arguably the most important of the 75 clock neurons. This is because ablating or silencing these neurons abolishes rhythms in constant darkness (DD). They reside in the accessory medulla region of the fly brain, an important pacemaker center in many insects, and express the neuropeptide PDF. In addition, they are essential for predicting dawn. A very recent study suggests that the sLNvs are also able to modulate the timing of the evening (E) peak of behavior via PDF. The other ventral-lateral group, the four large-ventro-lateral neurons (lLNvs), also express PDF and send projections to the medulla, the visual center of the fly brain; they are important arousal neurons. Consistent with the ablation experiments mentioned above, the absence of pdf function or reducing PDF levels via RNAi causes substantial arrhythmic behavior in DD (Schlichting, 2019).

Other important clock neurons include the dorso-lateral neurons (LNds), which are essential for the timing of the E peak and adjustment to long photoperiods. Two other clock neuron groups, the lateral-posterior neurons (LPN and a subset of the dorsal neurons (DN1s), were recently shown to connect the clock network to sleep centers in the fly central complex. The DN2 neurons are essential for temperature preference rhythms, whereas no function has so far been assigned to the DN3s (Schlichting, 2019).

Despite these distinct functions, individual clock neuron groups are well-connected to each other. At the anatomical level, all lateral neuron clusters and even DN1 dorsal neurons send some of their projections into the accessory medulla, where they can interact. A second area of common interaction is the dorsal brain; only the lLNvs do not project there (Schlichting, 2019).

Several studies have investigated interactions between different clock neurons. Artificially expressing kinases within specific clock neurons causes their clocks to run fast or slow and also changes the overall free-running period of the fly, indicating that network signaling adjusts behavior. Similarly, speeding up or slowing down individual neurons is able to differentially affect behavioral timing in standard light-dark (LD) cycles. A high level of neuronal plasticity within the network also exists: axons of individual cells undergo daily oscillations in their morphology, and neurons change their targets depending on the environmental condition (Schlichting, 2019).

How neuronal communication influences the fly core feedback loop is not well understood. The latter consists of several interlocked transcriptional-translational feedback loops, which probably underlie rhythms in behavior and physiology. A simplified version of the core feedback loop consists of the transcriptional activators Clock (CLK) and Cycle (CYC) and the transcriptional repressors Period (PER) and Timeless (TIM). CLK and CYC bind to E-boxes within the period (per) and timeless (tim) genes (among other clock-controlled genes) and activate their transcription. After PER and TIM synthesis in the cytoplasm, they form a heterodimer and enter the nucleus toward the end of the night. There they interact with CLK and CYC, release them from their E-box targets and thereby inhibit their own transcription. All 75 pairs of clock neurons contain this canonical circadian machinery, which undergoes daily oscillations in level. Indeed, the immunohistochemical cycling of PER and TIM within these neurons is a classic assay to visualize these molecular oscillations (Schlichting, 2019).

Silencing PDF neurons stops their PER cycling, indicating an important role of neuronal firing in maintaining circadian oscillations. However, only two time points were measured, and the results were possibly confounded by developmental effects. PDF neuron silencing also phase advances PER cycling in downstream neurons, suggesting that PDF normally serves to delay cycling in target neurons. This is consistent with experiments showing that PDF signaling stabilizes PER. In addition, neuronal activation is able to mimic a light pulse and phase shift the clock due to firing-mediated TIM degradation (Schlichting, 2019).

To investigate more general features of clock neuron interactions on the circadian machinery, the majority of the fly brain clock neurons were silenced, and behavior and clock protein cycling within the circadian network was examined in a standard light-dark cycle (LD) as well as in constant darkness (DD). Silencing abolished rhythmic behavior but had no effect on clock protein cycling in LD, indicating that the silencing affects circadian output but not oscillator function in a cycling light environment. Silencing similarly abolished rhythmic behavior in DD but with very different effects on clock protein cycling. Although protein cycling in the LNds was not affected by neuronal silencing in DD, the sLNvs dampened almost immediately. Interestingly, this differential effect is under transcriptional control, suggesting that some Drosophila clock neurons experience activity-regulated clock gene transcription. Cell-specific CRISPR/Cas9 knockouts of the core clock protein Per further suggests that network properties are critical to maintain wild-type activity-rest rhythms. The data taken together show that clock neuron communication and firing-mediated clock gene transcription are essential for high amplitude and synchronized molecular rhythms as well as rhythmic physiology (Schlichting, 2019).

The central clock of animals is essential for dictating the myriad diurnal changes in physiology and behavior. Knocking out core clock components such as period or Clock severely disrupts circadian behavior as well as molecular clock properties in flies and mammals. This study show that similar behavioral effects occur when the central clock neurons are silenced and thereby abolish communication within this network and with downstream targets, that is fly behavior becomes arrhythmic in LD as well as DD conditions and resembles the phenotypes of core clock mutant strains (Schlichting, 2019).

Despite the loss of all rhythmic behavior, silencing did not impact the molecular machinery in LD conditions: PER and PDP1 protein cycling was normal. These findings suggest that (1) rhythmic behavior requires clock neuron output, which is uncoupled from the circadian molecular machinery by network silencing, and (2) synchronized molecular rhythms of clock neurons do not require neuronal activity. These findings are in agreement with previous work showing that silencing the PDF neurons had no effect on Per cycling within these neurons. The results presumably reflect the strong effect of the external light-dark cycle on these oscillators (Schlichting, 2019).

In DD however, the individual neurons change dramatically: the different neurons desynchronize, and their protein cycling damps to different extents. Interestingly, sLNv cycling relies most strongly on neuronal communication: these neurons cycle robustly in controls but apparently not at all in the silenced state. sLNvs were previously shown to be essential for DD rhythms. Unfortunately, the sensitivity of immunohistochemistry precludes determining whether the molecular clock has actually stopped or whether silencing has only (dramatically) reduced cycling amplitude. However, a simple interpretation of the adult-specific silencing experiment favors a stopped clock: decreasing the temperature to 18 degrees after a week at high temperature failed to rescue rhythmic behavior. A similar experiment in mammals gave rise to the opposite result, suggesting an effect of firing on circadian amplitude in that case. However, different explanations cannot be excluded, for example chronic effects of neuronal silencing or a too large phase difference between the different neuronal subgroups to reverse after a week without communication (Schlichting, 2019).

In either case, a stopped clock or an effect on clock protein oscillation amplitude, these results make another link to the mammalian literature: modeling of the clock network suggests that different neurons resynchronize more easily if the most highly-connected cells are intrinsically weak oscillators. The sLNvs are essential for DD rhythms, known to communicate with other clock neurons and are situated in the accessory medulla; this is an area of extensive neuronal interactions in many insects. These considerations rationalize weak sLNv oscillators (Schlichting, 2019).

An important role of interneuron communication in DD is in agreement with previous work showing that altering the speed of individual neuron groups can change the phase of downstream target neurons. An important signaling molecule is the neuropeptide PDF: its absence changes the phase of downstream target neurons, and silencing PDF neurons causes an essentially identical phenotype to the lack of PDF. However, the effects reported in this study are much stronger and show different levels of autonomy than PDF ablation, suggesting that other signaling molecules and/or the neuronal activity of additional clock neurons are essential to maintain proper rhythmic clock protein expression (Schlichting, 2019).

To address these possibilities, this study took two approaches. First, clock gene RNA levels were investigated after silencing. The goal was to assess whether the damping of silenced neurons is under gene expression control, likely transcriptional control. Indeed, tim mRNA profiles nicely reproduced the protein cycling profiles: robust cycling of all (assayed) clock neurons was maintained in LD even with silencing, but tim-mRNA levels in the sLNvs stopped cycling in DD; in contrast, robust cycling was maintained in the LNds. This suggests that the changes in protein cycling amplitude and also possibly phase are under transcriptional control. Importantly, the tim signal in the sLNvs disappeared upon silencing, suggesting that neuronal activity promotes clock gene transcription at least in this subset of neurons. This recapitulates for the first time in Drosophila the robust positive relationship between neuronal firing and clock gene transcription in mammals. To date, Drosophila neuronal firing had only been connected to post-transcriptional clock protein regulation, namely TIM degradation. Conceivably, these two effects are connected: TIM degradation might be required to relieve transcriptional repression and maintain cycling (Schlichting, 2019).

The second approach was a cell-specific knockout strategy, applied to the clock neuron network. Three guides were generated targeting the CDS of per and also CAS9 was expressed in a cell-specific manner. The guides caused double strand breaks in the per gene, which in turn led to cell-specific per mutations. This adult brain knockout strategy worked reliably and specifically, in glial cells as well as neurons, with high efficiency and with no apparent background effects. This strategy was successfully used to knock out most if not all Drosophila GPCRs and it is believed to be superior to RNAi for most purposes. Importantly, expression of the guides with the clk856-GAL4 driver phenocopied per⁰¹ behavior. To focus on individual clock neurons, cell-specific knockouts were generated in different clock neurons. PERKO in the PDF cells did not increase the level of arrhythmicity, only a PERKO in most lateral neurons, E cells as well as PDF cells, generated high levels of arrhythmic behavior (Schlichting, 2019).

Many animals exhibit morning and evening peaks of locomotor behavior. In Drosophila, two corresponding circadian neural oscillators-M (morning) cells and E (evening) cells-exhibit a corresponding morning or evening neural activity peak. Yet little is known of the neural circuitry by which distinct circadian oscillators produce specific outputs to precisely control behavioral episodes. This study shows that ring neurons of the ellipsoid body (EB-RNs) display spontaneous morning and evening neural activity peaks in vivo: these peaks coincide with the bouts of locomotor activity and result from independent activation by M and E pacemakers. Further, M and E cells regulate EB-RNs via identified PPM3 dopaminergic neurons, which project to the EB and are normally co-active with EB-RNs. These in vivo findings establish the fundamental elements of a circadian neuronal output pathway: distinct circadian oscillators independently drive a common pre-motor center through the agency of specific dopaminergic interneurons (Liang, 2019).

Light is one of the most important factors regulating rhythmical behavior of Drosophila melanogaster. It is received by different photoreceptors and entrains the circadian clock, which controls sleep. The retina is known to be essential for light perception, as it is composed of specialized light-sensitive cells which transmit signal to deeper parts of the brain. This study examined the role of specific photoreceptor types and peripheral oscillators located in these cells in the regulation of sleep pattern. Sleep was shown to be controlled by the visual system in a very complex way. Photoreceptors expressing Rh1, Rh3 are involved in night-time sleep regulation, while cells expressing Rh5 and Rh6 affect sleep both during the day and night. Moreover, Hofbauer-Buchner (HB) eyelets which can directly contact with s-LN (v) s and l-LN (v) s play a wake-promoting function during the day. In addition, this study showed that L2 interneurons, which receive signal from R1-6, form direct synaptic contacts with l-LN (v) s, which provides new light input to the clock network (Damulewicz, 2020).

Tetrad synapses are formed between the retina photoreceptor terminals and postsynaptic cells in the first optic neuropil (lamina) of Drosophila. They are remodelled in the course of the day and show distinct functional changes during activity and sleep. These changes result from fast degradation of the presynaptic scaffolding protein Bruchpilot (BRP) by Cryptochrome (CRY) in the morning and depend on BRP-170, one of two BRP isoforms. This process also affects the number of synaptic vesicles, both clear and dense-core, delivered to the presynaptic elements. In cry⁰¹ mutants lacking CRY and in brp^Δ170, the number of synaptic vesicles is lower in the morning peak of activity than during night-sleep while in wild-type flies the number of synaptic vesicles is similar at these two time points. CRY may also set phase of the circadian rhythm in plasticity of synapses. The process of synapse remodelling stimulates the formation of clear synaptic vesicles in the morning. They carry histamine, a neurotransmitter in tetrad synapses and seem to be formed from glial capitate projections inside the photoreceptor terminals. In turn dense-core vesicles probably carry synaptic proteins building the tetrad presynaptic element (Damulewicz, 2020).

The results confirmed earlier studies that the presynaptic element (T-bar) of tetrad synapses is remodelled during the day and night and this rhythm is regulated by light and circadian clock. In the present study, it was found additionally that cyclic changes occur in the T-bar ultrastructure and its volume. Ultrastructural changes in T-bars and synaptic vesicles were possible because of using high resolution electron microscope tomography (EMT). In the present study it was also found that the number of synaptic vesicles cycles during the day, but this rhythm is masked by light. This result indicates that in the case of tetrad synapses, which are sites of fast neurotransmission between photoreceptors and the first order interneurons, intense neurotransmission occurs during the morning peak of locomotor activity, when more synaptic vesicles are attached to the T-bar platform than during sleep (ZT16) and are transported from capitate projections located next to the T-bars. At ZT16, tetrad synapses are ready for neurotransmission, since the total number of synaptic vesicles near tetrad synapses is similar at ZT1 and ZT16, but it is in a standby mode with lower frequency of synapses and vesicles, which are not attached to the T-bar platform and are not delivered from glial cells, respectively. However, transmission can be activated and is efficient because synapses during sleep have larger volume, and synaptic vesicles can be transported to the T-bar platform, if necessary, in response to an unexpected light pulse. Cyclic remodelling of tetrad presynaptic sites depends on BRP, which must be delivered to T-bars and degraded after light exposure after binding to CRY. This fast remodelling of synapses affects the number of vesicles transported to the presynaptic element (Damulewicz, 2020).

The present study was carried out only in light/dark (LD 12:12) conditions because in earlier studies it was found that the rhythm of the changes in BRP abundance in tetrad synapses of D. melanogaster is circadian. The rhythm is maintained in constant darkness (DD) and abolished in the per⁰¹ clock null mutant. On the basis of these results, it is assumed that rhythmic changes during the 24 h cycle reported is this paper are also circadian; however, in DD, the morning peak in BRP is not present because it depends on light (Damulewicz, 2020).

Electron microscope tomography (EMT) used in this study showed that synaptic vesicles are attached to the platform of the T-bar with filamentous proteins that are reduced in brp^Δ170 and brp^Δ190 mutants, which have fewer synaptic vesicles compared with wild-type flies. Two types of vesicles, clear and dense-core were detected. Dense-core synaptic vesicles were less numerous than clear ones. It is known that synaptic vesicles of tetrad synapses contain histamine, while the content of dense-core vesicles is unknown. It is possible that they carry presynaptic proteins to the T-bar. The comparison of ultrastructure of tetrad synapses in the morning peak of activity (ZT1) and during sleep (ZT16) indicates that more vesicles are attached to the T-bar platform and to capitate projections at ZT1, but this pattern is not present in brpΔ170, brp^Δ190 and cry⁰¹ . This result confirmed an earlier study that both high motor activity in the morning and light exposure increase activity of the visual system. In the morning, there is intense transport of synaptic vesicles to T-bars and delivery of histamine in clear vesicles from the epithelial glial cells through capitate projections. The evidence for a role of capitate projections in neurotransmitter recycling has already been reported and now this study showed, using EMT, that vesicles are produced from capitate projections and directly delivered to T-bars. This intense transport is damaged in all mutants studied, suggesting that both BRP isoforms and CRY are needed for this process. In addition, in the brp^Δ190 mutant lacking BRP-190, the T-bar structure is less dense than in the other strains studied. In a previous study, it was found that there are approximately 50% fewer synapses in brp^Δ190 than in Canton S and brp^Δ170. Another study reported that BRP isoforms are important for the formation of T-bars in neuromuscular junctions, and in brp mutants T-bars are smaller than in controls. T-bar height was reduced in brp^Δ190, whereas the widths of pedestal and platform were reduced in both mutants. They also decrease transmission since the active zone was smaller in both mutants and the number of synaptic vesicles was reduced. These ultrastructural changes are correlated with cell physiology since the amplitude of evoked excitatory junctional current was decreased in both mutants with a stronger effect in brp^Δ190 (Damulewicz, 2020).

The obtained reconstructions of tetrad T-bars from TEM serial sections of the lamina showed that although there were fewer synapses during the night (ZT16), the volume of the T-bar was larger at that time than in the morning (ZT1), while the total number of synaptic vesicles was similar. In contrast, a day/night difference (ZT1 vs. ZT16) in the number of vesicles was observed in brp^Δ170 and cry⁰¹ . This suggests that the CRY protein and BRP-170 are responsible for an increase in the number of synaptic vesicles during the morning peak of activity. Since CRY co-localizes with BRP and is involved in BRP degradation in the morning, CRY is probably also involved in the degradation of other proteins of synaptic vesicle organization in the morning since the number of vesicles in cry⁰¹ was low in the morning but high at night (ZT16). It seems that in cry⁰¹, synaptic vesicles are not delivered to the photoreceptor terminals from capitate projections and tethered to the cytomatrix in the morning. It is also interesting that the daily rhythm in the number of vesicles is not maintained in BRP mutants, which confirms an earlier study, and the BRP N-terminus, which lacks brp^Δ190, is necessary to maintain daily remodelling of the T-bar structure. Although both isoforms participate in building the cytomatrix their functions seem to be different in the course of the day. It is also possible that CRY is not only responsible for degradation of synaptic proteins but also as a protein, what is known, affecting the clock. In another cell types, in clock neurons l-LNvs, CRY, except interaction with TIM, is responsible for blue light response and firing of the l-LNvs. In the lamina it was found that in cry⁰¹ mutant the daily rhythm in synapse number and their remodelling was delayed in phase and the day/night difference in Canton S increased when peaks in the number of synapses were shift to ZT4 and ZT16. In result the difference between ZT1 and ZT16 was increased in cry⁰¹ (Damulewicz, 2020).

BRP is also responsible for rapid remodelling of the presynaptic active zone (AZ), and as reported in Drosophila NMJ, presynaptic homeostatic potentiation increases the number of BRP molecules and other AZ proteins, Unc13A and RBP, within minutes (Damulewicz, 2020).

When synaptic vesicles were counted at two different distances from the T-bar, to 200 nm and above 200 nm, there were differences between clear vesicles containing histamine and dense-core ones located in both areas. More clear vesicles were located near the T-bar and fewer above 200 nm. In the case of dense-core vesicles, their number was similar in both areas. This difference was not so striking in mutants in the case of vesicles located next to the platform, but in brp^Δ170 and cry⁰¹, there were more dense-core vesicles at ZT16 in the distance above 200 nm than closer to the T-bar. This result indicates that BRP-170 and CRY are important for the distribution of clear synaptic vesicles next to the T-bar as well as dense-core vesicles located above 200 nm from the presynaptic element. It is possible that dense-core vesicles contain T-bar proteins, probably BRP. When transport along the actin cytoskeleton is disrupted, the number of tetrad synapses decreases, and rapid AZ remodelling also fails (Damulewicz, 2020).

The above mentioned ultrastructural changes depend on the level of the presynaptic scaffolding protein BRP, which changes in abundance during the day and night. These changes are controlled by the daily expression of CRY, which seems to have many functions in photoreceptors in addition to being the circadian clock photoreceptor. In an earlier study, it was found that CRY interacts with BRP but only during light exposure and leads to the degradation of BRP during the day/light phase of the 24 h cycle. This seems to be responsible for the decrease in BRP level in the middle of the day after its peak at the beginning of the day. The lack of CRY in cry⁰¹ mutants changes the pattern of the tetrad presynaptic profile frequency during the day and the size of the T-bar. However, the rhythm is not completely abolished, which indicates that other proteins are also involved in the daily remodelling of tetrad synapses. Since CRY plays several functions in photoreceptors, changes in the number of tetrad synapse and T-bar size in cry⁰¹ may result from different processes and lack of interactions of CRY with TIM and BRP. CRY is a component of the molecular clock and interacts with TIM, and this may affect daily changes in the number and size of T-bars. In addition, light-activated CRY binds BRP and targets it to degradation. Previous work showed that flies with constitutively active CRY have low BRP level. In turn, cry⁰¹ mutants show changes in the pattern of BRP expression, with higher BRP level during the day (ZT4), at the time when wild-type flies have minimum of BRP expression. The pattern of BRP expression is similar to the pattern of daily changes in tetrad synapse number, so it is possible that the number of synapses or T-bar size is directly dependent on the amount of BRP which oscillates during the day. However, CRY in the retina photoreceptors binds also actin and is involved in the organization of phototransduction cascade of proteins in rhabdomeres38. This may be also involved in the regulation of T-bar structure. The differences in T-bar size of cry⁰¹ are shown at time when in Canton S CRY is active (during the day) or its level increases (ZT16). At the beginning of the night the level of CRY is very low, so there was no effect on T-bar structure and no difference between CS and cry⁰¹ was observed (Damulewicz, 2020).

The epithelial glial cells are important for many processes during phototransduction and in recycling neurotransmitters and other compounds during the night. Glia take up histamine and metabolize it to carcinin, which is next delivered to the photoreceptor terminals, and capitate projections are involved in this process. Activity of glial cells is also controlled by the circadian clock. During the night, glial cells seem to be more active than neurons to recycle neurotransmitters, and many proteins, including proteins of ion pumps, are found at higher concentrations at that time. The high number of synaptic vesicles near the tetrad T-bar during the morning peak of activity in Drosophila seems to depend on capitate projections invaginating from the epithelial glia to the photoreceptor terminals in the lamina of Drosophila (Damulewicz, 2020).

Although the presynaptic cytomatrix can be rapidly remodelled with transmission strength, it is also affected by motor and visual system activity, external factors, such as light in the case of the visual system, and the circadian clock, showing plasticity and correlation to changes in behaviour during the day/night cycle. As was shown in the present study synaptic plasticity and synapse remodelling during the day is a complex process which involves presynaptic proteins of the T-bar as well as two types of synaptic vesicles, clear and dense-core, and glial cells. It was also found that fast degradation of proteins involved in transmission is as important as pre- and postsynaptic protein synthesis (Damulewicz, 2020).

The central complex is one of the major brain regions that control sleep in Drosophila. However, the circuitry details of sleep regulation have not been elucidated yet. This study shows a novel sleep-regulating neuronal circuit in the protocerebral bridge (PB) of the central complex. Activation of the PB interneurons labeled by the R59E08-Gal4 and the PB columnar neurons with R52B10-Gal4 promoted sleep and wakefulness, respectively. A targeted GFP reconstitution across synaptic partners (t-GRASP) analysis demonstrated synaptic contact between these two groups of sleep-regulating PB neurons. Furthermore, it was found that activation of a pair of dopaminergic (DA) neurons projecting to the PB (T1 DA neurons) decreased sleep. The wake-promoting T1 DA neurons and the sleep-promoting PB interneurons formed close associations. Dopamine 2-like receptor (Dop2R) knockdown in the sleep-promoting PB interneurons increased sleep. These results indicated that the neuronal circuit in the PB, regulated by dopamine signaling, mediates sleep-wakefulness (Tomita, 2021).

Mechanosensory neurons located across the body surface respond to tactile stimuli and elicit diverse behavioral responses, from relatively simple stimulus location-aimed movements to complex movement sequences. How mechanosensory neurons and their postsynaptic circuits influence such diverse behaviors remains unclear. Previous work has shown that Drosophila perform a body location-prioritized grooming sequence when mechanosensory neurons at different locations on the head and body are simultaneously stimulated by dust. This study identified nearly all mechanosensory neurons on the Drosophila head that individually elicit aimed grooming of specific head locations, while collectively eliciting a whole head grooming sequence. Different tracing methods were used to reconstruct the projections of these neurons from different locations on the head to their distinct arborizations in the brain. This provides the first synaptic resolution somatotopic map of a head, and defines the parallel-projecting mechanosensory pathways that elicit head grooming (Eichler, 2023).

In many behaviors such walking and swimming, animals need to coordinate their left and right limbs. In Drosophila, wing grooming can be induced by activation of sensory organs called campaniform sensilla. Flies usually clean one wing at a time, coordinating their left and right hind legs to sweep the dorsal and ventral surfaces of the wing. This study has identified a pair of interneurons located in the ventral nerve cord that was named wing projection neurons 1 (wPN1) whose optogenetic activation induces wing grooming. Inhibition of wPN1 activity reduces wing grooming. They receive synaptic input from ipsilateral wing campaniform sensilla and wing mechanosensory bristle neurons, and they extend axonal arbors to the hind leg neuropils. Although they project contralaterally, their activation induces ipsilateral wing grooming. Anatomical and behavioral data support a role for wPN1 as command neurons coordinating both hind legs to work together to clean the stimulated wing (Zhang, 2022).

This study has identified wPN1 as potential command neurons for wing grooming. Although the criteria for command neurons is debated, neurons whose activation can induce a coherent motor program and whose function is required for that behavior to occur normally, such as wPN1, provide important entry points to map the neural circuits governing that behavior. The characterization of wPN1 reveals additional complexities in the control of grooming. Wing scratch remains unaffected, indicating additional sensory-to-motor pathways between wings and legs. Other left-right coordination circuits must also exist, since inhibition of wPN1 disrupts wing grooming but leaves hind-leg rubbing largely normal. wPN1 provides an entry point to identify downstream commissural and pre-motor neurons that coordinate left and right leg movements (Zhang, 2022).

Wing cleaning normally occurs late in the hierarchical sequence of grooming subroutines, and wPN1 may provide insights into when it is selected. Perhaps sensory information from wing campaniform sensilla and mechanosensory bristles integrate or accumulate to a threshold sufficient to activate wPN1, or perhaps wPN1 is transiently inhibited by circuits driving other grooming behaviors. Identification of wPN1 makes these testable hypotheses with the potential to reveal a general control mechanism for sequential motor behaviors (Zhang, 2022).

These behavior and anatomy data provide strong evidence that wPN1 plays an essential, command-like role in coordinating left and right back legs, but higher-resolution behavioral analysis and complete neuronal circuit mapping downstream of wPN1 are necessary to confirm this function (Zhang, 2022).

Sweet and bitter compounds excite different sensory cells and drive opposing behaviors. However, it remains unclear how sweet and bitter tastes are represented by the neural circuits linking sensation to behavior. To investigate this question in Drosophila, this study devised trans-Tango(activity), a strategy for calcium imaging of second-order gustatory projection neurons based on trans-Tango, a genetic transsynaptic tracing technique. Spatial overlap was found between the projection neuron populations activated by sweet and bitter tastants. The spatial representation of bitter tastants in the projection neurons was consistent, while that of sweet tastants was heterogeneous. Furthermore, it was discovered that bitter tastants evoke responses in the gustatory receptor neurons and projection neurons upon both stimulus onset and offset and that bitter offset and sweet onset excite overlapping second-order projections. These findings demonstrate an unexpected complexity in the representation of sweet and bitter tastants by second-order neurons of the gustatory circuit (Snell, 2022).

A central question in taste coding is how sweet and bitter tastes are represented beyond the first-order sensory neurons of the circuit. This study is the first to broadly characterize the taste responses of second-order projection neurons in Drosophila, a strategy enabled by trans-Tango(activity), a novel configuration of trans-Tango. Evidence was found for a variety of GPN response profiles, including some tuned specifically to either sweet or bitter tastants, and some that respond to both. It was also discovered that bitter tastants can evoke activity in GRNs and GPNs upon both stimulus onset and offset. The spatial distribution of the GPN offset response extends beyond the onset-responsive region and overlaps with regions responding to sweet tastants. These results thus demonstrate considerable complexity in the taste responses of GPNs (Snell, 2022).

Beyond these findings on taste representation, this study demonstrates more generally that trans-Tango can fill a niche in analyzing neural circuit function. Although a plethora of genetic tools exist for probing circuit function in Drosophila, they are limited by genetic access (e.g., the expression patterns of Gal4 driver lines). There is an obvious use in having a line that labels a broad population of neurons at a defined stage of the circuit; for example, the GH146-Gal4 driver has been extensively used because it labels a broad set of second-order olfactory projection neurons with little background expression (Snell, 2022).

In many other circuits, though, no comparable driver exists. Using trans-Tango initiated from a sparse set of starter neurons, one can selectively image the calcium responses of their downstream projection neurons, as has been done in this study in the gustatory system. The increasing availability of highly specific split-Gal4 drivers will enable this strategy in a wide variety of systems (Snell, 2022).

Moreover, this study demonstrates that using a nuclear-localized reporter as an output of trans-Tango can facilitate the counting of postsynaptic neurons. This strategy can be used to assess the degree of overlap between the second-order neurons of two different circuits, as has been done in this study for the sweet and bitter circuits (Snell, 2022).

A caveat of this analysis method is that the regions of interest (ROIs) obtained through the segmentation algorithm do not necessarily have a one-to-one correspondence with underlying neurons. As a result, it was not possible to definitively conclude whether a given ROI response profile matches the tuning of an individual gustatory projection neurons (GPNs). It is thus possible that ROIs exhibiting responses to multiple tastants result from the close proximity of multiple narrowly tuned neurons. Although proximity is unlikely to fully explain these findings, determining the extent to which ROIs reflect multiple cell types will require identifying cell-type-specific markers for different subclasses of GPNs (Snell, 2022).

Furthermore, this caveat implies that the proportion of ROIs responding to a given stimulus does not necessarily correspond to the proportion of GPNs responding to that stimulus. Thus, it is not possible to draw a strong conclusion about the relative number of GPNs exhibiting each observed response profile. Although ROIs responsive to both bitter and sweet tastants are described, the underlying GPNs with this response profile may be few in number. However, even one such neuron type would be notable, as no GRNs have been reported to encode both sweet and bitter taste (Snell, 2022).

The range of tastant responses observed suggests that second-order gustatory neurons may exhibit more diverse tuning profiles than first-order neurons. For example, some GPNs appear to encode both bitter taste and either water or mechanosensory features of the stimulus. Although weak calcium responses to water have been observed in bitter GRNs, responses of similar strength to bitter and water have been observed in many GPN ROIs. This suggests that GPNs may either nonlinearly transform the bitter GRN signal or perhaps integrate sensory inputs across different taste qualities and even across different sensory modalities. Additionally, the relative magnitudes of the bitter onset and offset responses varied across regions, with some responding similarly to these features and others responding far more strongly to offset. A similar dichotomy has not been observed in GRNs. Thus, these two types of GPN bitter responses likely arise from different circuit-level mechanisms. Furthermore, contrary to observed responses in GRNs, more homogeneity has been observed in GPN responses to bitter tastants than to sweet ones. These results suggest that substantial signal transformation occurs in the transition from the first to the second order of the taste circuit. (Snell, 2022).

The discovery of a bitter offset response is supported by an additional report of this phenomenon, along with another report of an offset response of sweet GRNs to lactic acid. The fact that the fly actively encodes onset and offset of some tastants raises questions about the relevance of these responses for the animal. Distinct onset and offset responses have been observed in other sensory modalities, such as vision. In vision, for example, stimulus offset may serve as a cue for the motion of an object. Bitter offset might similarly signal a change in the fly's gustatory environment. In the wild, flies can encounter a mixture of noxious and nutritious substances at a single feeding site: Drosophila feed on microbes that grow on decaying plant matter, and some of these microbes metabolize plant-derived compounds that are normally toxic to flies, thereby making them safe for the fly's consumption (Snell, 2022).

As many plant toxins are bitter, a fly sampling such a food source would encounter a series of bitter and non-bitter patches; a bitter offset response could thus signal movement off of a toxic substrate and onto a nutritive microbial one (Snell, 2022).

The data suggests that different bitter-responsive GPNs exhibit a different balance of onset- and offset-driven activity. Separate neural channels differentially encoding bitter onset and offset may endow the fly with greater sensitivity to changes in bitter concentration. In primate vision, separate ON and OFF pathways have been proposed to enable metabolically efficient coding of intensity changes to enhance contrast sensitivity and increase the dynamic range of the system (Snell, 2022).

Likewise, the cockroach has separate olfactory sensory neurons tuned to odor onset and offset, which increase the animal's ability to detect small concentration changes. The bitter offset pathway in Drosophila might perform a similar function, enabling the fly to efficiently navigate concentration gradients of bitter compounds on heterogeneous food sources. Finally, as this parallel ON/OFF pathway circuit motif has been observed in several sensory systems, including vision, olfaction, touch, and thermosensation, it may have general utility in sensory coding (Snell, 2022).

An alternative explanation is that bitter onset and offset may have opposing hedonic valences to the fly. The cessation of punishment is known to act as a reward in conditioning experiments (Snell, 2022).

Because bitter taste is aversive, bitter offset may thus be interpreted as rewarding. Separating these components of the bitter taste response may allow for differential input to the dopaminergic neurons of the mushroom body that mediate aversive and appetitive conditioning. The intriguing observation that the bitter offset response overlaps with the sweet response in lateral regions is consistent with this idea. If there is a GPN that underlies both the bitter offset and sweet responses in this area, this neuron may functionally encode the valence of the stimulus. Yet, even if these responses to bitter offset and sweet are caused by separate GPNs, their overlapping projections in this region may indicate that these GPNs converge onto common third-order neurons in the circuit. The results suggest that some GPNs integrate sweet and bitter taste information. Such integration may indicate that a population coding model is operative in the gustatory system of the fly. However, determining the coding scheme implemented by the gustatory system will require further investigation, including a determination of how that code is read out by downstream regions. It remains to be seen how downstream circuits use the taste information encoded by GPNs to sculpt the behavior of the fly (Snell, 2022).

Neural circuit assembly features simultaneous targeting of numerous neuronal processes from constituent neuron types, yet the dynamics is poorly understood. This study used the Drosophila olfactory circuit to investigate dynamic cellular processes by which olfactory receptor neurons (ORNs) target axons precisely to specific glomeruli in the ipsi- and contralateral antennal lobes. Time-lapse imaging of individual axons from 30 ORN types revealed a rich diversity in extension speed, innervation timing, and ipsilateral branch locations and identified that ipsilateral targeting occurs via stabilization of transient interstitial branches. Fast imaging using adaptive optics-corrected lattice light-sheet microscopy showed that upon approaching target, many ORN types exhibiting 'exploring branches' consisted of parallel microtubule-based terminal branches emanating from an F-actin-rich hub. Antennal nerve ablations uncovered essential roles for bilateral axons in contralateral target selection and for ORN axons to facilitate dendritic refinement of postsynaptic partner neurons. Altogether, these observations provide cellular bases for wiring specificity establishment (Li, 2021).

The Drosophila olfactory system has served as a model for investigating the mechanisms of neural circuit assembly. Axons of 50 types of olfactory receptor neurons (ORNs) and dendrites of 50 types of projection neurons (PNs) form 1-to-1 connections in 50 discrete, stereotyped, and individually identifiable glomeruli in the antennal lobe to relay olfactory information from the periphery to the brain. During the assembly of the adult olfactory system, PNs first extend dendrites to establish a coarse map. ORN axons then choose the dorsolateral or ventromedial trajectory to circumnavigate the antennal lobe, cross the midline, and invade the ipsi- and contralateral antennal lobes to find their synaptic partners. A multitude of cellular and molecular mechanisms have been identified that direct dendrite and axon targeting of selected PN and ORN types; some mechanisms first discovered in the fly olfactory system were subsequently found to be conserved in wiring the mammalian brain. Still, these studies are far from understanding the developmental algorithms that orchestrate the precise wiring of 50 pairs of ORN and PN types. Time-lapse imaging can provide cellular context in which wiring molecules exert their functions. Moreover, the ease of identifying neuron types based on their glomerular targets can determine the degree to which different neuron types use the same wiring rules (Li, 2021).

In this study, based on a previous protocol for studying the fly visual system in explant cultures, this study developed an antennae-brain explant preparation that recapitulates the precision by which the olfactory circuit is assembled in vivo. Time-lapse imaging of 30 ORN types revealed heterogenous axon targeting behaviors that contribute to the eventual wiring specificity. High-resolution adaptive optical lattice light-sheet microscopy (AO-LLSM) enabled discovery of an axon terminal structure prior to ORN axons reaching targets. This study also found that cytoskeletal organization of ORN axon terminals differs substantially from that of the classic growth cones from neurons in primary culture. Finally, ORN axon ablation uncovered essential roles for bilateral ORN axons in contralateral target selection, and for ORN axons to facilitate dendritic refinement of PNs (Li, 2021).

Prior to this study, it was unclear what cellular mechanism is used for ipsilateral target selection. The data support the following model: ORN axons send out transient interstitial branches at multiple locations along the main axon; the branch that reaches the target region becomes stabilized, and further interstitial branches are suppressed. Stabilization of appropriately positioned branches and elimination of ectopic branches are also used for topographic retinotopic targeting, suggesting that the mechanism of transient interstitial branching followed by stabilization applies to the formation of both continuous and discrete neural maps (Li, 2021).

The exploring branches that were discovered using AO-LLSM imaging suggest a means by which a growing ORN axon may increase the chance of identifying its target. These exploring branches consist of long, microtubule-based parallel branches that extend and retract rapidly and independently, allowing them to sample a relatively large region for possible targets. The transient occurrence of exploring branches when ORN axons approach their target region suggests that they are induced by local cues near target regions to facilitate target selection. In the ipsilateral antennal lobe, exploring branches were found in ORN types that form ipsilateral branches shortly after the main axon passes by, consistent with them serving as the precursor to the eventual ipsilateral branch. Exploring branches are also found in axon terminals in the contralateral antennal lobe in ORN types, suggesting a general role in facilitating contralateral target identification. For ORN types that have a long delay in extending the ipsilateral branch, no exploring branches were observed, suggesting a distinct mechanism for consolidating the ipsilateral branch. Nevertheless, dynamic interstitial branches occur over a prolong period of time until the formation of the ipsilateral branch, suggesting that these ORN types also use stabilization of transient interstitial branches as a means to consolidate the ipsilateral branch (Li, 2021).

In summary, after the initial trajectory choice such that ORN axons navigate in the half of the antennal lobe where their eventual targets are, it is proposed that the next critical step in ORN axon development is the stabilization of transient interstitial branches by target-derived cues, aided at least in part by the exploring branches. Together, these cellular mechanisms begin to explain how each ORN chooses one of 50 glomerular targets precisely (Li, 2021).

A surprising finding is that the cytoskeletal organization of ORN terminals differs substantially from that of classic growth cones, comprising F-actin-based filopodia and lamellipodia at the periphery and a microtubule-enriched central hub. The terminal branches of ORN axons, in particular the exploring branches, are filled with microtubules, whereas F-actin is concentrated at the central hub. Similar cytoskeletal organization was also found in photoreceptor axon terminals. These differences are unlikely due to species difference, as the classic growth cone cytoskeletal organization is found in neurons (mostly dissociated in culture) from Aplysia, Drosophila, and mammals. It cannot be ruled out that F-actin is present in low amount at the terminal of each exploring or post-innervation branch but is beyond the detection limit of the utrophin-based F-actin labeling; if so, each terminal branch would have its own growth cone at its tip, resembling classic growth cones. Even if that is the case, ORN axon terminals still differ from classic growth cones by having multiple microtubule-based parallel branches emanating from an F-actin rich central hub. Indeed, EB1-GFP puncta can be found at the tip of the branch, suggesting that microtubules can fill the entire branch. Microtubule polymerization has been shown to mediate membrane extension directly in lipid vesicle (Li, 2021).

It is suspected that the deviation of cytoskeletal organization in ORN axon terminals from the classic growth cone is likely due to the more complex environments axon terminals need to explore in the brain compared with the primary culture. Indeed, a recent study showed that neurons cultured in three-dimensional environments have microtubules extending to the edge of growth cones unconstrained by F-actin. The current findings have important implications for mechanisms that convert cell-surface recognition of extracellular cues into cytoskeletal-based structural changes in axon terminals during axon targeting. Specifically, we suggest that signaling to microtubule is particularly important at initial stages of target selection (Li, 2021).

Bilaterally symmetric organization of the nervous system is a cardinal feature of all bilaterians. Unilateral antennal nerve severing indicates the requirement of bilateral axons in target selection. The simplest cellular mechanism is direct interactions between ipsilateral and contralateral ORN axons. These interactions may facilitate midline crossing by creating a critical mass of midline-penetrating axons, disruption of which may cause some axons to leave the antennal lobe instead. Later, bilateral axon-axon interactions between the same ORN types may facilitate target selection of contralateral ORNs. The data do not rule out the possibility that bilateral interactions may be indirect; for example, ipsilateral ORNs may change the properties of their partners PNs, which in turn regulate target selection of contralateral ORNs. Indeed, upon unilateral antennal nerve severing, targeting defects was mostly found in ORN types that sequentially innervate ipsilateral and contralateral glomeruli. The ease of severing antennal nerve in explant cultures provides a means to further investigate cellular and molecular mechanisms of bilateral interactions (Li, 2021).

In conclusion, time-lapse imaging has greatly enriched understanding of the cellular events that enable the stepwise assembly of the fly olfactory circuit, and highlight the precise genetic control of multiple steps during ORN axon targeting. These include the choice of a trajectory along which an ORN axon navigates the ipsilateral antennal lobe, the timing and location of stabilizing its ipsilateral branch, and the interactions with contralateral ORN axons to cross the midline and innervate its contralateral target. Finally, ORN axons also help refine dendrites of their partner PNs, which pattern the antennal lobe first. The stage is set to combine live imaging and the cellular insights it has brought with genetic manipulations of key wiring molecules identified by genetic, transcriptomic, and proteomic approaches to reach a deeper level of mechanistic understanding of the circuit assembly process (Li, 2021).

Although the targeting precision in the explant culture mimics closely in vivo, it takes ORN axons longer to reach the same developmental stage in culture than in vivo. Thus, measurements involving time in explants may be protractions of equivalent events in vivo. The small number of single axons from specific ORN types, due to limited drivers that label specific ORN types strongly in early development, did not allow assessment of the variation of targeting behavior among ORNs of the same type. Although axon targeting of a large fraction of antennal ORN types was sampled, sample axons were not examined from 6 maxillary palp ORN types because the explant did not include maxillary palp. It is unclear whether maxillary palp ORN axons follow similar rules as antennal ORN axons. However, because maxillary palp ORN axons reach the antennal lobe substantially later than antennal ORN axons, the lack of maxillary palp ORN axons in explants should not affect the early stages of antennal ORN axon targeting (Li, 2021).

Consumption of food and water is tightly regulated by the nervous system to maintain internal nutrient homeostasis. Although generally considered independently, interactions between hunger and thirst drives are important to coordinate competing needs. In Drosophila, four neurons called the Interoceptive Subesophageal zone Neurons (ISNs) respond to intrinsic hunger and thirst signals to oppositely regulate sucrose and water ingestion. This study investigated the neural circuit downstream of the ISNs to examine how ingestion is regulated based on internal needs. Utilizing the recently available fly brain connectome, this study found that the ISNs synapse with a novel cell type Bilateral T-shaped neuron (BiT) that projects to neuroendocrine centers. In vivo neural manipulations revealed that BiT oppositely regulates sugar and water ingestion. Neuroendocrine cells downstream of ISNs include several peptide-releasing and peptide-sensing neurons, including insulin producing cells (IPC), crustacean cardioactive peptide (CCAP) neurons, and CCHamide-2 receptor isoform RA (CCHa2R-RA) neurons. These neurons contribute differentially to ingestion of sugar and water, with IPCs and CCAP neurons oppositely regulating sugar and water ingestion, and CCHa2R-RA neurons modulating only water ingestion. Thus, the decision to consume sugar or water occurs via regulation of a broad peptidergic network that integrates internal signals of nutritional state to generate nutrient-specific ingestion (Lez-Segarra, 2023).

Gustatory sensory neurons detect caloric and harmful compounds in potential food and convey this information to the brain to inform feeding decisions. To examine the signals that gustatory neurons transmit and receive, this study reconstructed gustatory axons and their synaptic sites in the adult Drosophila melanogaster brain, utilizing a whole-brain electron microscopy volume. 87 gustatory projections were reconstructed from the proboscis labellum in the right hemisphere and 57 from the left, representing the majority of labellar gustatory axons. Gustatory neurons contain a nearly equal number of interspersed pre- and postsynaptic sites, with extensive synaptic connectivity among gustatory axons. Morphology- and connectivity-based clustering revealed six distinct groups, likely representing neurons recognizing different taste modalities. The vast majority of synaptic connections are between neurons of the same group. This study resolves the anatomy of labellar gustatory projections, reveals that gustatory projections are segregated based on taste modality, and uncovers synaptic connections that may alter the transmission of gustatory signals (Engert, 2002).

All animals have specialized sensory neurons dedicated to the detection of the rich variety of chemicals in the environment that indicate the presence of food sources, predators, and conspecifics. Gustatory sensory neurons have evolved to detect food-associated chemicals and report the presence of caloric or potentially harmful compounds. Examining the activation and modulation of gustatory sensory neurons is essential as it places fundamental limits on the taste information that is funneled to the brain and integrated to form feeding decisions (Engert, 2002).

The Drosophila melanogaster gustatory system is an attractive model to examine the synaptic transmission of gustatory neurons. Molecular genetic approaches coupled with physiology and behavior have established five different classes of gustatory receptor neurons (GRNs) in adult Drosophila that detect different taste modalities. One class, expressing members of the gustatory receptor (GR) family, including Gr5a and Gr64f, detects sugars and elicits acceptance behavior. A second class expressing different GRs, including Gr66a, detects bitter compounds and mediates rejection behavior. A third class contains the ion channel Ppk28 and detects water. The fourth expresses the Ir94e ionotropic receptor, whereas the fifth contains the Ppk23 ion channel. These cells have been proposed to mediate detection of low-salt and high-salt concentrations, respectively. In addition to well-characterized gustatory neurons and a peripheral strategy for taste detection akin to mammals, the reduced number of neurons in the Drosophila nervous system and the availability of electron microscopy (EM) brain volumes offer the opportunity to examine gustatory transmission with high resolution (Engert, 2002).

The cell bodies of gustatory neurons are housed in sensilla on the body surface, including the proboscis labellum, an external mouthparts organ that detects taste compounds prior to ingestion. Gustatory neurons from each labellum half send bilaterally symmetric axonal projections to the subesophageal zone (SEZ) of the fly brain via the labial nerves. Gustatory axons terminate in the medial SEZ in a region called the anterior central sensory center (ACSC). Axons from bitter gustatory neurons send branches to the midline and form an interconnected medial ring, whereas other gustatory axons remain ipsilateral and anterolateral to bitter projections. Although projections of different gustatory classes have been mapped using light-level microscopy, the synaptic connectivity of gustatory axons in adult Drosophila is largely unexamined (Engert, 2002).

To explore the connectivity of GRNs and lay the groundwork to study gustatory circuits with synaptic resolution, the recently available Full Adult Fly Brain (FAFB) EM dataset to fully reconstruct gustatory axons and their synaptic sites. 87 GRN axonal projections were reconstructed in the right hemisphere and 57 in the left, representing between 83-96% and 54-63% of the total expected, respectively. By annotating chemical synapses, it was observed that GRNs contain a nearly equal number of interspersed pre- and postsynaptic sites. Interestingly, GRNs synapse onto and receive synaptic inputs from many other GRNs. Using morphology- and connectivity-based clustering, six distinct neural groups were identified, likely representing groups of GRNs that recognize different taste modalities. This study reveals extensive anatomical connectivity between GRNs within a taste modality, arguing for presynaptic processing of taste information prior to transmission to downstream circuits (Engert, 2002).

This study has characterized different classes of gustatory projections and their interconnectivity by high-resolution EM reconstruction. Different projection patterns corresponding to gustatory neurons were identified, recognizing different taste modalities. The extensive connections between GRNs of the same taste modality provide anatomical evidence of presynaptic processing of gustatory information (Engert, 2002).

An emerging theme stemming from EM reconstructions of Drosophila sensory systems is that sensory neurons of the same subclass are synaptically connected. In general, different sensory neuron subclasses have spatially segregated axonal termini in the brain, thereby constraining the potential for connectivity. In the adult olfactory system, approximately 40% of the input onto olfactory receptor neurons (ORNs) comes from other ORNs projecting to the same olfactory glomerulus. Similarly, mechanosensory projections from Johnston's organ of the same submodality are anatomically segregated and synaptically connected. In Drosophila larvae, 25% of gustatory neuron inputs are from other GRNs, although functional classes were not resolved. In the adult Drosophila gustatory system, GRNs are interconnected, with approximately 39% of GRN input coming from other GRNs. Consistent with other classes of sensory projections, this study found that gustatory projections are largely segregated based on taste modality and form connected groups. A general function of sensory-sensory connections seen across sensory modalities may be to enhance weak signals or increase dynamic range (Engert, 2002).

By clustering neurons based on anatomy and connectivity, it was possible to resolve different GRN categories. The distinct morphologies of bitter neurons and candidate low-salt-sensing neurons, known from immunohistochemistry, are recapitulated in the projection patterns of GRN groups 1-3 of the right hemisphere, enabling high-confidence identification. The projections of high-salt-, sugar-, and water-sensing neurons are ipsilateral, with similarities in their terminal arborizations. Nevertheless, comparisons between EM and light-level projections argue that these taste categories are also resolved into different, identifiable clusters. GRN categories were identified as low salt (Ir94e) and high salt (the remaining category) based on previous studies but note that the full complement of tastes that these GRNs detect requires additional investigation. The GRN categories that were identified in this study are based on anatomical comparisons alone and remain tentative until further examination of taste response profiles of connected second-order neurons, which may now be identified by examining connectivity downstream of GRNs (Engert, 2002).

This study reconstructed 83-96% of the GRNs on the right hemisphere and 54-63% on the left, based on total GRN counts from previous studies. GRN categories may be further refined upon reconstruction of the entire GRN population or upon analysis that includes postsynaptic partners. In addition, GRNs are found at different locations on the proboscis labellum and are housed in three taste bristle types. Segregation based on labellar location or bristle type may further divide the GRN categories described in this study. Interestingly, in the clustering analysis, it was found that bitter projections cluster into two distinct groups. It is hypothesized that these different subsets are comprised of bitter GRNs from different taste bristle classes or bitter GRNs with different response properties (Engert, 2002).

Examining GRN-GRN connectivity revealed connectivity between GRNs of the same group as well as different groups. While it is tempting to speculate that interactions between different taste modalities may amplify or filter activation of feeding circuits, this study was unable to identify cross-activation between sugar and water GRNs by calcium or voltage imaging. It is possible that these interactions are dependent on a feeding state or act on a time frame not examined in this study. Alternatively, activation may fall below the detection threshold of calcium or voltage imaging. Additionally, far fewer synapses occur between anatomical classes than within classes, especially restricting analyses to neurons connected by five or more synapses, suggesting that the few synapses may not be relevant for taste processing. Finally, the anatomy and connectivity-based clustering may not categorize all individual GRNs correctly, and misclassification of GRNs would impact connectivity analyses. Regardless, these studies suggest that presynaptic connectivity between different GRN classes does not substantially contribute to taste processing (Engert, 2002).

Overall, this study resolves the majority of labellar gustatory projections and their synaptic connections, revealing that gustatory projections are segregated based on taste modality and sensory-sensory connectivity. The identification of GRNs detecting different taste modalities now provides an inroad to enable the examination of the downstream circuits that integrate taste information and guide feeding decisions (Engert, 2002).

Gustatory sensory neurons detect caloric and harmful compounds in potential food and convey this information to the brain to inform feeding decisions. To examine the signals that gustatory neurons transmit and receive, this study reconstructed gustatory axons and their synaptic sites in the adult Drosophila melanogaster brain, utilizing a whole-brain electron microscopy volume. 87 gustatory projections were reconstructed from the proboscis labellum in the right hemisphere and 57 from the left, representing the majority of labellar gustatory axons. Gustatory neurons contain a nearly equal number of interspersed pre- and postsynaptic sites, with extensive synaptic connectivity among gustatory axons. Morphology- and connectivity-based clustering revealed six distinct groups, likely representing neurons recognizing different taste modalities. The vast majority of synaptic connections are between neurons of the same group. This study resolves the anatomy of labellar gustatory projections, reveals that gustatory projections are segregated based on taste modality, and uncovers synaptic connections that may alter the transmission of gustatory signals (Engert, 2002).

All animals have specialized sensory neurons dedicated to the detection of the rich variety of chemicals in the environment that indicate the presence of food sources, predators, and conspecifics. Gustatory sensory neurons have evolved to detect food-associated chemicals and report the presence of caloric or potentially harmful compounds. Examining the activation and modulation of gustatory sensory neurons is essential as it places fundamental limits on the taste information that is funneled to the brain and integrated to form feeding decisions (Engert, 2002).

The Drosophila melanogaster gustatory system is an attractive model to examine the synaptic transmission of gustatory neurons. Molecular genetic approaches coupled with physiology and behavior have established five different classes of gustatory receptor neurons (GRNs) in adult Drosophila that detect different taste modalities. One class, expressing members of the gustatory receptor (GR) family, including Gr5a and Gr64f, detects sugars and elicits acceptance behavior. A second class expressing different GRs, including Gr66a, detects bitter compounds and mediates rejection behavior. A third class contains the ion channel Ppk28 and detects water. The fourth expresses the Ir94e ionotropic receptor, whereas the fifth contains the Ppk23 ion channel. These cells have been proposed to mediate detection of low-salt and high-salt concentrations, respectively. In addition to well-characterized gustatory neurons and a peripheral strategy for taste detection akin to mammals, the reduced number of neurons in the Drosophila nervous system and the availability of electron microscopy (EM) brain volumes offer the opportunity to examine gustatory transmission with high resolution (Engert, 2002).

The cell bodies of gustatory neurons are housed in sensilla on the body surface, including the proboscis labellum, an external mouthparts organ that detects taste compounds prior to ingestion. Gustatory neurons from each labellum half send bilaterally symmetric axonal projections to the subesophageal zone (SEZ) of the fly brain via the labial nerves. Gustatory axons terminate in the medial SEZ in a region called the anterior central sensory center (ACSC). Axons from bitter gustatory neurons send branches to the midline and form an interconnected medial ring, whereas other gustatory axons remain ipsilateral and anterolateral to bitter projections. Although projections of different gustatory classes have been mapped using light-level microscopy, the synaptic connectivity of gustatory axons in adult Drosophila is largely unexamined (Engert, 2002).

To explore the connectivity of GRNs and lay the groundwork to study gustatory circuits with synaptic resolution, the recently available Full Adult Fly Brain (FAFB) EM dataset to fully reconstruct gustatory axons and their synaptic sites. 87 GRN axonal projections were reconstructed in the right hemisphere and 57 in the left, representing between 83-96% and 54-63% of the total expected, respectively. By annotating chemical synapses, it was observed that GRNs contain a nearly equal number of interspersed pre- and postsynaptic sites. Interestingly, GRNs synapse onto and receive synaptic inputs from many other GRNs. Using morphology- and connectivity-based clustering, six distinct neural groups were identified, likely representing groups of GRNs that recognize different taste modalities. This study reveals extensive anatomical connectivity between GRNs within a taste modality, arguing for presynaptic processing of taste information prior to transmission to downstream circuits (Engert, 2002).

This study has characterized different classes of gustatory projections and their interconnectivity by high-resolution EM reconstruction. Different projection patterns corresponding to gustatory neurons were identified, recognizing different taste modalities. The extensive connections between GRNs of the same taste modality provide anatomical evidence of presynaptic processing of gustatory information (Engert, 2002).

An emerging theme stemming from EM reconstructions of Drosophila sensory systems is that sensory neurons of the same subclass are synaptically connected. In general, different sensory neuron subclasses have spatially segregated axonal termini in the brain, thereby constraining the potential for connectivity. In the adult olfactory system, approximately 40% of the input onto olfactory receptor neurons (ORNs) comes from other ORNs projecting to the same olfactory glomerulus. Similarly, mechanosensory projections from Johnston's organ of the same submodality are anatomically segregated and synaptically connected. In Drosophila larvae, 25% of gustatory neuron inputs are from other GRNs, although functional classes were not resolved. In the adult Drosophila gustatory system, GRNs are interconnected, with approximately 39% of GRN input coming from other GRNs. Consistent with other classes of sensory projections, this study found that gustatory projections are largely segregated based on taste modality and form connected groups. A general function of sensory-sensory connections seen across sensory modalities may be to enhance weak signals or increase dynamic range (Engert, 2002).

By clustering neurons based on anatomy and connectivity, it was possible to resolve different GRN categories. The distinct morphologies of bitter neurons and candidate low-salt-sensing neurons, known from immunohistochemistry, are recapitulated in the projection patterns of GRN groups 1-3 of the right hemisphere, enabling high-confidence identification. The projections of high-salt-, sugar-, and water-sensing neurons are ipsilateral, with similarities in their terminal arborizations. Nevertheless, comparisons between EM and light-level projections argue that these taste categories are also resolved into different, identifiable clusters. GRN categories were identified as low salt (Ir94e) and high salt (the remaining category) based on previous studies but note that the full complement of tastes that these GRNs detect requires additional investigation. The GRN categories that were identified in this study are based on anatomical comparisons alone and remain tentative until further examination of taste response profiles of connected second-order neurons, which may now be identified by examining connectivity downstream of GRNs (Engert, 2002).

This study reconstructed 83-96% of the GRNs on the right hemisphere and 54-63% on the left, based on total GRN counts from previous studies. GRN categories may be further refined upon reconstruction of the entire GRN population or upon analysis that includes postsynaptic partners. In addition, GRNs are found at different locations on the proboscis labellum and are housed in three taste bristle types. Segregation based on labellar location or bristle type may further divide the GRN categories described in this study. Interestingly, in the clustering analysis, it was found that bitter projections cluster into two distinct groups. It is hypothesized that these different subsets are comprised of bitter GRNs from different taste bristle classes or bitter GRNs with different response properties (Engert, 2002).

Examining GRN-GRN connectivity revealed connectivity between GRNs of the same group as well as different groups. While it is tempting to speculate that interactions between different taste modalities may amplify or filter activation of feeding circuits, this study was unable to identify cross-activation between sugar and water GRNs by calcium or voltage imaging. It is possible that these interactions are dependent on a feeding state or act on a time frame not examined in this study. Alternatively, activation may fall below the detection threshold of calcium or voltage imaging. Additionally, far fewer synapses occur between anatomical classes than within classes, especially restricting analyses to neurons connected by five or more synapses, suggesting that the few synapses may not be relevant for taste processing. Finally, the anatomy and connectivity-based clustering may not categorize all individual GRNs correctly, and misclassification of GRNs would impact connectivity analyses. Regardless, these studies suggest that presynaptic connectivity between different GRN classes does not substantially contribute to taste processing (Engert, 2002).

Overall, this study resolves the majority of labellar gustatory projections and their synaptic connections, revealing that gustatory projections are segregated based on taste modality and sensory-sensory connectivity. The identification of GRNs detecting different taste modalities now provides an inroad to enable the examination of the downstream circuits that integrate taste information and guide feeding decisions (Engert, 2002).

Sweet and bitter compounds excite different sensory cells and drive opposing behaviors. However, it remains unclear how sweet and bitter tastes are represented by the neural circuits linking sensation to behavior. To investigate this question in Drosophila, this study devised trans-Tango(activity), a strategy for calcium imaging of second-order gustatory projection neurons based on trans-Tango, a genetic transsynaptic tracing technique. Spatial overlap was found between the projection neuron populations activated by sweet and bitter tastants. The spatial representation of bitter tastants in the projection neurons was consistent, while that of sweet tastants was heterogeneous. Furthermore, it wads discovered that bitter tastants evoke responses in the gustatory receptor neurons and projection neurons upon both stimulus onset and offset and that bitter offset and sweet onset excite overlapping second-order projections. These findings demonstrate an unexpected complexity in the representation of sweet and bitter tastants by second-order neurons of the gustatory circuit (Snell, 2022).

In flies, neuronal sensors detect prandial changes in circulating fructose levels and either sustain or terminate feeding, depending on internal state. This study describes a three-part neural circuit that imparts satiety-dependent modulation of fructose sensing. Dorsal fan-shaped body neurons display oscillatory calcium activity when hemolymph glucose is high, and these oscillations require glutamatergic input from SLP-AB or 'Janus' neurons projecting from the protocerebrum to the asymmetric body. Suppression of activity in this circuit, either by starvation or by genetic silencing, promotes specific drive for fructose ingestion. This is achieved through neuropeptidergic signaling by tachykinin, which is released from the fan-shaped body when glycemia is high. Tachykinin, in turn, signals to Gr43a-positive fructose sensors to modulate their response to fructose. Together, these results demonstrate how a three-layer neural circuit links the detection of two sugars to produce precise satiety-dependent control of feeding behavior (Musso, 2021).

Regulation of energy intake is a complex process involving food search, an animal's internal state, and the sensory qualities of food. In flies, fructose, either consumed directly or rapidly metabolized from precursors, promotes feeding through activation of a brain fructose sensor called Gr43a. This study describes how a neuronal network composed of neurons in the FB and asymmetric body contributes to energy homeostasis by detecting satiety-dependent changes in hemolymph glucose and modulating fructose drive (Musso, 2021).

The central complex, which is composed of the FB, the protocerebral bridge (PB), the ellipsoid body, and the noduli, is regarded as a center for sensorimotor integration that functions in goal-directed behavior. The FB is organized in nine horizontal layers and nine vertical columns. FB large field neurons of layers 1 to 3, and inputs to these layers from the PB, encode flight direction and general sensory orientation. FB layers 6 and 7 are well known to regulate sleep and arousal, locomotor control, courtship, visual memory, and decision-making related to taste. Layer 6 also plays a role in avoiding conditioned odors, while layers 1, 2, 4, and 5 respond to electric stimuli and are required for innate odor avoidance. However, the function of the most dorsal FB layers (8 and 9), mostly innervated local tangential neurons and AB-FBl8 (or vΔA_a), remained poorly understood. The results demonstrate a role for these layers in feeding regulation (Musso, 2021).

dFB oscillations were found to be require glutamatergic input from Janus neuron projections to the asymmetric body. Described for the first time in 2004, very little is known about AB function; 92.4% of flies display asymmetry in the AB, with the body present only in the right hemisphere, while 7.6% also have a body on the left side. It is noted that oscillations in the dFB display a tendency to be faster on the right side, with clearly asynchronous activity between the two sides that may reflect their asymmetric input from Janus neurons. The small proportion of flies displaying symmetry in the AB have defects in LTM, a process that is known to require energy. It is speculated that these symmetric flies may have a dysfunctional Janus neurons-to-dFB connection, resulting in impaired Tk release. This could affect LTM either directly or through changes in feeding. A role for TK in memory has been demonstrated in honeybees and mammals, and TkR86C appears to be expressed in serotonergic paired neurons known to interact with MB-MP1 neurons required for LTM formation. Tk also acts through TkR99D to modulate activity in neurons producing insulin-like peptides, which affect LTM formation (Musso, 2021).

Modulation of dFB oscillations by Janus neurons requires glutamatergic signaling through a group of glutamate receptors including KaiR1D, NmdaR1, NmdaR2, and GluClα, but not AMPA receptors. Both KaiR1D receptors, which are homomeric, and N-methyl-D-aspartate (NMDA) receptors, which are heteromeric complexes between subunits 1 and 2, pass Ca2+ current. NMDA receptors (NMDAR) are well known for their role in mediating synaptic plasticity and can also trigger oscillatory activity. NMDAR function as molecular coincidence detectors, requiring simultaneous ligand binding and membrane depolarization for activation. It is possible that dFB neuron oscillations are triggered by the coincident detection of glutamate from Janus neurons and glucose from the hemolymph; however, because the FB are receiving many inputs from other brain region, it is suspect that dFB oscillations require additional inputs as well. The chloride channel GluClα is also required for dFB oscillations. GluClα has been previously implicated in on/off responses of the visual system of flies and memory retention in honeybees, demonstrating a role in regulating cell excitability. Perhaps, GluClα functions in repolarization of the dFB neurons between calcium bursts. Further study will be required to fully understand how the suite of glutamate receptors function together to drive oscillations, along with the source of input to Janus neurons in the protocerebrum (Musso, 2021).

Because glucose is the primary circulating energy source, one might intuitively expect that enhancing feeding in response to postingestive glucose detection would be the most efficient means of optimizing energy uptake. However, using elevation of hemolymph glucose as a signal to continue feeding is problematic because glucose levels are tightly regulated and elevated glucose serves as a signal of satiety. On the other hand, internal fructose can vary widely in response to ingestion and can therefore be a more reliable indicator of recent sugar intake. Thus, the separation of glucose as a satiety indicator and fructose as marker of sugar consumption removes the potential ambiguity of each as a signal. Moreover, fructose typically coexists with other nutritive sugars in common food sources. Therefore, it may not be the case that flies specifically benefit from fructose intake but rather that fructose serves as an effective proxy for general carbohydrate ingestion. By using fructose and the narrowly tuned Gr43a fructose receptor to survey sugar consumption, flies can effectively benefit from both a fructose-mediated positive feedback loop and glucose-mediated negative feedback to co-operatively ensure appropriate energy intake (Musso, 2021).

The finding that dFB glucose sensing modulates fructose feeding via Gr43a brain neurons fits with the established model of Gr43a brain neurons as central fructose sensors. For this mechanism to effectively sustain feeding on a rich sugar source, ingested sugars must rapidly increase fructose signaling to Gr43a brain neurons, which then must acutely promote feeding. While the precise kinetics of internal fructose elevation after sugar consumption have not been quantified, fructose levels in the head rapidly increase 10-fold after fructose feeding and then return to baseline. The role of direct fructose sensing by Gr43a brain neurons is highlighted by the observation that Gr43a knockdown in those neurons results in markedly lower relative intake of fructose compared to glucose. Unexpectedly, knockdown of Gr64a, another sugar receptor expressed in the same neurons, produced the opposite effect. This could be because Gr64a contributes to modulation of Gr43a brain neurons by other sugar cues, and the absence of this activity makes Gr43a-mediated fructose responses more pronounced. Alternatively, Gr43a may be expressed more strongly after Gr64a knockdown, leading to an increased fructose response (Musso, 2021).

Little is known about the mechanisms downstream of Gr43a brain neurons that promote feeding. All Gr43a brain neurons express the peptide Crz, but knockdown of Crz expression produced no significant effect on fructose preference over glucose. This suggests an important functional role for another neurotransmitter, although it is also possible that the RNAi knockdown was not effective. Irrespective of mechanism, two experiments support the idea that activation of Gr43a neurons acutely enhances feeding. First, silencing of dFB neurons by genetic manipulation or prolonged starvation produces Gr43a-dependent fructose preference within the first 10 min of a flyPAD assay. Second, closed-loop optogenetic activation of Gr43a brain neurons was sufficient to produce a strong positive preference within 10 min in the STROBE (Musso, 2021).

The separable functions of glucose and fructose sensing in flies bear notable resemblance to the differential effects of these two sugars in the mammalian hypothalamus. In particular, AMPK expression in the arcuate nucleus of the hypothalamus is known to link energy levels to food drive. When glycemia is low, AMPK is activated and thereby promotes feeding through orexigenic AgRP/NPY neuron activity. Glucose administration suppresses activity in these peptidergic neurons, while fructose can have the opposite effect and promote further feeding. The first description of fly Gr43a neurons noted their orexinegenic activity and suggested a potential functional homology with the hypothalamus. In the present study, a multilayered neural system centered on a brain energy sensor (dFB), was uncovered whose activation by glucose leads to anorexigenic behavior through inhibition of the brain fructose sensor Gr43a. Thus, the results are consistent with at least partial functional homology between the mammalian hypothalamus and brain Gr43a neurons of the fly (Musso, 2021).

Habituated animals retain a latent capacity for robust engagement with familiar stimuli. In most instances, the ability to override habituation is best explained by postulating that habituation arises from the potentiation of inhibitory inputs onto stimulus-encoding assemblies and that habituation override occurs through disinhibition. Previous work has shown that inhibitory plasticity contributes to specific forms of olfactory and gustatory habituation in Drosophila. This study analyzed how exposure to a novel stimulus causes override of gustatory (proboscis extension reflex; PER) habituation. While brief sucrose contact with tarsal hairs causes naive Drosophila to extend their proboscis, persistent exposure reduces PER to subsequent sucrose stimuli. This study shows that in so habituated animals, either brief exposure of the proboscis to yeast or direct thermogenetic activation of sensory neurons restores PER response to tarsal sucrose stimulation. Similar override of PER habituation can also be induced by brief thermogenetic activation of a population of tyrosine hydroxylase (TH)-positive neurons, a subset of which send projections to the subesophageal zone (SEZ). Significantly, sensory-neuron induced habituation override requires transmitter release from these TH-positive cells. Treatments that cause override specifically influence the habituated state, with no effect on the naive sucrose response across a range of concentrations. Taken together with other findings, these observations in female flies are consistent with a model in which novel taste stimuli trigger activity in dopaminergic neurons which, directly or indirectly, inhibit GABAergic cells that drive PER habituation. The implications of these findings for general mechanisms of attentional and sensory override of habituation are discussed (Trisal, 2022).

Swallowing is an essential step of eating and drinking. However, how the quality of a food bolus is sensed by pharyngeal neurons is largely unknown. This study finds that mechanical receptors along the Drosophila pharynx are required for control of meal size, especially for food of high viscosity. The mechanical force exerted by the bolus passing across the pharynx is detected by neurons expressing the mechanotransduction channel NOMPC (no mechanoreceptor potential C) and is relayed, together with gustatory information, to IN1 neurons in the subesophageal zone (SEZ) of the brain. IN1 (ingestion neurons) neurons act directly upstream of a group of peptidergic neurons that encode satiety. Prolonged activation of IN1 neurons suppresses feeding. IN1 neurons receive inhibition from DSOG1 (descending subesophageal neurons) neurons, a group of GABAergic neurons that non-selectively suppress feeding. These results reveal the function of pharyngeal mechanoreceptors and their downstream neural circuits in the control of food ingestion (Yang, 2021).

Overconsumption is harmful for animals. Although the drive to ingest can be overwhelming for a hungry animal in the initial stage of a meal, inhibition becomes more dominant with the processes of food intake. This study found that food flowing across the pharynx accumulates the satiety state in the brain, demonstrating that multiple strategies are used by the nervous systems to avoid overeating. These pharyngeal sensory neurons are sensitive to sugar and mechanical force, serving as a flow meter that monitors food quality and amount so that the brain knows how much food is ingested even before the food reaches the intestine. This circuit may coordinate with other satiety signals, such as those conveyed by mechanical feedback from the intestine, to control feeding (Yang, 2021).

Gustatory and mechanosensory neurons are well separated on the fly labellum before their axons reach the SEZ, where they interact with each other to regulate the perception of food quality. In contrast, the sensory neurons in the pharynx seem to adapt a different coding mechanism. Some of the pharyngeal neurons are polymodal because they respond to chemical and mechanical stimuli, with PM neurons being an example. A 'generalist' versus 'specialist strategy has been found in other sensory organs too. Being able to evaluate multiple properties of a bolus in the pharynx allow the animals to effectively control the feeding amount. There are sensory neurons in the pharynx that may be tuned to gustatory or mechanosensory cues. For example, the R41E11-GAL4 and nompC-QF labeled approximately 10 pairs of neurons in LSOs along the pharynx, similar to the number observed for mechanosensory neurons. Most of those neurons are likely 'generalist' and are tuned to mechanical stimuli only. It would be valuable to determine the full repertoire of these sensory neurons to understand how the swallowing maneuver is initiated, sustained, and terminated (Yang, 2021).

It has been proposed that IN1 neurons may function as a key node of the feeding control circuits to govern rapid feeding decisions. Previous studies have revealed that IN1 neurons are directly downstream of pharyngeal GRNs and that activation of IN1 neurons to sugar stimulation is correlated with a fly's motivation to feed. Because activation of IN1 neurons triggers proboscis extension to food, they are likely upstream of the motor circuit that controls feeding. IN1 neurons thus appear to function as a hub that integrates sensory information to initiate food ingestion. This study found that IN1 neurons' activity is under control of the fly's feeding states. IN1 neurons are directly downstream of DSOG1 neurons that non-selectively suppress ingestion. In fed flies, DSOG1 neurons impart inhibition on IN1 neurons, resulting in a transient and moderate response to a sugar sip that triggers a robust and sustained calcium response in fasted flies (Yang, 2021).

It has been proposed that DSOG1 neurons impart constant inhibition on the neuronal circuits that initiate food ingestion. However, the upstream circuit of DSOG1 neurons has not been identified. A cohort of neuropeptide receptor genes has been screened, but none of them seemed to function on DSOG1 neurons in feeding control. This study found that interrupting signaling of the neuropeptide MIP phenocopied overfeeding in flies with silenced DSOG1 neurons. It is tantalizing to hypothesize that MIP neurons are upstream of the DSOG1 circuit, either directly or indirectly. Because the receptors of MIP have not yet been identified, further experiments are need to differentiate between the two possibilities (Yang, 2021).

Besides PM neurons, there are many NOMPC-expressing mechanosensory neurons along the fly pharynx. Because of the lack of specific driver lines and the technique to record a single neuron's activity during feeding, their roles in feeding regulation are interesting open questions and await in-depth investigation. Moreover, the receptors of MIP peptide have not been identified, especially the ones involved in feeding regulation, making it difficult to establish a connection between MIP neurons and DSOG1 neurons (Yang, 2021).

Neural circuits carry out complex computations that allow animals to evaluate food, select mates, move toward attractive stimuli, and move away from threats. In insects, the subesophageal zone (SEZ) is a brain region that receives gustatory, pheromonal, and mechanosensory inputs and contributes to the control of diverse behaviors, including feeding, grooming, and locomotion. Despite its importance in sensorimotor transformations, the study of SEZ circuits has been hindered by limited knowledge of the underlying diversity of SEZ neurons. This study generate a collection of split-GAL4 lines that provides precise genetic targeting of 138 different SEZ cell types in adult D. melanogaster, comprising approximately one third of all SEZ neurons. The single cell anatomy of these neurons was characterized, and they were found to cluster by morphology into six supergroups that organize the SEZ into discrete anatomical domains. The majority of local SEZ interneurons are not classically polarized, suggesting rich local processing, whereas SEZ projection neurons tend to be classically polarized, conveying information to a limited number of higher brain regions. This study provides insight into the anatomical organization of the SEZ and generates resources that will facilitate further study of SEZ neurons and their contributions to sensory processing and behavior (Sterne, 2021).

This study describes the SEZ Split-GAL4 Collection, a library of 277 split-GAL4 lines covering 138 SEZ cell types, which affords unprecedented genetic access to SEZ neurons for behavioral and functional study. These studies provide insight into the diversity of SEZ cell types and their organization into discrete anatomical domains. The SEZ Split-GAL4 Collection will enable further investigation of how local SEZ circuitry and ascending SEZ paths process sensory inputs and control specific behaviors (Sterne, 2021).

Most of the SEZ Split-GAL4 lines are specific, with 149/277 lines classified as ideal or excellent. These lines will be useful to manipulate individual SEZ cell types for behavioral, functional, and imaging experiments. The remaining, less specific, lines (those belonging to the good or poor categories) will still be useful for imaging and as starting points for creating more specific reagents. Good and poor lines may be used to generate CDM masks to search for new hemidrivers to make further split-GAL4 lines. Alternatively, their expression patterns may be refined using Killer Zipper or three-way intersections with LexA or QF lines. All lines in the SEZ Split-GAL4 Collection may be used to generate further tools including complementary split-LexA and split-QF reagents. Split-LexA and split-QF lines may be used in concert with the split-GAL4 lines reported here to simultaneously manipulate two independent neuronal populations for advanced intersectional experiments, including behavioral epistasis (Sterne, 2021).

By combining insights from a single-cell transcriptome atlas with direct cell counts of SEZ neuromeres, it is estimated that the SEZ Split-GAL4 Collection labels 30% of the ~1700 neurons in the SEZ. Because of the lack of stereotyped neuronal cell body positions in D. melanogaster, it is not possible to assign cell bodies to defined neuropil regions without a genetic marker. The advantage of this method of estimating SEZ neuron number is that it is based on analysis of the four genetically defined SEZ neuromeres, the tritocerebral, the mandibular, the maxillary, and the labial neuromeres. However, previous reports demonstrate that some deutocerebral commissures cross below the esophageal foramen, and therefore an unknown number of deutocerebral cell bodies may be part of the SEZ. The limitations of this estimate of SEZ neuron number therefore include the inability to directly count cells derived from the tritocerebral neuromere, the inability to directly count neurons rather than glia, and the inability to assess deutocerebral contributions. Thus, the estimate of SEZ cell number is likely an underestimate. Once all SEZ neurons are densely reconstructed in an EM volume, direct counts of SEZ neuronal cell bodies obtained by EM will provide a more accurate assessment of SEZ neuron number. Regardless, the SEZ Split-GAL4 Collection targets 510 neuronal cell bodies, which represents a substantial improvement in the ability to precisely target SEZ cell types for functional and morphological analysis. This study did not ascertain the neuromere or neuroblast of origin of the SEZ cell types in the SEZ Split-GAL4 Collection. However, recent work has established reliable anatomical criteria that define the boundaries between the four SEZ neuromeres and has mapped all secondary lineages of the SEZ. Future efforts should focus on bridging previously identified fascicle, neuropil, and sensory domains into a common template or coordinate space to determine the neuromere and neuroblast origin of SEZ cell types (Sterne, 2021).

Discovering and genetically targeting SEZ cell types required the use of registered light-level imagery and computer-assisted searching. Four distinct strategies were used to identify 129 novel and 9 previously reported SEZ cell types in registered light-level imagery. Critically, each of these strategies allowed use of CDM mask searching to identify additional hemidrivers with which to target each cell type of interest. CDM mask searching enabled combing of large datasets and greatly increased the ease and speed of split-GAL4 generation over previous methods. The same strategies can be leveraged to gain genetic access to yet-undiscovered SEZ cell types. The recent electron microscopy (EM) volumes of the D. melanogaster brain provide an avenue for identifying SEZ cell types that are not covered by the SEZ Split-GAL4 Collection. Notably, this approach awaits comprehensive reconstruction of the SEZ, a region that is not included in the recently published dense reconstruction of the 'hemibrain' volume. Another EM volume, 'FAFB,' provides imagery of an entire adult female fly brain at synaptic resolution and includes the SEZ. Improvements in automated reconstruction of EM volumes coupled with large-scale human annotation should soon provide exhaustive reconstruction of the SEZ from which to identify additional SEZ cell types. Furthermore, available bridging registrations between EM volumes and light-level imagery should facilitate the identification of hemidrivers to target SEZ cell types discovered from EM reconstructions. Even without identifying additional SEZ cell types, the split-GAL4 reagents described will allow behavioral and functional evaluation of circuit hypotheses derived from EM imagery (Sterne, 2021).

These analyses of the SEZ Split-GAL4 Collection provide insight into the cellular architecture of the SEZ. To computationally probe the organization of the SEZ, 121 SEZ cell types were morphologically clustered using NBLAST. This approach reveals six cellular domains in the SEZ that are organized in a largely layered fashion from anterior to posterior. This layered structure is also hinted at by the recent description of SEZ neuropil domains throughout development from the larva to the adult. Based on anatomical position and the known function of a few SEZ neurons, it is tempting to speculate that different morphological clusters may participate in different behavioral functions. Group 1 contains projection neurons that innervate the region of the SMP surrounding the pars intercerebralis (PI), suggesting that group 1 neurons may impinge on neurosecretory neurons or function in energy and fluid homeostasis circuits. The proximity of group 1 interneurons to previously described interoceptive SEZ neurons (ISNs) and ingestion neurons (IN1) supports this hypothesis. Group 2 contains Fdg, a feeding-related neuron, as well as cell types (indigo, tinctoria) that are located near pumping motor neurons, suggesting that group 2 neurons have roles in feeding sequence generation. Group 3 contains G2N-1, a candidate second-order gustatory neuron, and projection neurons that innervate recently described taste-responsive SLP regions, suggesting that group 3 may, in part, be composed of taste-responsive neurons. Many interneurons in group 4 are located near proboscis motor neurons that control rostrum protraction, haustellum extension, and labellar spreading, indicating that group 4 members function in proboscis motor control. The proximity of neurons in group 5 to previously described stopping neuron MAN, and the inclusion of an antennal grooming neuron, suggests that group 5 neurons may participate in circuits that control grooming and stopping behaviors. Group 6 is located in the posterior SEZ and posterior slope, regions implicated in flight behaviors, including wing and neck control. While potential behavioral functions are hypothesized for each supergroup, it is readily acknowledged that the roles of the neurons described in this study are likely more diverse (Sterne, 2021).

These studies also shed light on information flow both within the SEZ and out of the SEZ to the higher brain. 91 local interneurons, 30 projection neurons, 16 descending neurons, and 1 sensory neuron were identified. Polarity analysis of 121/138 of the SEZ cell types covered by the SEZ Split-GAL4 Collection revealed that SEZ interneurons tend to have mixed or biased polarity while SEZ projection neurons tend to be classically polarized. Polarity analyses of the lateral horn, mushroom body, descending neurons, and protocerebral bridge identified few neurons with completely mixed polarity. Unlike these brain regions, the SEZ contains a large number of local interneurons. The mixed polarity of the SEZ interneurons argues for local and reciprocal connectivity between neurons, with information flowing in networks rather than unidirectional streams. Projection neurons, in contrast, may serve chiefly to pass information from highly interconnected SEZ circuits to other brain regions in a unidirectional manner. Notably, many SEZ projection neurons were identified that innervate the SMP-a region known to contain neurosecretory cell types. This may betray a role for acute taste detection or feeding circuit activation in the regulation of hormone secretion. In addition, the frequent innervation of the superior lateral protocerebrum and lateral horn by SEZ projection neurons may hint at the site of olfactory-gustatory synthesis. In contrast, this study did not identify projection neurons that link the SEZ directly to the central complex or mushroom body. If dense reconstruction of EM volumes corroborates the lack of direct connectivity between the SEZ and these regions, information must be conveyed through indirect pathways. As an example, taste information influences local search behaviors during foraging, a task that is expected to involve the central complex. Indirect relay of taste information to the central complex to inform foraging behavior would be consistent with previous anatomical studies suggesting that the central complex receives diverse indirect sensory inputs. Furthermore, the mushroom body is known to respond to taste, raising the possibility that taste information from gustatory sensory neuron axons in the SEZ must be relayed through yet another brain region before reaching mushroom body cell types. Thus, this analysis of SEZ neuron polarity indicates local SEZ processing and demonstrates direct pathways to a subset of higher brain regions (Sterne, 2021).

Overall, the SEZ Split-GAL4 Collection represents a valuable resource that will facilitate the study of the SEZ. This analysis of the collection reveals the cellular anatomy and polarity of individual SEZ neurons and their organization into six discrete domains in the SEZ. Coupled with emerging insights from reconstruction of EM volumes, the SEZ Split-GAL4 Collection will allow the use of genetic dissection to test circuit-level hypotheses about sensory processing and motor control in the SEZ (Sterne, 2021).

Pavlovian conditioning is a broadly used learning paradigm where defined stimuli are associated to induce behavioral switching. To define a causal relationship between activity change in a single neuron and behavioral switching, this study took advantage of a 'command neuron' that connects cellular function to behavior. To examine the cellular and molecular basis of Pavlovian conditioning, previous work identified a pair of feeding command neurons termed 'feeding neurons' in the adult Drosophila brain using genetic screening and opto- and thermo-genetic techniques. The feeding neuron is activated by sweet signals like sucrose and induces the full complement of feeding behaviors, such as proboscis extension and food pumping. Ablation or inactivation of the pair of feeding neurons abolishes feeding behavior, suggesting that this single pair of neurons is indispensable for natural feeding behaviors. This study describes a novel conditioning protocol to associate a signal-mediating rod removal from legs (conditioned stimulus [CS]) to feeding behavior induced by sucrose stimulation (unconditioned stimulus [US]). Calcium imaging of the feeding neuron demonstrated it acquires responsiveness to CS during conditioning, with inactivation of the feeding neuron during conditioning suppressing plasticity. These results suggest conditioning alters signals flowing from the CS into the feeding circuit, with the feeding neuron functioning as a key integrative hub for Hebbian plasticity (Sakurai, 2021).

This study demonstrate Pavlovian conditioning between tactile (CS) and gustatory (US) stimuli results in altered information processing by a pair of command neurons that control the Drosophila feeding circuit. This conditioning paradigm creates CS-induced excitement of the feeding neuron that commands feeding behavior in this animal, with the conditioned response requiring activity of the feeding neuron during pairing. Pioneering studies by Kandel and colleagues demonstrated the first synaptic and cellular mechanism underlying classical conditioning using the Aplysia gill withdrawal response. In Aplysia, the presynaptic terminal of a sensory neuron innervating the motor neuron was modulated by serotonin. Presynaptic modulation as a mechanism to generate Drosophila valence behaviors has been extensively studied, and recent progress indicates presynaptic terminals innervating mushroom body output neurons are modulated by dopaminergic neurons to establish Drosophila valence through appetitive and aversive olfactory association. Neither Aplysia plasticity nor Drosophila valence in these paradigms requires postsynaptic activity during learning. In contrast, Hebb proposed general principles to explain mechanisms for memory formation that better match results from commonly used mammalian experimental models, such as hippocampal long-term potentiation (LTP).Hebb postulated sequential firing of a presynaptic neuron and postsynaptic partner strengthens their connection. The requirement of feeding neuron activity for the conditioned response observed in this study fits well to a Hebbian mechanism if the underlying change is manifested in altered synaptic properties (although it cannot be excluded that inactivation of the feeding neuron and subsequent behavioral changes also alter neuromodulation, influencing memory formation). During association, CS-conveying neurons and the feeding neuron driven by sucrose stimulation would now fire together, resulting in strengthened connection between CS-conveying neurons and the feeding neuron according to a Hebbian mechanism. The response to US, however, did not change during conditioning, suggesting that connections between US-conveying neurons and the feeding neuron were not altered. Thus, one can hypothesize that the CS-feeding neuron circuit was newly established, whereas the pre-existing US-feeding neuron connection was not changed. These results suggest that Pavlovian conditioning is established through a change in information processing by the command neuron, which functions as the integrative hub of the feeding circuit (Sakurai, 2021).

This Pavlovian conditioning mechanism can also accommodate presynaptic modulation as demonstrated in Aplysia plasticity and Drosophila valence if reward signals are coupled to Hebbian plasticity through presynaptic neuromodulation. For Drosophila valance memory, reward signals consist of both sweet sensing and nutrition. Similar reward signals are likely to be relevant in vivo for Pavlovian conditioning, although the nutrition reward is eliminated in the current study due to removal of the esophagus from the preparation and application of a sucrose-wet paper strip only to the sensilla of the proboscis. Therefore, reward signals are likely constant between the groups that were tested, even for different US responses in the halorhodopsin experiments. Thus, differences in reward signal can be excluded from the altered conditioned responses observed between the groups. It is hypothesized that inactivation of the feeding neuron results in weaker memory due to postsynaptic activity in this neuron contributing to memory formation independent of changes in the reward signal. It is speculated that reward signals in the current model may also be mediated by dopamine, octopamine, or serotonin, similar to their role as reward signals in the mushroom bodies for Drosophila valence memory. In Aplysia, presynaptic adenylyl cyclase, which synthesizes cAMP, is believed to associate CS and US in this conditioning paradigm through US-driven serotonin modulation of the presynaptic terminal of the CS-conveying neuron. Adenylyl cyclase is encoded by rut, while dnc encodes a cAMP phosphodiesterase that degrades cAMP. As demonstrated in Aplysia and Drosophila, cAMP functions as a signal to modulate synaptic transmission. Given its role in LTP, cAMP is likely to play a critical role in Hebbian plasticity as well, consistent with the disruption of CS-US pairing in rut and dnc mutants. Considering the involvement of postsynaptic cells in Hebbian plasticity, retrograde signals from the postsynaptic cell can also be coupled to presynaptic cAMP signaling, as demonstrated previously at the Drosophila neuromuscular junction (Sakurai, 2021).

In the original experiment conducted by Pavlov, it is speculated there are groups of neurons that command feeding behavior in the dog. CS/US association may change responsiveness of a subset of those neurons that result in sound-induced saliva secretion, even in the absence of food signals. Electrophysiological studies have shown neural responses to CS are altered after Pavlovian conditioning in cat red nucleus and rabbit cerebellum, although how this kind of plastic change leads to alterations in command neuron function is unknown. Neurons with command function have been identified across many species. A command neuron is pivotally located within the sensorimotor watershed of a neuronal circuit and triggers a behavioral program after integrating numerous sensory inputs. Command neurons were first identified in crayfish through experiments where electrical stimulation of a certain neuron switched on or off behaviors, such as rhythmical movement of the swimmeret or escape responses. After identification of command neurons in invertebrate CNSs Mauthner cells were demonstrated to command escape behavior in fish. Recently, a group of neurons commanding feeding behavior have been identified in the mouse brain. Therefore, the scheme shown in Figure 4D may represent a common mechanism underlying Pavlovian conditioning across species, given the role of command neurons as an integrative hub within the sensorimotor watershed of neuronal circuits (Sakurai, 2021).

The feeding neuron in the Drosophila brain functions as a single command neuron pair that triggers the entire feeding program.3 This feature allowed reliable demonstration that CS-induced activation of the feeding neuron after conditioning was as robust as US-induced activation, suggesting the CS-induced activation of the feeding neuron can trigger the conditioned behavior. Thus, neurophysiological changes can be unambiguously correlated with behavioral change, making the causal relationship clear and allowing reliable manipulation. The current results are consistent with the assumption that both the CS signal and the US signal converge at a single identified neuron through a Hebbian mechanism. Taking advantage of the defined circuit with the feeding neuron at the center, it is not possible to define the cellular and molecular mechanisms for synaptic plasticity using this experimentally accessible neuron within the CNS. This approach, coupled with real-time live imaging, may allow tracking of changes in the structure or activity of identified synapses responsible for memory formation once CS-conveying neurons are defined in the experimental system. If so, it may be possible to directly observe pre- and/or postsynaptic changes mediating memory formation on the dendrite of the feeding neuron. Whether a new circuit is generated by strengthening a rudimentary pre-existing connection or a new connection forms de novo during associative conditioning will require future analysis. Molecular and cellular mechanisms underlying this plastic change can be investigated in detail as previously characterized at neuromuscular junctions. Taken together, the study of synaptic plasticity in the feeding neuron provides a model system to characterize basic principles of memory formation at the single-cell level (Sakurai, 2021).

Sucrose is an attractive feeding substance and a positive reinforcer for Drosophila. But Drosophila females have been shown to robustly reject a sucrose-containing option for egg-laying when given a choice between a plain and a sucrose-containing option in specific contexts. How the sweet taste system of Drosophila promotes context-dependent devaluation of an egg-laying option that contains sucrose, an otherwise highly appetitive tastant, is unknown. This study reports that devaluation of sweetness/sucrose for egg-laying is executed by a sensory pathway recruited specifically by the sweet neurons on the legs of Drosophila First, silencing just the leg sweet neurons caused acceptance of the sucrose option in a sucrose versus plain decision, whereas expressing the channelrhodopsin CsChrimson in them caused rejection of a plain option that was "baited" with light over another that was not. Analogous bidirectional manipulations of other sweet neurons did not produce these effects. Second, circuit tracing revealed that the leg sweet neurons receive different presynaptic neuromodulations compared to some other sweet neurons and were the only ones with postsynaptic partners that projected prominently to the superior lateral protocerebrum (SLP) in the brain. Third, silencing one specific SLP-projecting postsynaptic partner of the leg sweet neurons reduced sucrose rejection, whereas expressing CsChrimson in it promoted rejection of a light-baited option during egg-laying. These results uncover that the Drosophila sweet taste system exhibits a functional division that is value-based and task-specific, challenging the conventional view that the system adheres to a simple labeled-line coding scheme (Chen, 2022).

Swallowing is an essential step of eating and drinking. However, how the quality of a food bolus is sensed by pharyngeal neurons is largely unknown. This study finds that mechanical receptors along the Drosophila pharynx are required for control of meal size, especially for food of high viscosity. The mechanical force exerted by the bolus passing across the pharynx is detected by neurons expressing the mechanotransduction channel NOMPC (no mechanoreceptor potential C) and is relayed, together with gustatory information, to IN1 neurons in the subesophageal zone (SEZ) of the brain. IN1 (ingestion neurons) neurons act directly upstream of a group of peptidergic neurons that encode satiety. Prolonged activation of IN1 neurons suppresses feeding. IN1 neurons receive inhibition from DSOG1 (descending subesophageal neurons) neurons, a group of GABAergic neurons that non-selectively suppress feeding. These results reveal the function of pharyngeal mechanoreceptors and their downstream neural circuits in the control of food ingestion (Yang, 221).

Feeding is an essential part of animal life that is greatly impacted by the sense of taste. Although the characterization of taste-detection at the periphery has been extensive, higher order taste and feeding circuits are still being elucidated. This study used an automated closed-loop optogenetic activation screen to detect novel taste and feeding neurons in Drosophila melanogaster. Out of 122 Janelia FlyLight Project GAL4 lines preselected based on expression pattern, this study identified six lines that acutely promote feeding and 35 lines that inhibit it. As proof of principle, R70C07-GAL4, which labels neurons that strongly inhibit feeding, was analyzed. Using split-GAL4 lines to isolate subsets of the R70C07-GAL4 population, both appetitive and aversive neurons were found. Furthermore, this study shows that R70C07-GAL4 labels putative second-order taste interneurons in the subesophageal zone that contact both sweet and bitter sensory neurons. These results serve as a resource for further functional dissection of fly feeding circuits (Lau, 2021).

This study reconstructed, from a whole CNS EM volume, the synaptic map of input and output neurons that underlie food intake behavior of Drosophila larvae. Input neurons originate from enteric, pharyngeal and external sensory organs and converge onto seven distinct sensory synaptic compartments within the CNS. Output neurons consist of feeding motor, serotonergic modulatory and neuroendocrine neurons. Monosynaptic connections from a set of sensory synaptic compartments cover the motor, modulatory and neuroendocrine targets in overlapping domains. Polysynaptic routes are superimposed on top of monosynaptic connections, resulting in divergent sensory paths that converge on common outputs. A completely different set of sensory compartments is connected to the mushroom body calyx. The mushroom body output neurons are connected to interneurons that directly target the feeding output neurons. These results illustrate a circuit architecture in which monosynaptic and multisynaptic connections from sensory inputs traverse onto output neurons via a series of converging paths (Miroschnikow, 2018).

Motor outputs of a nervous system can be broadly defined into those carried out by the muscles to produce movements and by the glands for secretion. Both of these behavioral and physiological events are regulated by a network of output neurons, interneurons and sensory neurons, and a major open question is how one neural path is selected from multiple possible paths to produce a desired output. Nervous system complexity and tool availability have strongly dictated the type of experimental system and analysis that can be used to address this issue, such as a focus on a particular organism, behavior or type of neuron. In this context, the detailed illustrations of different parts of nervous systems at neuronal level as pioneered by Cajal, to the first complete description of a nervous system wiring diagram at synaptic level for C. elegans, demonstrate the power of systematic neuroanatomical analysis in providing a foundation and guide for studying nervous system function. However, the technical challenges posed by such analysis have limited the type of organisms for which synaptic resolution mapping can be performed at the scale of an entire nervous system (Miroschnikow, 2018).

Analysis of the neural circuits that mediate food intake in the Drosophila larvae offers numerous advantages in meeting the challenge of neuroanatomical mapping at a whole brain level, and combining it with the ability to perform behavioral and physiological experiments. The muscle system that generates the different movements necessary for transporting food from the pharynx to the esophagus, as well as the endocrine system responsible for secreting various hormones for metabolism and growth, have both been well described. These are also complemented by the analysis of feeding behavior in adult flies. Although there is broad knowledge at the morphological level on the organs underlying larval feeding behavior and physiology, as well as on the nerves innervating them in the periphery, the central connectivity of the afferent and efferent neurons within these nerves are largely unknown. At the same time, advances in the EM reconstruction of an entire CNS of a first instar larva offers an opportunity to elucidate an animals' feeding system on a brain-wide scale and at synaptic resolution. As part of this community effort, an integrated analysis of fast synaptic and neuropeptide receptor connections for an identified cluster of 20 interneurons that express the neuropeptide hugin, a homolog of the mammalian neuropeptide neuromedin U, and which regulates food intake behavior. This analysis showed that the class of hugin neurons modulating food intake receives direct synaptic inputs from a specific group of sensory neurons, and in turn, makes mono-synaptic contacts to output neuroendocrine cells. The study not only provided a starting point for a combined approach to studying synaptic and neuropeptidergic circuits, but a basis for a comprehensive mapping of the sensory and output neurons that innervate the major feeding and endocrine organs (Miroschnikow, 2018).

Feeding is one of the most universal and important activities that animals engage in. Despite large differences in the morphology of the external feeding organs, the internal gut structures are quite similar across different animals; indeed, even within closely related species, there can be large differences in the external organs that detect and gather food, whereas the internal organs that transport food through the alimentary canal are much more similar. Recent studies have also pointed out the functional similarities between the subesophageal zone in insects and the brainstem in vertebrates for regulating feeding behavior. In mammals, the different cranial nerves from the medulla innervate distinct muscles and glands of the foregut (Figure 1A). For example, the VIIth cranial nerve (facial nerve) carries taste sensory information from anterior 2/3 of the tongue, and innervates the salivary glands, and lip and facial muscles. The IXth cranial nerve (glossopharyngeal nerve) receives taste inputs from the posterior 1/3 of the tongue, and innervates the salivary glands and pharynx muscles. The Xth cranial nerve (vagus nerve) receives majority of the sensory inputs from the enteric nervous system of the gut, and innervates pharynx and esophagus muscles. The XIth cranial nerve (spinal accessory nerve) and the XIIth cranial nerve (hypoglossal nerve) are thought to carry strictly motor information which innervate the pharynx and neck muscles, and the tongue muscles. The distinct cranial nerves project onto topographically distinct areas in the medulla of the brainstem. It is also noted that olfactory information is carried by cranial nerve I, a strictly sensory nerve that projects to the olfactory bulb (OB), an area topographically distinct from the brainstem. In addition, there are direct neuronal connections between the brainstem and the hypothalamus, the key neuroendocrine center of vertebrates (Miroschnikow, 2018).

Analogously, distinct pharyngeal nerves of the Drosophila larva are connected to the subesophageal zone (SEZ), and also carry sensory and motor information that regulate different parts of the body. The AN (antennal nerve) carries sensory information from the olfactory, pharyngeal and internal organs, and innervates the pharyngeal muscles for pumping in food. The serotonergic neurons that innervate the major endocrine center and the enteric nervous system also project through the AN. Note also that the olfactory sensory organs project to the antennal lobe (AL), which abuts the SEZ yet is topographically separate. The MxN (maxillary nerve) carries external and pharyngeal sensory information, and innervates the mouth hooks, whose movements are involved in both feeding and locomotion. The PaN (prothoracic accessory nerve) carries external sensory information from the upper head region, and innervates the muscles involved in head tilting (see EM reconstruction of the pharyngeal nerves of Drosophila larva). Furthermore, the SEZ has direct connections to median neurosecretory cells (mNSCs) and the ring gland. In sum, although a large body of knowledge exists on the gross anatomy of the nerves that target the feeding organs in vertebrates and invertebrates, the synaptic pathways within the brain that interconnect the sensory inputs and output neurons of the individual nerves remain to be elucidated (Miroschnikow, 2018).

This study has reconstructed all sensory, serotonergic modulatory (Se0) and motor neurons of the three pharyngeal nerves that underlie the feeding motor program of Drosophila larvae. The activity of these nerves has previously been shown to be sufficient for generating the feeding motor pattern in isolated nervous system preparations, and that the central pattern generators (CPGs) for food intake lie in the SEZ. All monosynaptic connections were identified between the sensory inputs and the motor, Se0 and previously described median neurosecretory ouput neurons, thus providing a full monosynaptic reflex circuit for food intake. Polysynaptic pathways that are integrated onto the monosynaptic reflex circuits. In addition, the multisynaptic non-olfactory neuron connections from the sensory neurons to the mushroom body memory circuit were also mapped, and these were shown to be different from those involved in monosynaptic reflex circuits. Finally, a set of mushroom body output neurons were traced onto the neurosecretory and other feeding output neurons. Reflex circuits can be seen to represent the simplest synaptic architecture in the nervous system, as formulated by Charles Sherrington. Anatomical reconstructions of monosynaptic and polysynaptic reflex circuits can also be seen in the works of Cajal. A model is proposed of how different mono- and polysynaptic pathways can be traversed from a set of sensory neurons to specific output neurons, which has relevance for understanding the mechanisms of action selection (Miroschnikow, 2018).

It was asked how the hugin neuropeptide circuit, which relays gustatory information to the protocerebrum, would be positioned with respect to the monosynaptic reflex and multisynaptic MB memory circuits. Based on earlier studies on mapping sensory inputs onto hugin protocerebrum neurons (huginPC), it was expected that most inputs from the ACp, which is the primary gustatory sensory compartment. However, most of the huginPC neurons receive inputs from the sensory compartments ACa and AVa, which are the two major monosynaptic compartments that originate from enteric regions. HuginPC neurons do receive inputs from the external and pharyngeal organs (i.e., through sensory compartment ACp), but to a much smaller degree. Thus, unlike the MB circuit that utilizes a completely new set of sensory inputs, the huginPC circuit is associated with a feeding related monosynaptic circuit (Miroschnikow, 2018).

Based on these observations from the hugin neuropeptide circuit in interconnecting sensory and neuroendocrine outputs, a broader question concerning input-output connections was asked: for any given pair of neurons comprising the monosynaptic reflex circuit, how many additional polysynaptic paths exist and what could be the functional significance of such parallel pathways? To illustrate, a target neuron was selected from a cluster of neurosecretory and serotonergic modulatory output cells (Dilps and Se0ens), and all sensory neurons that make monosynaptic connections with at least two synapses were listed listed. It was then asked, using the same threshold, how many different di-synaptic paths (2-hop) exist and how often a particular interneuron is used for the different possible converging paths ('degree' of convergence). The relative synaptic strengths of the connection among the various paths ('ranking index' of 1.0 represents highest synaptic strength from all possible inputs to the output neuron). Several properties are revealed: (1) different sensory neurons make monosynaptic contacts to a common output target (2) each output neuron can be reached from a given sensory neuron by multiple routes through the use of different interneurons (3) a given interneuron can receive inputs from different sensory neurons to target the same output neuron; this would fit the definition of the ‘common path’ that Sherrington described. These observations hold true for the majority of monosynaptic sensory-output pairs examined. However, no correlation was seen between the relative synaptic strength and the commonness of the respective paths (i.e., how often a path is used) (see Integration of polysynaptic connections onto monosynaptic circuits) (Miroschnikow, 2018).

This study provides a comprehensive synaptic map of the sensory and output neurons that underlie food intake and metabolic homeostasis in Drosophila larva. Seven topographically distinct sensory compartments, based on modality and peripheral origin, subdivide the SEZ, a region with functional similarities to the vertebrate brainstem. Sensory neurons that form monosynaptic connections are mostly of enteric origin, and are distinct from those that form multisynaptic connections to the mushroom body (MB) memory circuit. Different polysynaptic connections are superimposed on the monosynaptic input-ouput pairs that comprise the reflex arc. Such circuit architecture may be used for controlling feeding reflexes and other instinctive behaviors (Miroschnikow, 2018).

Reflex circuits represent a basic circuit architecture of the nervous system, whose anatomical and physiological foundations were laid down by Cajal and Sherrington. The Drosophila larval feeding reflex circuit comprises the motor neurons that innervate the muscles involved in pharyngeal pumping, as well as the neurosecretory neurons that target the endocrine organs. They also include a cluster of serotonergic neurons that innervate the entire enteric nervous system, and which may have neuromodulatory effects on the feeding system in a global manner. The vast majority of output neurons are targeted monosynaptically from a set of topographically distinct sensory synaptic compartments in the CNS. These compartments target the output neurons in overlapping domains: the first, ACa, targets all neuroendocrine cells as well as the serotonergic neurons; the second, AVa, targets a subset of neuroendocrine cells, the serotonergic neurons and most of the pharyngeal motor neurons, while the third, AVp, targets the serotonergic neurons and a different set of pharyngeal motor neurons. With these outputs, one can in principle fulfill the most basic physiological and behavioral needs for feeding: neurosecretory cells for metabolic regulation and pharyngeal motor neurons for food intake. This set of monosynaptic connections can thus be seen to represent an elemental circuit for feeding, since the connections between the input and output neurons cannot be broken down any further (Miroschnikow, 2018).

Vast majority of the sensory inputs comprising this 'elemental feeding circuit' derive from the enteric nervous system to target the pharyngeal muscles involved in food intake and neuroendocrine output organs. However, there is a small number of monosynaptic reflex connections that originate from the somatosensory compartment. The output neurons targeted by these somatosensory neurons are motor neurons that control mouth hook movements and head tilting, movements which are involved in both feeding and locomotion. In this context, it is noteworthy that monosynaptic reflex connections are found to a much lesser degree in the larval ventral nerve cord, which generates locomotion. An analogous situation exists in C. elegans, where majority of the monosynaptic reflex circuits are found in the head motor neurons and not in the body. One reason could be due to the relative complexity in the response necessary for food intake as compared to locomotion. For example, a decision to finally not to swallow a harmful substance, once in the mouth, may require a more local response, for example muscles limited to a very specific region of the pharynx and esophagus, where monosynaptic arc might suffice. By contrast, initiating escape behaviors requires a more global response with respect to the range and coordination of body movements involved, although it also employs multimodal sensory integration via a multilayered circuit (Miroschnikow, 2018).

The inter-sensory connections show a combination of hierarchical and reciprocal connections, which may increase the regulatory capability and could be especially important for monosynaptic circuits. By contrast, very few monosynaptic connections exist between the larval olfactory, chordotonal or nociceptive class IV sensory neurons in the body. Interestingly, there is also a much higher percentage of intersensory connections between olfactory receptor neurons in the adult as compared to the larva, which could function in gain modulation at low signal intensities. This might be attributable to adults requiring faster processing of olfactory information during flight navigation (or mating), and/or to minimize metabolic cost. Whether such explanation also applies to the differences in intersensory connection between the different types of sensory neurons in the larvae remains to be determined (Miroschnikow, 2018).

This study found very few cases where a monosynaptic path between any sensory-output pair is not additionally connected via a polysynaptic path. An interesting question in the context of action selection mechanism is which path a sensory signal uses to reach a specific target neuron. For example, a very strong sensory signal may result in a monosynaptic reflex path being used. However, a weaker sensory signal may result in using a different path, such as one with less threshold for activation. This would also enable the integration of different types of sensory signals through the usage of multiple interneurons, since the interneurons may receive sensory inputs that are not present in monosynaptic connections. For example, sensory neurons can target the neuroendocrine cells directly (monosynaptically), or through a hugin interneuron (di-synaptically). The sensory compartments that directly target the neuroendocrine cells are of enteric origin; however, when hugin neurons are utilized as interneurons, not only is the number of sensory neurons from the same sensory compartment increased, but sensory neurons are added from a completely new peripheral origin. Thus, the hugin interneurons enable sensory inputs from different peripheral origins, for example to integrate enteric inputs with pharyngeal gustatory inputs, to influence an output response, which, in this case, is to stop feeding (Schoofs et al., 2014a) (Miroschnikow, 2018).

The coexistence of polysynaptic and monosynaptic paths could also be relevant for circuit variability and compensation: destruction of any given path would still enable the circuit to function, but with more restrictions on the precise types of sensory information it can respond to. In certain cases, this may even lead to strengthening of alternate paths as a form of synaptic plasticit (Miroschnikow, 2018).

An open issue is how the sensory synaptic compartments might be connected to the feeding central pattern generators (CPGs) which have been demonstrated to exist in the SEZ, especially since CPGs are defined as neural circuits that can generate rhythmic motor patterns in the absence of sensory input. However, the modulation of CPG rhythmic activity can be brought about by sensory and neuromodulatory inputs. A complete circuit reconstruction of the larval SEZ circuit may shed some light on the circuit structure of feeding CPGs (Miroschnikow, 2018).

A more complex circuit architecture is represented by the MB, the site of associative learning and memory in insects: a completely different set of sensory synaptic compartments is used to connect the various projection neurons to the MB calyx. Thus, the MB module is not superimposed onto the monosynaptic reflex circuits but rather forms a separate unit. The classical studies by Pavlov demonstrated conditioned reflex based on an external signal and an autonomic secretory response in response to food. Although a comparable autonomic response has not been analyzed in the larvae, analogous associative behavior based on odor choice response has been well studied. It is also noteworthy that in the Aplysia, classical conditioning of the gill withdrawal reflex involves monosynaptic connections between a sensory neuron (mechanosensory) and a motor neuron, and neuromodulation by serotonin. This constellation has similarities with the elemental feeding circuit consisting of sensory, motor and serotonergic modulatory neurons. For more complex circuits of feeding behavior in the mouse, a memory device for physiological state, such as hunger, has been reported involving synaptic and neuropeptide hormone circuits. Functional studies on MB output neurons such as the MBON-f1, which may be part of a ‘psychomotor’ pathway and which targets a number of interneurons that connect to the neurosecretory, serotonergic and pharyngeal motor neurons, may help address how memory circuits interact with feeding circuits (Miroschnikow, 2018).

Feeding behavior manifests itself from the most primitive instincts of lower animals, to deep psychological and social aspects in humans. It encompasses cogitating on the finest aspects of food taste and the memories evoked by the experience, to sudden reflex reactions upon unexpectedly biting down on a hard seed or shell. Both of these extremes are mediated, to a large degree, by a common set of feeding organs, but the way these organs become utilized can vary greatly. The architecture of the feeding circuit described in this study allows the various types of sensory inputs to converge on a limited number of output responses. The monosynaptic pathways would be used when fastest response is needed. The presence of polysynaptic paths would enable slower and finer control of these reflex events by allowing different sensory inputs, strengths or modalities to act on the monosynaptic circuit. This can be placed in the context in the control of emotions and survival circuits, or by cortex regulation of basic physiological or autonomic processes. In a striking example, pupil dilation, a reflex response, has been used as an indicator of cognitive activity. Here, a major function of having more complex circuit modules on top of monosynaptic circuits may be to allow a finer regulation of feeding reflexes, and perhaps of other reflexes or instinctive behaviors (Miroschnikow, 2018).

As an outlook, this analysis provides an architectural framework of how a feeding circuit is organized in the CNS. The circuit is divided into two main axes that connect the input to the output systems: the sensory-neurosecretory cell axis and the sensory-motor neuron axis. The sensory system targets overlapping domains of the output neurons; for example, a set of sensory neurons targets exclusively the neuroendocrine cells, other targets both neuroendocrine and pharyngeal motor neurons, and another just the pharyngeal motor neurons. The inputs derive mostly from the internal organs. These connections form the monosynaptic reflex circuits. With these circuits, one can perform the major requirements of feeding regulation, from food intake and ingestion to metabolic homeostasis. Additional multisynaptic circuits, such as the CPGs, those involving sensory signaling from the somatosensory system (external inputs), or those comprising the memory circuits, are integrated or added to expand the behavioral repertoire of the animal see Input-output synaptic organization of the larval feeding system and its connectivity architecture in the brain. Although circuit construction may proceed from internal to the external, the sequence is reversed in a feeding animal: the first sensory cues are external (olfactory), resulting in locomotion (somatic muscles) that can be influenced by memory of previous experience; this is followed by external taste cues, resulting in food intake into the mouth; the final action is the swallowing of food, involving pharyngeal and enteric signals and reflex circuits. However, regardless of the types of sensory inputs, and whether these are transmitted through a reflex arc, a memory circuit or some other multisynaptic circuits in the brain, they will likely converge onto a certain set of output neurons, what Sherrington referred to as the ‘final common path’. The current work is a first step towards finding the common paths (Miroschnikow, 2018).

The decision to engage in one behavior often precludes the selection of others, suggesting cross-inhibition between incompatible behaviors. For example, the likelihood to initiate feeding might be influenced by an animal's commitment to other behaviors. This study examined the modulation of feeding behavior in the fruit fly, Drosophila melanogaster, and identified a pair of interneurons in the ventral nerve cord that is activated by stimulation of mechanosensory neurons and inhibits feeding initiation, suggesting that these neurons suppress feeding while the fly is walking. Conversely, inhibiting activity in these neurons promotes feeding initiation and inhibits locomotion. These studies demonstrate the mutual exclusivity between locomotion and feeding initiation in the fly, isolate interneurons that influence this behavioral choice, and provide a framework for studying the neural basis for behavioral exclusivity in Drosophila (Mann, 2013).

The neurons that inhibit proboscis extension (which are named PER_in) have cell bodies and processes in the first leg neuromeres of the VNC and projections to the SOG, the brain region that contains gustatory sensory axons and proboscis motor neuron dendrites. Labeling with the presynaptic synaptotagmin- GFP marker and the postsynaptic DenMark marker indicated that the dendrites of PER_in neurons are restricted to the first leg neuromeres, whereas axons are found in both the SOG and the first leg neuromeres. The anatomy of these neurons suggests that they convey information from the leg neuromeres to a region of the fly brain involved in gustatory processing and proboscis extension. Anatomical studies examining the proximity of PER_in fibers to gustatory sensory dendrites or proboscis motor axons revealed that PER_in neurons do not come into close contact with known neurons that regulate proboscis extension (Mann, 2013).

Many behaviors are mutually exclusive, with the decision to commit to one behavior excluding the selection of others. This study shows that feeding initiation and locomotion are mutually exclusive behaviors and that activity in a single pair of interneurons influences this behavioral choice. PER_in neurons are activated by stimulation of mechanosensory neurons and activation of PER_in inhibits proboscis extension, suggesting that they inhibit feeding while the animal is walking. Consistent with this, leg removal or immobilization enhances proboscis extension probability and this is inhibited by increased PER_in activity. The opposite behavior is elicited upon inhibiting activity in PER_in neurons: animals show constitutive proboscis extension at the expense of locomotion. This work shows that activity in a single pair of interneurons dramatically influences the choice between feeding initiation and movement (Mann, 2013).

The precise mechanism of activation of PER_in neurons remains to be determined. PER_in dendrites reside in the first leg neuromere, suggesting that they process information from the legs. Stimulation of leg chemosensory bristles with sucrose or quinine or activation of sugar, bitter, or water neurons using optogenetic approaches did not activate PER_in neurons, nor did satiety state change tonic activity. Stimulation of sensory nerves into the ventral nerve cord and stimulation of mechanosensory neurons, using a nompC driver, activated PER_in. In addition, by monitoring activity of PER_in while flies moved their legs, it was demonstrated that activity was coincident with movement (Mann, 2013).

These studies argue that PER_in is activated by nongustatory cues in response to movement, likely upon detection of mechanosensory cues. Additional cues may also activate PER_in. Studies of behavioral exclusivity in other invertebrate species suggest two mechanisms by which one behavior suppresses others. One strategy is by competition between command neurons that activate dedicated circuits for different behaviors. More common is a strategy in which decision- making occurs by distributed activity changes across neural populations. Although this studies are a starting point to begin to examine these models in Drosophila, the circuits for proboscis extension and locomotion drive different motor neurons, muscles, and behaviors, suggesting that they may be connected by a few links rather than largely overlapping circuitry. PER_in is likely to inhibit feeding initiation while the animal is moving and is one critical link. The observation that simply gluing the proboscis in an extended state, but not in a retracted state, inhibits locomotion suggests that motor activity or proprioceptive feedback from the proboscis acts as a reciprocal link to locomotor circuits (Mann, 2013).

Neurons act over different timescales and in response to different sensory cues to influence behavior. The powerful molecular genetic approaches available in Drosophila enable the precise manipulation of individual neurons and allow for the examination of their function in awake, behaving animals. Modulatory neurons such as PER_in are difficult to identify by calcium imaging or electrophysiological approaches because they influence gustatory-driven behavior but are not activated by gustatory stimulation. The ability to probe the function of neurons in unbiased behavioral screens facilitates the identification of neurons that act as critical nodes to influence behavior. The identification and characterization of PER_in as a significant modulator of feeding initiation provides a foundation for future studies determining how PER_in influences proboscis extension circuits to alter behavioral probability and how mechanosensory inputs activate PER_in. In addition, examining how proboscis extension suppresses locomotion will provide important insight into the links between different behaviors (Mann, 2013).

Neural circuits for a given behavior do not work in isolation. Information from multiple sensory cues, physiological state, and experience must be integrated to guide behavioral decisions. This work uncovers a pair of interneurons that influences the choice between feeding initiation and locomotion. The discovery of the PER_in neurons will aid in examining the neural basis of innate behaviors and the decision-making processes that produce them (Mann, 2013).

Odor-based learning and innate odor-driven behavior have been hypothesized to require separate neuronal circuitry. Contrary to this notion, innate behavior and olfactory learning were recently shown to share circuitry that includes the Drosophila mushroom body (MB). But how a single circuit drives two discrete behaviors remains unknown. This study defines an MB circuit responsible for both olfactory learning and innate odor avoidance and the distinct dDA1 dopamine receptor-dependent signaling pathways that mediate these behaviors. Associative learning and learning-induced MB plasticity require rutabaga-encoded adenylyl cyclase activity in the MB. In contrast, innate odor preferences driven by naive MB neurotransmission are rutabaga independent, requiring the adenylyl cyclase ACXD. Both learning and innate odor preferences converge on PKA and the downstream MBON-γ2α'1. Importantly, the utilization of this shared circuitry for innate behavior only becomes apparent with hunger, indicating that hardwired innate behavior becomes more flexible during states of stress (Noyes, 2023).

The data reveal the shared use of a discrete circuit for both state-dependent odor-driven behavior and experience-dependent odor learning. The shared components include the upstream DA neurons, the MBn-expressed DA receptor dDA1, and the downstream MBON. Odor response processing for state-dependent behavior and odor learning diverge at the level of the dDA1 receptor-activated adenylyl cyclase, with ACXD employed for innate state-dependent odor driven behavior and rut employed for olfactory learning. The unique activation of rut for olfactory learning is explained by the fact that this adenylyl cyclase functions as a coincidence detector, synergistically responding to both DA receptor activation from the unconditioned stimulus and Ca2+ increases due to the conditioned stimulus. ACXD is a transmembrane AC that is expressed in a number of tissues including the brain and is orthologous to the mammalian AC2. Mammalian AC2 activity is Ca2+ independent. If ACXD is also Ca2+ independent, it would provide a mechanism for the engagement of distinct cAMP pathways by dDA1 for state-dependent versus experience-dependent olfactory behavior. Thus, common neural circuitry is employed for both state-dependent and conditioned behaviors with the unique changes of MBn output influenced by the intracellular signaling pathways that are mobilized (Noyes, 2023).

Dopaminergic input to the dDA1 DA receptor expressed in the γ2 compartment of mushroom body neurons (MBn) activates an intracellular signaling pathway that includes the ACXD adenylyl cyclase, PKA activity, and the release of ACh. The downstream MBON-γ2α'1 responds to the MB ACh release through the α2 nACh receptor, with the activity of the MBON-γ2α'1 ultimately dictating the balance in state-dependent odor approach/avoidance. The simplest model to account for the state-dependent MBON activity would have the internal state modulating DA input into the MBn to increase or decrease ACh release onto the MBON. However, the data failed to detect a significant change in ACh release between the fed and starved conditions. Nevertheless, the activity of the PPL1-γ2α'1 (PPL referring to protocerebral posterior lateral region of the brain) that influences the MB γ2 compartment is required for state-dependent behavioral responses to odor. A proposed model for reconciling these observations envisions that the basal activity of this circuit is required for behavioral-state odor choice but that starvation mobilizes a qualitative or quantitative signal independent of the magnitude ACh release by the MBn to increase MBON activity. An unidentified signal representing hunger could directly enhance MBON excitability. For example, octopamine has been proposed as a feeding signal that acts directly on MBONs (Noyes, 2023).

Similarly, a hunger signal could act on neurons elsewhere in the brain that ultimately connect to MBONs through intermediary neurons. The hunger-responsive neuropeptide leucokinin acts on DAns that connect to MBONs (Noyes, 2023).

Alternatively, there may be a co-neurotransmitter released by the MBn due to starvation that works to increase MBON activity. Finally, the possibility that starvation does modulate MBn ACh output is left open, but the reporters employed lack the sensitivity to detect this change. Future investigations into state-dependent changes in MBON-γ2α'1 physiology will need to grapple with numerous competing hypotheses (Noyes, 2023).

Changes in odor responses in MBONs have suggested that learning induces a change in connectivity in the MBn-MBON synapse. In addition, compartment specific plasticity in MB ACh release was discovered that fits with the idea that plasticity observed in MBONs occurs from the input of MB compartments. However, there has been a lack of data connecting the MBn ACh release plasticity with MBON plasticity and particularly to the central role for the rut adenylyl cyclase. The data offer this important connection. The MB γ2 compartment undergoes a rapid depression in response to odor/shock pairings during aversive learning and that rut is required for the acquisition of this depression. Downstream of the MB γ2 compartment, MBON-γ2α'1 drives approach and also undergoes a learning-induced depression (Noyes, 2023).

These results put prior speculation about how the genetic regulation of cAMP signaling through the rut adenylyl cyclase drives Drosophila memory on concrete ground. This work does not conclusively delineate a role for dDA1 in the MB plasticity. Loss of MB dDA1 dramatically reduced naive odor responses in MB γ2. This precluded attempts to measure dDA1 effects on MB γ2 depression because the naive responses were already low. Interestingly, both learning- and starvation-dependent odor avoidance require PKA. A likely explanation is that rut and ACXD are spatially segregated, creating distinct cAMP microdomains or signaling platforms (Noyes, 2023).

Thus, PKA activity would result in the phosphorylation of unique substrates within those microdomains. The characterization of the MB as a brain region for learned, but not innate, olfactory behavior was motivated by experiments eliminating MBn or blocking MB output. Disrupting the MB eliminates odor-associated memory but has no effect on innate avoidance of those same odors. Recent work has overturned this simple categorization demonstrating some DANs, MBONs, and MBns do contribute to innate olfactory behavior in certain circumstances. Interestingly, the majority of reports define a role for MBns in innate behavioral responses to food-related odors. The current results, using more general, non-food odors, puts the hypothesis that MBn regulates innate behavior on more solid ground. Importantly, MB dDA1 was found to be required for state-dependent behavior to general odors. This is in contrast to a report finding that dDA1 is not involved but that DAMB is required for state-dependent behavior to food odors. This difference will be a key element to understand state-dependent behavior moving forward. The Drosophila and mammalian olfactory systems are remarkably similar in terms of anatomical organization and function (Noyes, 2023).

In both, odorant molecules activate olfactory sensory neurons (OSNs), with each OSN only expressing one type of odorant receptor (OR). Each OSN expressing the same type of OR project to the same glomeruli. Within the glomeruli, the OSNs synapse onto projection neuron (PN) dendrites, and PN activity is modified in the glomeruli by local inhibitory interneurons before being sent on to multiple higher-order brain regions. PN neurons connect to downstream neurons in the mammalian piriform cortex and in the Drosophila MB in a seemingly random manner. Like the Drosophila MB, the piriform cortex is critical for olfactory memory. It is not clear how the piriform cortex is involved in state-dependent olfactory behavior. However, in humans, odor coding changes in the piriform cortex with hunger and sleep deprivation, and piriform cortex neuron activity levels are inversely correlated with sexual satiety in rats (Noyes, 2023).

It is concluded from the results demonstrating dDA1-dependent MBn Ach release and a dDA1-dependent MBON-γ2&alpha'1 Ca2+ in response to odor that dDA1 directly modulates the MBn/MBON connection. However, due to a limitation in the sensitivity of the ACh sensor employed, it was not possible to directly record ACh input to MBON-γ2&alpha'1. Based on the established direct cholinergic connection between these MBn and MBONs and the lack of any known non-MB cholinergic innervation to this brain region, it is believed that the conclusions are merited. However, it is necessary to leave open as a formal possibility that other intermediary neurons mediate this relationship (Noyes, 2023).

The sensitivity of animals to sensory input must be regulated to ensure that signals are detected and also discriminable. However, how circuits regulate the dynamic range of sensitivity to sensory stimuli is not well understood. A given odor is represented in the insect mushroom bodies (MBs) by sparse combinatorial coding by Kenyon cells (KCs), forming an odor quality representation. To address how intensity of sensory stimuli is processed at the level of the MB input region, the calyx, this study characterized a set of novel mushroom body output neurons that respond preferentially to high odor concentrations. A pair of MB calyx output neurons, MBON-a1/2, were shown to be postsynaptic in the MB calyx, where they receive extensive synaptic inputs from KC dendrites, the inhibitory feedback neuron APL, and octopaminergic sVUM1 neurons, but relatively few inputs from projection neurons. This pattern is broadly consistent in the third-instar larva as well as in the first instar connectome. MBON-a1/a2 presynaptic terminals innervate a region immediately surrounding the MB medial lobe output region in the ipsilateral and contralateral brain hemispheres. By monitoring calcium activity using jRCamP1b, it was found that MBON-a1/a2 responses are odor-concentration dependent, responding only to ethyl acetate (EA) concentrations higher than a 200-fold dilution, in contrast to MB neurons which are more concentration-invariant and respond to EA dilutions as low as 10^-4. Optogenetic activation of the calyx-innervating sVUM1 modulatory neurons originating in the SEZ (Subesophageal zone), did not show a detectable effect on MBON-a1/a2 odor responses. Optogenetic activation of MBON-a1/a2 using CsChrimson impaired odor discrimination learning compared to controls. It is proposed that MBON-a1/a2 form an output channel of the calyx, summing convergent sensory and modulatory input, firing preferentially to high odor concentration, and might affect the activity of downstream MB targets (Mohamed, 2023).

Animals retain some but not all experiences in long-term memory (LTM). Sleep supports LTM retention across animal species. It is well established that learning experiences enhance post-learning sleep. However, the underlying mechanisms of how learning mediates sleep for memory retention are not clear. Drosophila males display increased amounts of sleep after courtship learning. Courtship learning depends on Mushroom Body (MB) neurons, and post-learning sleep is mediated by the sleep-promoting ventral Fan-Shaped Body neurons (vFBs). This study shows that post-learning sleep is regulated by two opposing output neurons (MBONs) from the MB, which encode a measure of learning. Excitatory MBONs-γ2α'1 becomes increasingly active upon increasing time of learning, whereas inhibitory MBONs-β'2mp is activated only by a short learning experience. These MB outputs are integrated by SFS neurons, which excite vFBs to promote sleep after prolonged but not short training. This circuit may ensure that only longer or more intense learning experiences induce sleep and are thereby consolidated into LTM (Lei, 2022).

This study has identified a neural circuit that regulates learning-induced sleep for LTM consolidation. This circuit links neurons essential for learning and memory in Drosophila, the MB neurons, with those critical for post-learning sleep, the vFBs8. It is proposed that only a longer learning experience is sufficient to induce sleep, and thereby be consolidated into LTM. Given that the increasing duration of a learning experience correlates with the total amount of time males spend on futile courtship towards mated females during training, selective activation of vFBs likely depends on the amount or intensity of a learning experience, rather than just its duration. Post-learning sleep induction requires integration of two MB outputs, previously implicated in courtship memory in SFSs. Post-learning activity of the excitatory MBONs-γ2α'1 increases linearly with the duration of the prolonged learning experience. In contrast, activity of the inhibitory MBONs-β'2mp peaks after a short experience sufficient to induce STM. As a result, only when the males court mated females sufficiently long or intensely, the activity of MBONs-γ2α'1 reaches the threshold required to activate SFSs. This in turn leads to activation of vFBs to promote post-learning sleep and the reactivation of those dopaminergic neurons (DANs) that were involved in memory encoding. Consequently, biochemical processes essential for LTM consolidation become engaged (Lei, 2022).

How might MBONs-γ2α'1 and MBONs-β'2mp measure the learning experience to control post-learning sleep? In homeostatic sleep regulation, the potentiation of R2 neurons reflects a measure of sleep loss that is sensed by dFBs, likely in response to the accumulation of byproducts of oxidative stress during sleep loss. In the case of learning-induced sleep, it is envisioned that learning results in lasting changes in the molecular pathways essential for memory formation in the MB. For example, the cAMP pathway along with the dopamine receptor are activated during sleep in a discrete 3-h time window after learning in rodents and Drosophila males lacking a dopamine receptor, and hence unable to learn, do not display increased post-learning sleep. Thus, the accumulation of changes in the cAMP signaling pathway upon increasing learning experience with mated females might lead to the increasing potentiation of MBONs-γ2α'1 and MBONs-β'2mp after learning. Interestingly, MBONs-γ2α'1 and MBONs-β'2mp display distinct temporal activity patterns upon learning which likely reflects their distinct neuronal properties (Lei, 2022).

This study reveals a circuit mechanism that ensures that only persistent, and thus likely significant, learning experiences generate post-learning sleep to consolidate LTM. Recent findings suggest that dFBs, involved in sleep homeostasis, might mediate a paradoxical type of sleep, in humans also called Rapid Eye Movement (REM) sleep. This in conjunction with the current data, provide an opportunity to investigate whether the post-learning sleep, mediated by vFBs, might represent another type of sleep implicated in mammals in memory consolidation (Lei, 2022).

Dynamic functional connectivity within brain circuits requires coordination of intercellular signaling and intracellular signal transduction. Critical roles for cAMP-dependent protein kinase A (PKA) signaling are well established in the Drosophila mushroom body (MB) learning and memory circuitry, but local PKA activity within this well-mapped neuronal network is uncharacterized. This study use an in vivo PKA activity sensor (PKA-SPARK) to test spatiotemporal regulatory requirements in the MB axon lobes. Immature animals had little detectable PKA activity, whereas postcritical period adults showed high field-selective activation primarily in just 3/16 defined output regions. In addition to the age-dependent PKA activity in distinct α'/β' lobe nodes, females show sex-dependent elevation compared with males in these same restricted regions. Loss of neural cell body Fragile X mental retardation protein (FMRP) and Rugose [human Neurobeachin (NBEA)] suppresses localized PKA activity, whereas overexpression (OE) of MB lobe PKA-synergist Meng-Po (human SBK1) promotes PKA activity. Elevated Meng-Po subverts the PKA age-dependence, with elevated activity in immature animals, and spatial-restriction, with striking γ lobe activity. Testing circuit signaling requirements with temperature-sensitive shibire (human Dynamin) blockade, broadly expanded PKA activity was found within the MB lobes. Using transgenic tetanus toxin to block MB synaptic output, greatly heightened PKA activity was found in virtually all MB lobe fields, although the age-dependence is maintained. It is concluded spatiotemporally restricted PKA activity signaling within this well-mapped learning/memory circuit is age-dependent and sex-dependent, driven by FMRP-Rugose pathway activation, temporally promoted by Meng-Po kinase function, and restricted by output neurotransmission providing network feedback (Sears, 2022).

This study explored the spatiotemporal regulation of PKA activity within the MB lobes. PKA signaling initiates in early adulthood, with heightened activity in just 3/16 MB lobe output neuron fields (α'1, β'1, and β'2ap). In addition to age-dependence, this regional PKA signaling displays sex-dependence, with elevation in females over males. These findings were made possible with the PKA-SPARK biosensor; this fluorescent reporter uses motifs found in earlier PKA sensors, with pharmacological and genetic approaches promoting and preventing PKA activity verifying this new tool in cell culture and in vivo. While it is possible that the PKA-SPARK reporter is revealing only the strongest PKA activity, at the least α'1, β'1, and β'2ap connectivity regions have much higher PKA activity levels compared with the rest of the MB neuropil. In two disease models of intellectual disability and ASDs, from loss of either FMRP or Rugose/NBEA, PKA activity remains spatiotemporally restricted, but is dramatically reduced. There is surprisingly little change from overactivation of the FMRP→Rg control pathway, but PKA activity is profoundly altered by the PKA pathway Meng-Po kinase, with OE enhancing spatiotemporal PKA signaling and loss suppressing PKA activity. Network feedback downstream of KC neurotransmission strongly suppresses PKA activity, since blocking KC synaptic output with conditional shibire^ts or transgenic TNT induces widespread PKA activity signaling. Thus, localized PKA activity is highly regulated at the circuit level (Sears, 2022).

At a macro level, the α'1 and β'1 regions with the highest localized PKA activity levels have been linked to valence, i.e., whether local activation causes animals to approach or avoid a stimulus. The α'1 and β'2 fields exhibit opposing output valence (positive and negative, respectively), while the β'1 role appears less clear. However, higher PKA signaling can be activated in the γ3 region (via Meng-Po), which in combination with the β'1 region drives positive valence. Moreover, dopaminergic and serotonergic biosensor signals function through the β' region from a variety of external sense stimuli, to which the α'1/β'1 fields display high sensitivity, suggesting broad responsiveness. Very recent work shows high spontaneous activity in α'/β' restricted to young animals , suggesting α'/β' PKA signaling may be controlled by selective developmental activity. Moreover, inhibiting miR-92a in α/β and γ, but not α'/β', was shown to enhance memory, indicating another layer of lobe-selective circuit regulation. Note that the output neurons from these MB lobe regions are different (α'1 cholinergic, β'1 GABAergic, and β'2 glutamatergic), suggesting more complex integrative circuit functions. PKA signaling activation after the early-use critical period is consistent with sensory experience-dependent regulation. Importantly, MB sensory integration functions differ markedly between females and males, correlating with the report in this study of sex-dependent PKA signaling differences in females compared with males (Sears, 2022).

Two learning and memory proteins, FMRP and Rugose, are needed for full PKA activity in the α'1 and β'1 MB lobe output neuron fields. RNA-binding FMRP is a translational regulator known to facilitate PKA signaling, which is lost in the FXS, the commonest heritable cause of intellectual disability and ASD. Rugose/NBEA is a PKA-anchor that facilitates learning and memory, and is also associated with ASDs. Previous work has shown FMRP binds to rugose mRNA to drive KC expression. As predicted, disruption of this FMRP→Rg regulative pathway strongly impairs PKA activity in the MB lobes. In contrast, localized PKA signaling is dramatically strengthened by MB OE of the Meng-Po kinase, which induces early-onset PKA activity before adult sensory experience, spatially expands high PKA activity to the γ3 MBON field, and profoundly elevates PKA activity within all the normal MB lobe regions of heightened PKA signaling. Consistently, Meng-Po kinase OE also greatly improves learning and memory, via PKA phosphorylation, but additionally via signaling feedback synergy. Moreover, meng-po RNAi causes the opposite result of reducing localized PKA-SPARK puncta. Based on both loss and gain of function, it is suggested that Meng-Po enhances localized PKA activity, reflecting circuit level kinase regulation. Determining how Meng-Po-regulated PKA activity determines circuit excitability and regional balance will be a major subject of future research (Sears, 2022).

Two different KC synaptic output blocking methods dramatically expand PKA activity signaling in the MB. Both conditional shibire^ts and transgenic tetanus toxin tools block KC neurotransmission, but through quite different mechanisms. At 33°C, KC-targeted shibire^ts drives PKA activity expansion in the MB γ lobe. This change could indicate cross-compartment network interactions between the γ lobe and other MB regions. Increasing γ1 PKA activity is especially interesting, as γ1 toggles inhibition of other MB regions. The tetanus toxin protease blocks neurotransmission through eliminating SV exocytosis, and therefore provides a stronger and more selective means to silence KC synaptic output. Consistently, TNT animals show a more profound expansion of PKA activity throughout the MB lobes, albeit again affecting only spatial and not temporal patterning. Neither shibire^ts shibire^ts nor TNT blockade alters early PKA activity, suggesting induction of PKA signaling is determined primarily by later experience-driven activity. Localized PKA activity changes with KC output block implies active circuit balance; for example, weighing aversive versus attractive responses to sensory stimuli. The widespread PKA activity upregulation with KC output block leads to a hypothesis that enhancing KC neuron activity should result in elevated PKA signaling (Sears, 2022).

At the MB circuit level, multiple candidate synaptic pathways need to be explored for roles in local PKA activity regulation in different MB lobe output neuron fields. GABAergic inputs to dopaminergic neurons are one likely candidate, since GABA treatment has been shown to correct dfmr1 mutant circuit defects exacerbated by glutamate exposure. Moreover, GABAergic anterior paired lateral (APL) neurons broadly control MB activity through feedback to the KCs, and are most strongly activated by the α'/β' lobes. Recent work shows that treatment with a dopamine transport inhibitor also ameliorates rugose mutant social interaction and memory deficits. Another candidate is the Amnesiac neuropeptide from the serotonergic dorsal paired medial neurons, required for their normal development in the broad innervation of the MB lobes. In the context of these studies, de facto depression may feedback onto KCs to promote PKA activity signaling. The impact of upstream input onto the MB lobes is an important consideration, including how this circuitry combines with spontaneous MB activity and internal lobe circuitry to determine PKA signaling. Future research directions should attempt to dissect how these different layers of neuromodulation control localized PKA activity signaling within the MB lobe circuit, and between females and males, by manipulating input-specific neuronal activity in targeted transgenic studies (Sears, 2022).

In conclusion, this study reports that PKA activity signaling in the Drosophila brain MB learning and memory center is highly induced during early experiential adulthood, with selective upregulation in the α'1, β'1, and β'2ap MB lobe output neuron regions. Age-specific and sex-specific PKA signaling controlled within KCs and downstream of KC output shows that spatiotemporally restricted MB lobe PKA activity is regulated through a combination of both intracellular control and intercellular network-level mechanisms. Importantly, PKA signaling can be precociously promoted and spatial expanded though the activity of the PKA-synergist Meng-Po kinase. Moreover, KC neurotransmission inhibits localized PKA signaling within the MB circuit. Future studies will be aimed toward generating new genetic responder tools to test KC signaling with both neurotransmission output blockade and activity promotion of upstream and downstream MB circuit components, simultaneously and independently of KCs. PKA activity signaling will be tested with the manipulation of specific KC partners, by altering neurotransmission signaling in combination with postsynaptic neurotransmitter receptor mutants to determine network communication cues. Taken together with the current work, these ongoing studies will continue to expand understanding of circuit-level PKA signaling regulation in normal function and in neurologic disease model contexts (Sears, 2022).

Associative brain centers, such as the insect mushroom body, need to represent sensory information in an efficient manner. In Drosophila melanogaster, the Kenyon cells of the mushroom body integrate inputs from a random set of olfactory projection neurons, but some projection neurons-namely those activated by a few ethologically meaningful odors-connect to Kenyon cells more frequently than others. This biased and random connectivity pattern is conceivably advantageous, as it enables the mushroom body to represent a large number of odors as unique activity patterns while prioritizing the representation of a few specific odors. How this connectivity pattern is established remains largely unknown. This study tested whether the mechanisms patterning the connections between Kenyon cells and projection neurons depend on sensory activity or whether they are hardwired. A large number of mushroom body input connections were mapped in partially anosmic flies-flies lacking the obligate odorant co-receptor Orco-and in wild-type flies. Statistical analyses of these datasets reveal that the random and biased connectivity pattern observed between Kenyon cells and projection neurons forms normally in the absence of most olfactory sensory activity. This finding supports the idea that even comparatively subtle, population-level patterns of neuronal connectivity can be encoded by fixed genetic programs and are likely to be the result of evolved prioritization of ecologically and ethologically salient stimuli (Hayashi, 2022).

Dopaminergic neurons with distinct projection patterns and physiological properties compose memory subsystems in a brain. However, it is poorly understood whether or how they interact during complex learning. This study identified a feedforward circuit formed between dopamine subsystems and showed that it is essential for second-order conditioning, an ethologically important form of higher-order associative learning. The Drosophila mushroom body comprises a series of dopaminergic compartments, each of which exhibits distinct memory dynamics. A slow and stable memory compartment can serve as an effective 'teacher' by instructing other faster and transient memory compartments via a single key interneuron, which was identified by connectome analysis and neurotransmitter prediction. This excitatory interneuron acquires enhanced response to reward-predicting odor after first-order conditioning and, upon activation, evokes dopamine release in the 'student' compartments. These hierarchical connections between dopamine subsystems explain distinct properties of first- and second-order memory long known by behavioral psychologists (Yamada, 2023).

To identify and memorize discrete but similar environmental inputs, the brain needs to distinguish between subtle differences of activity patterns in defined neuronal populations. The Kenyon cells (KCs) of the Drosophila adult mushroom body (MB) respond sparsely to complex olfactory input, a property that is thought to support stimuli discrimination in the MB. To understand how this property emerges, the role was explored of the inhibitory anterior paired lateral (APL) neuron in the input circuit of the MB, the calyx. Within the calyx, presynaptic boutons of projection neurons (PNs) form large synaptic microglomeruli (MGs) with dendrites of postsynaptic KCs. Combining electron microscopy (EM) data analysis and in vivo calcium imaging, it was shown that APL, via inhibitory and reciprocal synapses targeting both PN boutons and KC dendrites, normalizes odour-evoked representations in MGs of the calyx. APL response scales with the PN input strength and is regionalized around PN input distribution. These data indicate that the formation of a sparse code by the KCs requires APL-driven normalization of their MG postsynaptic responses. This work provides experimental insights on how inhibition shapes sensory information representation in a higher brain centre, thereby supporting stimuli discrimination and allowing for efficient associative memory formation (Prisco, 2021).

Sleep is a fundamental behavioral state important for survival and is universal in animals with sufficiently complex nervous systems. As a highly conserved neurobehavioral state, sleep has been described in species ranging from jellyfish to humans. Biogenic amines like dopamine, serotonin and norepinephrine have been shown to be critical for sleep regulation across species but the precise circuit mechanisms underlying how amines control persistence of sleep, arousal and wakefulness remain unclear. The fruit fly, Drosophila melanogaster, provides a powerful model system for the study of sleep and circuit mechanisms underlying state transitions and persistence of states to meet the organisms motivational and cognitive needs. In Drosophila, two neuropils in the central brain, the mushroom body (MB) and the central complex (CX) have been shown to influence sleep homeostasis and receive aminergic neuromodulator input critical to sleep-wake switch. Dopamine neurons (DANs) are prevalent neuromodulator inputs to the MB but the mechanisms by which they interact with and regulate sleep- and wake-promoting neurons within MB are unknown. This study investigated the role of subsets of PAM-DANs that signal wakefulness and project to wake-promoting compartments of the MB. PAM-DANs were found to be GABA responsive and required GABA(A)-Rdl receptor in regulating sleep. In mapping the pathways downstream of PAM (protocerebral anterior medial) neurons innervating γ5 and β'2 MB compartments it was found that wakefulness is regulated by both DopR1 and DopR2 receptors in downstream Kenyon cells (KCs) and mushroom body output neurons (MBONs). Taken together, this study has identified and characterized a dopamine modulated sleep microcircuit within the mushroom body that has previously been shown to convey information about positive and negative valence critical for memory formation. These studies will pave way for understanding how flies balance sleep, wakefulness and arousal (Driscoll, 2021).

The mushroom body lobes are tiled by discrete anatomic compartments defined by the axons of a specific subset of DANs and the dendrites of one or two mushroom body output neurons (MBONs). This anatomical arrangement positions DANs to strategically convey positive and negative reinforced information by changing the synaptic weight of KC-MBONs in producing aversive and appetitive responses (Driscoll, 2021).

While, the most in-depth analysis of these synapses and distinct DAN-KC-MBON connectivity and behavioral output comes from studies of olfactory conditioning, there is evidence that these synapses play a critical role in innate behaviors like feeding and sleep. Although, role of DA on sleep has been extensively investigated in Drosophila, the commonly used TH-Gal4 driver line labels most dopamine neuron clusters, but is absent from the several PAM clusters that projects to MB (Driscoll, 2021).

This study specifically probed PAM subsets that project to γ5, γ4, and β'2 MB compartments. This study focused on this subset because KCs and MBONs downstream of these PAM neurons can be neuroanatomically resolved and have been shown to be required for wakefulness. Further, KCs and MBONs that form the γ5, γ4, and β'2 synaptic compartments alter their spontaneous neural activity in response to sleep need (induced by mechanical sleep-deprivation). The ability to use cell-specific split-GAL4 tools provides opportunity to resolve the precise circuit mechanisms by which PAM neurons regulate wakefulness (Driscoll, 2021).

GABA signaling also modulates sleep and wake microcircuits within MB. The key source of GABA in the MB is anterior paired lateral neurons, APL and dorsal paired medial neurons (DPM), which are electrically coupled and increase sleep by GABAergic inhibition of wake-promoting KCs. In the context of associative learning, there is strong evidence for interactions between KCs, APL, DPM and DANs but it is not clear if GABA and dopamine signaling represent opposing inputs to the KCs and MBONs in the regulation of sleep. This study found that the excitability of PAM DANs involved in wakefulness is blocked by sleep-promoting GABA signaling and mediated by ionotropic receptor subtype GABAA-Rdl (Driscoll, 2021).

A recent study showed that GABA inhibitory input to the presynaptic terminals of the PAM neurons regulates appetitive memory and that this interaction is mediated by GABA-B3 receptors that are clustered in PAM boutons localized to PAM-γ5 and -α1 compartments. These data are consistent with the findings that PAM-γ5 are GABA responsive and that multiple receptors are critical to this interaction. Since, no role was found for GABA-B3 in PAM mediated sleep regulation, it is likely that PAM γ5, γ4, and β'2 express multiple GABA receptors which are differentially recruited in sleep and learning. How and what regulates the expression of these receptors in PAM subsets presents a potential mechanism of presynaptic gating to MB core circuits. Transcriptomic analysis of PAM neurons reveals extremely high levels of Rdl expression followed by GABA-B3. Among the PAM subsets mean TPM or transcripts per million of Rdl receptor in PAM γ5, γ4, and β'2 are much higher as compared to other PAM subsets (Driscoll, 2021).

Simple connection query search of the recently released hemibrain data85 reveals there is significant bidirectional connectivity between APL, DPM, and PAM neurons. Further, a recent study showed that APL neurons express the inhibitory D2R receptor55. APL mediated GABAergic inhibition of the PAM neurons was recently shown to control the intensity and specificity of olfactory appetitive memory but previous results show that blocking GABA release from APL neurons only modestly affects sleep phenotypes (Driscoll, 2021).

While, the role of APL in GABA signaling to PAM γ5, γ4, and β'2 cannot be completely ruled out, other inputs to wake-regulating PAM DANs could also be GABAergic and critical for promoting sleep. A recent study using EM dataset of a Full Adult Female Fly Brain (FAFB) mapped the inputs and outputs of the PAMγ5 DANs and identified that this cell type is highly heterogenous and in addition to recurrent feedback from MBON01 γ5β'2a, it receives extensive input from other MBONs, sub-esophageal output neurons (SEZONs) and lateral horn output neurons86. The EM data also reveals that octopaminergic neurons synapse onto PAM γ5, γ4, and β'2 DANs. Whether, these inputs play a role in wakefulness is unknown but suggests that the PAMγ5 could serve as a key link between sensory inputs, wake-promoting octopamine signal and core sleep regulating circuitry within the MB. Each of these inputs could modulate PAM-DAN activity and dopamine release in regulating wakefulness via the MB (Driscoll, 2021).

In addition to probing the release and activity of these PAM-DANs the dopamine receptors and their location within the MB in signaling wakefulness were also explored. To this end validated RNAi lines were expressed in subsets of KCs and MBONs; DopR1 and DopR2 were found to be critical in mediating the wakefulness signal via KCs and γ5β'2 MBONs. Knocking down the receptor consistently increased total sleep and bout length. Furthermore, specific manipulations of DopR receptors within the MB did not directly alter locomotor activity as observed by manipulation of these receptors in CX. Although, loss-of-function mutations of D1 dopamine receptor DopR are shown to enhance repetitive air puff startle-induced arousal and increase sleep. Expression and restoration of DopR in the mutant background specifically in the central complex rescues the startle response, while, the sleep phenotype is rescued via a broad MB driver. The current data extends these findings by showing that the DopR receptors regulate sleep via the MB γ5 and β'2 compartment. Although, targeted RNAi experiments show that DopR's are required for sleep regulation by KCs and MBONs, the lack of a sleep phenotype in DopR2 mutant could be a result of global loss of receptor in the mutant as opposed to targeted loss of receptor function within MB. Dopamine signals wakefulness by activation of wake-promoting neurons of MB via DopR1 and DopR2 and within. the central complex, neurons of dFB are inhibited by dopamine via DopR2. Hence, DopR2 has opposing effects within MB and CX (Driscoll, 2021).

In vitro characterization indicates that DopR's signal through distinct G-proteins, with DopR1 via Gαs to stimulate cAMP production and DopR2 coupling to Gαq via increased calcium. These receptors are thought to have differential sensitivity to dopamine and could be potentially recruited by varying DA release or DAN activity. In the context of sleep regulation, this work reveals that both DopR1 and DopR2 induce wakefulness via the γ5 β'2 MB compartment but not γ4 compartment. Although, chronic activation of PAM γ4 induces wakefulness, the glutamatergic MBON γ4 < γ1,2 projects to multiple compartments and could potentially activate or inhibit MBONs and PAMs projecting to γ1 and γ2 compartment. The interaction between compartments is not well understood in the context of sleep and wake regulation and requires further investigation to better understand the role of DopR2 in regulating the γ4 compartment. The neuroanatomical specificity obtained from split-Gal4 lines combined with EM data has paved way for more detailed analysis of the role of dopamine signaling to MB in the context of sleep and other behaviors (Driscoll, 2021).

The sleep-regulating PAM DANs and associated KCs and MBONs identified in this study are also involved in mediating satiety, novelty, caffeine induced arousal, punishment and reward associated experiences suggesting that the activity of these neurons is tuned to several wake and arousal associated behaviors. This is further supported by the EM connectome data showing that MB receives extensive gustatory, auditory and visual input in addition to olfactory input (Driscoll, 2021).

Current models of sleep regulation rely on two main processes, the circadian clock and the sleep homeostat and don't completely account for multiple external and internal factors that influence wakefulness. The ability to sleep, however, is influenced by motivational or cognitive stimuli. It is therefore envisioned that sleep, wakefulness and arousal within MB are not located in distinct circuits, but rather mediated by distinct processes within a common circuit (Driscoll, 2021).

Animal behavior is shaped both by evolution and by individual experience. Parallel brain pathways encode innate and learned valences of cues, but the way in which they are integrated during action-selection is not well understood. This study used electron microscopy to comprehensively map with synaptic resolution all neurons downstream of all Mushroom Body output neurons (encoding learned valences) and characterized their patterns of interaction with Lateral Horn neurons (encoding innate valences) in Drosophila larva. The connectome revealed multiple convergence neuron types that receive convergent Mushroom Body and Lateral Horn inputs. A subset of these receives excitatory input from positive-valence MB and LH pathways and inhibitory input from negative-valence MB pathways. Functional connectivity from LH and MB pathways and behavioral roles of two of these neurons was confirmed. These neurons encode integrated odor value and bidirectionally regulate turning. Based on this it is speculated that learning could potentially skew the balance of excitation and inhibition onto these neurons and thereby modulate turning. Together, this study provides insights into the circuits that integrate learned and innate valences to modify behavior (Eschbach, 2021).

Selecting whether to approach or avoid specific cues in the environment is essential for survival across the animal kingdom. Many cues have both innate valences acquired through evolution and learned valences acquired through experience that can guide action selection. Innate and learned valences are thought to be computed in distinct brain areas, but the circuit mechanisms by which they are integrated and by which learned valences can override innate ones are poorly understood. Using the tractable Drosophila larva as a model system, this study describes with synaptic resolution the patterns of convergence between the output neurons of a learning center (the MB) and an innately attractive pathway in the lateral horn (the LH). 62 neurons per brain hemisphere were identified that represent direct points of convergence between the MB and the LH, that fall into a number of different subtypes based on their patterns of MB and LH inputs and potentially encode a number of distinct features. One subtype of 10 convergence neurons (CNs) receives excitatory input from positive-valence MB and LH pathways and inhibitory input from negative-valence MB pathways. Functional connectivity from LH and MB pathways and behavioral roles of two of these neurons were confirmed. These neurons encode integrated odor value (coding for positive value with an increase in their activity) and regulate turning. They are activated by an attractive odor, and when activated they repress turning. Conversely, when inactivated, they increase turning. Based on this, it is speculated that learning could potentially skew the balance of excitation and inhibition onto these neurons and thereby modulate turning. For one of these neurons, this study indeed verified that aversive learning skews inputs towards inhibition. Together, this study provides insights into the circuit mechanisms by which learned valences could interact with innate ones to modify behavior (Eschbach, 2021).

The brain areas that compute innate and learned valences of stimuli interact with each other, but despite recent progress, their patterns of interaction are not fully understood. In principle, MBONs could synapse directly onto LHNs thereby directly modifying innate valences. Alternatively, LHNs could directly synapse onto MBONs. Finally, learned and innate valences could initially be kept separate, and MBONs and LHNs could converge on downstream neurons. This study found that in Drosophila larva (1) some MBONs synapse directly onto some LHNs; (2) some MBONs received direct synaptic input from LHNs and (3) many MBONs and LHNs converge onto downstream CNs, similar to findings in the adult. One MBON (m1) was also a CN, receiving significant direct input from other MBONs and from LHNs. Overall, the architecture suggests some early mixing of representations of innate and learned valences, but to some extent these representations are also kept separate in the LH and MB, and then integrated by the downstream CNs. Maintaining some initial separation of representations of innate and learned valences prior to integration could offer more flexibility and independent regulation, for example by context or internal state. Convergence neurons could compute learned valence by comparing odor-drive to positive- and negative-valence MBONs (Eschbach, 2021).

The prevailing model of MB function in adult Drosophila proposes that in naive animals the odor drive to positive- and negative-valence MBONs is equal such that their outputs cancel each other out and the LH circuits guide olfactory behavior. Learning alters the overall output towards positive- or negative-valence MBONs by modifying specific KC-to-MBON connections. This model raises several important questions. First, how is the output from MBONs of opposite valence integrated to compute a learned valence? Second, how does it interact with the output of the LH. The current findings provide further support for this model and shed insight into these questions (Eschbach, 2021).

EM reconstruction combined with neurotransmitter information has revealed a class of 10 CNs that receive excitatory input from positive-valence MBONs and inhibitory input from negative-valence MBONs. These CNs are well poised to compute the learned odor valence by comparing the odor drive to positive- and negative-valence MBONs (Eschbach, 2021).

For two members of this class (MBON-m1 and CN-33), their MB drive was tested in untrained larvae, in vivo or in explants. On average, across the population of untrained individuals, KC activation did not induce a significant change in ∂F/F0 in either CN. However, it was found that in some individuals the MB drive onto the CNs was excitatory, and in others inhibitory, indicating that the MB can provide both excitatory and inhibitory drive to the CNs. Consistently, when inhibitory neurotransmission was blocked using PTX, MBON-m1 and CN-33 showed strong excitatory responses to KC activation (Eschbach, 2021).

High variability was observed in the responses of both CN-33 and MBON-m1 both to activation of the whole MB pathway, as well as to odor presentation. This variability may in part be due to technical reasons. Indeed, the variable responses to pan-KC optogenetic activation could be due to differences in CsChrimson expression in KCs across individuals (the expression of CsChrimson was verified for each individual recording but it was not quantified). However, such a difference is unlikely to be a reason for a range of responses that spans inhibition to excitation following the stimulation of the same neuronal population. Likewise, the exposure of the sensory organ to an odor may vary from one animal to the other depending on the location of the larva's head in the chip's channel. Together with other technical aspects, this may account for some of the variance observed in in vivo. Indeed, a decomposition of the variance within the datasets by ANOVA revealed a significant effect of the identity of the individual on the variance. Interestingly though, the inter-individual variability contributed to a higher fraction of the overall variance in odor response in larvae with functional MBs than in larvae with silenced MBs. This analysis suggests that the MB pathway is a significant source of variability in odor-evoked responses in MBON-m1 and CN-33 and is consistent with the highly variable responses of these CNs to direct KC activation. The variability in response of CNs to MB inputs across untrained individuals could be due to different experiences prior to these experiments, as suggested also by the fact that MBON-m1 responses to trained odors are modified with training. High variability in MBON responses to odor across individuals has previously also been observed in the adult flies and has been related to different individual experiences. The MBs can also compute other kinds of information, such as internal state which may modulate an individual's disposition toward sensory stimuli. Therefore, the average response across untrained individuals might be similar to the response of a single individual in a naive neutral state (with any interindividual differences averaged out across a population), and the variability may represent the degree of freedom for the MB to tune this response to a stimulus depending on previous experience or state (Eschbach, 2021).

How do the learned valences modify the innate ones? How is conflict between opposite innate and learned valences resolved during action selection? One possibility could involve the integration of conflicting signals into a unified representation of value, a notion similar to common currency valuation of options, which could then be used to promote or suppress specific actions. The findings suggest that the 10 CNs that can read out the learned valence by comparing MBON inputs of opposite valence also integrate the learned valence with the innate one. Thus, the 10 CNs that receive inhibitory input from negative-valence MBONs and excitatory input from positive-valence MBONs, also receive inputs from a positive-valence LHN pathway. These neurons are therefore well poised to compute an integrated odor value and code for a positive value with an increase in their activity. For two members of this class (MBON-m1 and CN-33) it was possible to confirm these predictions. This study has shown they are activated by an innately attractive odor via the LH pathway in untrained animals. Furthermore, when the innate attractiveness of an odor was reduced through aversive training, the activity of MBON-m1 was also reduced. Finally, it was shown that activating MBON-m1 and CN-33 represses turning, further supporting the idea that their activation encodes positive value. Interestingly, it was also found that a decrease in their activity promotes turning, raising the possibility that they could bi-directionally encode value, coding for negative value with a decrease in their activity (Eschbach, 2021).

In principle, a single CN of this type could potentially be sufficient to compute integrated odor value and regulate turning, but a population of 10 CNs with similar patterns of input from positive- and negative-valence pathways was found that likely operate partially redundantly with each other. Each CN also had unique aspects of connectivity, raising the possibility that they may also encode partially complementary features and that the integrated value could be distributed across the CN population (Eschbach, 2021).

Based on these findings the following model is proposed that could explain the way in which learning could modulate innate responses to odors through a population of integrative CNs: (1) In naive animals, the CNs are activated by innately attractive odors, mainly via the LH pathway (3) when activated these neurons repress turning, which enables crawling towards an attractive odor source; (3) in naive animals the net MB drive onto these CNs is close to 0; (4) aversive learning can skew the net MB output onto CNs towards inhibition, so that aversively-conditioned odor fails to activate these neurons; (5) if CNs are not activated, turning rate remains high when crawling towards the odor; (6) without sufficient suppression of turning by an odor, the animals' ability to approach the odor source is impaired. This proposed model could explain how aversive learning suppresses approach of innately attractive odors. To fully suppress approach, multiple CNs of this class would likely need to be silenced (Eschbach, 2021).

The model presented above could readily be extended to explain how appetitive learning could enhance odor approach and how strong aversive learning could switch innate odor approach to learned avoidance. Thus, appetitive learning could skew the net MB drive towards excitation thereby repressing turning even more. In contrast, strong aversive learning could skew the conditioned odor drive towards inhibitory negative-valence MBONs so much that the inhibition would become stronger than the excitatory LH drive. The CN would then be inhibited by the aversively conditioned odor. Since inhibition of CNs promotes turning, the aversively conditioned odor could promote odor avoidance by inducing turning. Consistent with this idea, inhibitory responses to the innately attractive odor were observed in MBON-m1 and in CN-33, in some untrained individuals, and strong excitatory responses in others, proving that odor drive onto CNs can range all the way from strong excitation to inhibition. Inhibition of MBON-m1 following aversive learning in the microfluidic device was not observed, but this could be because the animals did not form a very strong aversive memory under these conditions. Testing these proposed extensions of the model will require the future development of automated single-animal training assays and calcium imaging tracking microscopes to correlate the strength of the learned behavior with the conditioned-odor response of the CNs (Eschbach, 2021).

Finally, EM reconstruction did reveal a potentially opposite population of CNs that are inhibited by MBONs of positive value. It will be interesting to test in the future whether these neurons potentially encode negative value with an increase in their activity and positive value with a decrease in their activity and regulate turning in the opposite way: promoting turning when they are activated and repressing it when they are inhibited. Having two different populations of neurons that encode value in opposite ways could further increase the dynamic range of a distributed value code (Eschbach, 2021).

This study found that many of the CNs that receive input from both MBONs and LHNs also provide direct (including CN-33) or indirect (including MBON-m1) feedback to MB modulatory neurons that provide teaching signals for learning (n = 18). In a former study, it was shown that at least some of these feedback connections are functional and can influence memory formation. For example, CN-33/FAN-7 is capable of generating an olfactory memory when it is paired with an odor. This type of connectivity is consistent with learning theories that propose that future learning is influenced by predicted value computed based on prior learning. A major role of the CNs discovered in this study may therefore be not only to organize current actions, but also to regulate learning (Eschbach, 2021).

In summary, the comprehensive synaptic-resolution architecture of the circuits downstream of the learning center output neurons presented in this study is a valuable resource for constraining future modelling and function studies of value computation, action selection, and learning (Eschbach, 2021).

The mushroom bodies of Drosophila contain circuitry compatible with race models of perceptual choice. When flies discriminate odor intensity differences, opponent pools of αβ core Kenyon cells (on and off αβ(c) KCs) accumulate evidence for increases or decreases in odor concentration. These sensory neurons and "antineurons" connect to a layer of mushroom body output neurons (MBONs) which bias behavioral intent in opposite ways. All-to-all connectivity between the competing integrators and their MBON partners allows for correct and erroneous decisions; dopaminergic reinforcement sets choice probabilities via reciprocal changes to the efficacies of on and off KC synapses; and pooled inhibition between αβ_c KCs can establish equivalence with the drift-diffusion formalism known to describe behavioral performance. The response competition network gives tangible form to many features envisioned in theoretical models of mammalian decision making, but it differs from these models in one respect: the principal variables-the fill levels of the integrators and the strength of inhibition between them-are represented by graded potentials rather than spikes. In pursuit of similar computational goals, a small brain may thus prioritize the large information capacity of analog signals over the robustness and temporal processing span of pulsatile codes (Vrontou, 2021).

Two-alternative forced-choice tasks, in which a subject must commit to one of two alternatives, sometimes under time pressure and nearly always with uncertain information, are a commonly studied laboratory simplification of real-world decision making. The neural processes that culminate in a binary choice have been compared to the deliberations of a jury before a verdict: neurons, like jurors, gather evidence from witnesses over the course of a trial and then reconcile their divergent views in a majority vote (Vrontou, 2021).

The problem of how neural circuits implement this form of trial by jury has been approached in a range of species, from primates and rodents to fish and flies. A pioneering and influential body of work is built on a two-alternative forced-choice task in which monkeys distinguish directions of motion in a noisy random dot display. Recordings of correlated neuronal activity suggest that motion-sensitive neurons in the middle temporal visual area (MT or V5) provide momentary evidence that is temporally integrated in lateral intraparietal cortex (LIP) before passing an unspecified thresholding mechanism. Although the precise role attributed to LIP is a matter of debate, the principle that ephemeral sensory signals flow into integrators whose fill levels rise to a response threshold appears general; similar arrangements have been inferred to support visual motion discrimination in zebrafish and odor intensity discrimination in the fly (Vrontou, 2021).

In Drosophila, a rate-limiting integration step takes place in a particular group of third-order olfactory neurons. When flies decide on the direction of an odor concentration change, the membrane potentials of Kenyon cells (KCs) in the αβ core (αβ_c) division of the mushroom bodies drift noisily toward action potential threshold, just as accumulating evidence would drift toward a response bound. Consistent with the proposed correspondence of membrane voltage and integrated sensory information, and of action potential and decision thresholds, neurometric functions based on the average timing of the first odor-evoked spikes in the αβ_c KC population can account for the speed and accuracy of the decision-making animal; psychophysical estimates of noise in the decision process match the measured membrane potential noise of αβ_c KCs; and genetically targeted manipulations that alter the latencies of αβ_c KC spikes have the expected impact on reaction times (Vrontou, 2021).

Two functionally separate groups of αβ_c KCs, termed up and down or on and off cells, respond to increases or decreases in odor concentration and can therefore represent the strength of evidence for either of the two alternatives in the choice. This explicit representation of support for each of the competing hypotheses (as opposed to an aggregate representation of the extent to which one hypothesis is favored over the other) suggests that a decision involves a race between two integrators-one built from neurons that accumulate evidence for an increase in odor concentration and another composed of 'antineurons' that do the opposite. Changes in odorant receptor occupancy at the periphery alter the baseline activity of olfactory receptor neurons and the second-order projection neurons (PNs) with which they form receptor-specific glomerular channels. Large odor concentration changes in a channel's preferred direction drive high-frequency transmission from PNs to αβ_c KCs that promotes steep depolarizations to spike threshold and fast, accurate decisions, whereas small concentration changes in the preferred direction, or any change in the null direction, cause only a trickle of synaptic release; shallow, undulating membrane potential rises; and long spike delays that lead to slow, error-prone choices (Vrontou, 2021).

This study examined whether the circuitry downstream of αβ_c KCs is compatible with a model of two competing integrators. Three predictions of such a model were tested. First, to adjudicate the rival hypotheses advocated by on and off αβ_c KCs, mushroom body output neurons (MBONs) sampling the cores of the αβ lobes must listen to both. It is therefore expected that each core-innervating MBON is excited by increases as well as decreases in odor concentration. Second, as an animal learns the rules of the two-alternative forced-choice task-that an increase in odor intensity predicts imminent electric shock, whereas a decrease signals protection-the influence of αβ_c KCs championing the correct choice should be enhanced while that of proponents of the incorrect choice should be diminished. In other words, antagonistic changes are expected in the strengths of connections of on and off αβ_c KCs with the same action selection neurons if evidence for the competing alternatives is accumulated separately. Third, race models become equivalent to a drift-diffusion process-the formalism shown accurately to describe the psychophysics of the decision-making animal-only if they include an element of mutual or pooled inhibition to establish response competition between the integrators. Inhibition is needed to ensure that the integrators are anti-correlated so that evidence for one choice simultaneously counts as evidence against the other. This study therefore predicts the existence of inhibitory interactions between αβ_c KCs (Vrontou, 2021).

The idea that decisions are based on the accumulated spikes of oppositely tuned sensors was born in early attempts to unite psychophysical and neurophysiological measurements under the umbrella of signal detection theory. The recorded spike count distributions of direction-selective units in the monkey's area MT to motion in the preferred or null directions were taken to represent the responses of two neurons-the recorded neuron and its imagined antineuron conjugate-to movement in the neuron's preferred direction. The likely direction of motion can then be inferred as the probability that a draw from the neuron's response distribution yields a larger spike count than a draw from the antineuron's. At minimal motion strengths, when the two distributions are congruent, these odds are even and choices are random, but as the neuron responds ever more vigorously to increasingly coherent motion while the antineuron's response stays flat, the distributions unmix and the probability of a correct choice rises toward one. Comparing the spike counts of two sensors rather than thresholding the output of one removes shared sources of variation and with them the need of adjusting the discrimination threshold to achieve the best separation of the changing response distributions: a neuron-antineuron pair always returns a quantity proportional to likelihood ratio, the optimal hypothesis test. Although opponent sensory channels in one or another guise feature prominently in many decision-making models, their involvement in the brain is unproven: neurons and antineurons owe their status to each other, as inputs to comparator circuits, but these circuits remain uncharacterized (Vrontou, 2021).

This study draws back the curtain on one such circuit in the fly. Changes in odor intensity are registered by pools of on and off αβ_c KCs, which represent the strengths of the accumulated evidence for an increase or decrease in odor concentration. These pools of sensory neurons and antineurons couple to a second layer of neurons and antineurons, the core-innervating MBONs, which bias behavioral intent in opposite ways. Members of both neuronal pools in the sensory layer connect to both types of MBON in the action selection layer via plastic synapses. With two sets of neurons and antineurons and all-to-all feedforward connectivity between them, the comparator circuit allows for approach or avoidance following judgments of upward or downward changes in odor intensity-that is, it comprises neural pathways representing the possible contingencies seen behaviorally. The perceptual decision is won-correctly or incorrectly-by the αβ_c KC pool that reaches spike threshold first, and it is expressed in the behavior instructed by that pool's favored MBON partners (Vrontou, 2021).

Unlike neurons comprising the ON and OFF pathways of motion vision, on and off αβ_c KCs cannot be distinguished and manipulated genetically. This study has therefore exploited the sensitivity of KC-to-MBON synapses to the timing of reinforcement to reveal the convergence of separate on and off channels onto the same MBONs. KC-to-MBON synapses in their ground state exert finely balanced drive on the MBON ensemble, so that votes cast by its members cancel one another as in a hung jury, but experience can shift the synaptic weight distribution and the resulting pattern of MBON activation away from net zero. This study has documented such shifts for the approach-advocating MBON-γ1pedc>αβ: pairing odor on- or offset with electric shock weakens transmission from the on αβ_c KC pool and strengthens transmission from the off αβ_c KC pool (or vice versa), synergistically changing odor preference. The underlying mechanism is a switch from synaptic depression to synaptic potentiation when the order of odor-evoked KC activation and dopaminergic reinforcement is reversed. This mechanism operates at KC connections with all core-innervating MBONs but is likely engaged at different timescales that may reflect sequential memory phases; to demonstrate the mechanism's ubiquity, this temporal sequence was artificially collapsed by photostimulating DANs directly. Within the short time frame of these behavioral experiments, only PPL1-γ1pedc, but not PPL1-α'2α2, shows significant pain responses that modulate its sensitivity to a predictive odor, consistent with the view that PPL1-γ1pedc and its cognate MBON-γ1pedc>αβ represent the core circuit for the storage and expression of short-term aversive memories (Vrontou, 2021).

A crucial element of many neural network models of decision making is inhibitory feedback from a common interneuron pool driven by the competing integrators, which helps to amplify small differences in conflicting sensory evidence until, eventually, one integrator prevails. The response competition circuit this study has delineated contains such an inhibitory element but with the intriguing twist that the key variables are represented by membrane voltages rather than spikes. Analog processing may be a consequence of numerical constraints: if the mushroom bodies lack the neuron numbers needed to approximate continuous quantities with discrete-time action potentials, there may be little choice but to swap the advantages regenerative spikes could provide (such as long time windows for adding and retaining sensory evidence) for the greater information capacity of graded potentials. Perhaps more is different (Vrontou, 2021).

The capacity to use past experience to guide future action is a fundamental and conserved function of the nervous system. Associative memory formation, initiated by the coincident detection of a conditioned stimulus (CS; e.g., odor) and an unconditioned stimulus (US; e.g., sugar reward), leads to a short-lived memory (STM) trace within distinct circuits. Memories can be consolidated into long-term memories (LTMs) through processes that depend on de novo protein synthesis, require structural modifications within the involved neuronal circuits, and might lead to the recruitment of additional ones. Compared with modulation of existing connections, the reorganization of circuits affords the unique possibility of sampling for potential new partners. Nonetheless, only few examples of rewiring associated with learning have been established thus far (Baltruschat, 2021).

The formation and retrieval of olfactory-associative memories in Drosophila require the mushroom body (MB). Within the main MB input compartment, the calyx (MBC), second-order projection neurons (PNs), delivers olfactory information through cholinergic synapses to the intrinsic MB neurons, the Kenyon cells. In the MBC, large, olfactory PN boutons are enwrapped by the claw-like dendrite termini of ∼11 KCs on average, thereby forming characteristic synaptic complexes, the microglomeruli (MGs), which display functional and structural plasticity in adaptation and upon silencing. To start systematically addressing the mechanisms that support memory consolidation, this study sought to investigate the properties of identifiable synaptic MGs in the MB of the adult brain of Drosophila after the establishment of LTMs (Baltruschat, 2021).

Combining behavioral experiments with high-resolution microscopy and functional imaging, this study demonstrates that the consolidation of appetitive olfactory memories closely correlates with an increase in the number of MGs formed by the PNs that deliver the conditioned stimulus and their postsynaptic KC partners. These structural changes result in additional, functional synaptic connections. Thus, the circuit in the calyx of the fly MB reorganizes accompanying the consolidation of associative memories (Baltruschat, 2021).

This study reports input-specific reorganization of the adult MBC circuit associated with the formation of long-term, appetitive memory. By visualizing presynaptic markers in PNs and the KC postsynaptic densities, this study uncovered an increase in the number of PN boutons and, at the same time, reveal that these boutons are enveloped by KC postsynaptic profiles, suggesting that new MGs are formed during memory consolidation. These findings are particularly remarkable, given the high degree of complexity of the MG microcircuits revealed by EM reconstruction and including the dendrite claws of multiple KCs of distinct subtypes. The cellular mechanisms leading to the increased number of odor-specific complex MGs remain to be clarified, but they will require a tight coordination between pre- and postsynaptic partners. In this context, mutations in synaptic proteins or in proteins mediating cell-cell interactions, which specifically block LTM, will be of great interest (Baltruschat, 2021).

It is suggested that remodeling could be driven by intrinsic reactivation of KCs during the consolidation phase or by modulatory inputs into the calyx. In either case, a complex pattern of activation is expected, that might be difficult to reproduce in artificial settings. Although the present observations are limited for technical reasons to the specific case of cVA, the overall density of PN boutons in the MBC increases after appetitive long-term conditioning in honeybees, as well as in leaf-cutting ants after avoidance learning. Based on that and given that the olfactory pathway of cVA is not distinguishable from that of other odors, it is thus suggested that the findings might be generalizable. In comparison with those systems, however, genetic and functional identification of PN subsets were used to reveal that the structural modifications are specific and limited to the PNs conveying the conditioned odor. Importantly, in vivo functional imaging data support the view that the circuit reorganization leads to additional functional MGs responding to the conditioned odor. In addition, they demonstrate a specific change in functional response in the KC dendrites toward the trained odor because the calcium levels drop faster toward baseline after appetitive associative conditioning. The faster decay kinetics and more skewed response toward the onset of the stimulus could contribute to a more-efficient temporal summation of responses or refine the KC response and might be related to inhibitory modifications. An important open question is the effect of the increased number of responding MGs on the pattern of KC activation. KCs respond sparsely to odor input and require the coincident activation of multiples of their claws to produce an action potential. The data might underlie the addition of connections between the active PNs and a set of already-responding KCs, leading to facilitated response to the conditioned odor without changing the set of responding KCs. A recent publication, however, suggests an exciting alternative view. After aversive LTM establishment, the number of KCs responding to the conditioned odor is increased (Delestro, 2020). If it is hypothesized that appetitive conditioning leads to a similar outcome, the data could provide anatomical and functional support to these findings. The pattern of KC response could, thus, be modulated by experience in adulthood and might represent a rich signifier of sensory stimulus and context. Reconstruction of an MG from EM serial sections derived from FAFB dataset (Baltruschat, 2021).

Spatial orientation memory plays a crucial role in animal navigation. Recent studies of tethered Drosophila melanogaster (fruit fly) in a virtual reality setting showed that the head direction is encoded in the form of an activity bump, i.e., localized neural activity, in the torus-shaped ellipsoid body (EB). However, how this system is involved in orientation working memory is not well understood. This study investigated this question using free moving flies (D. melanogaster) in a spatial orientation memory task by manipulating two EB subsystems, C and P circuits, which are hypothesized for stabilizing and updating the activity bump, respectively. To this end, two types of inhibitory ring neurons (EIP and P) which innervate EB were suppressed or activated, and it was discovered that manipulating the two inhibitory neuron types produced distinct behavioral deficits, suggesting specific roles of the inhibitory neurons in coordinating the stabilization and updating functions of the EB circuits. The neural mechanisms underlying such control circuits were further elucidated using a connectome-constrained spiking neural network model (Han, 2021).

Maintaining spatial orientation is a crucial cognitive capability required for animal navigation, and understanding the detailed neural mechanisms of spatial orientation is of great interest to researchers in the fields of neurobiology or neuromorphic engineering. In recent years, significant progress has been made in identifying the neural circuits that support spatial orientation in the central complex of Drosophila melanogaster. The central complex has long been associated with short-term spatial memory, visual pattern memory, and motor control. The recent discoveries of head-direction selectivity and localized neural activity in two central complex neuropils, the ellipsoid body (EB) and the protocerebral bridge (PB), have also linked the central complex to the function of spatial orientation. These studies suggested that the head orientation is encoded by localized neural activity, called activity bump, and the bump location in EB shifts in accordance with changes of heading during movement. The function of the EB neurons resemble that of a compass and is, therefore, termed 'neural compass' (Han, 2021).

In light of these empirical observations, several neural circuit models of the central complex have been proposed to elucidate the neural circuit mechanisms of head-direction selectivity or other functions associated with the central complex. Some models focused on the stability of the activity bump or on the differences in the circuit dynamics between locus and fruit fly. Other models studied the plasticity involved in the flexible retinotopic mapping but used simpler firing rate models or schematic models. A large-scale firing-rate neural network model that covered the entire central complex was able to reproduce the steering and homing behavior of bees, but the EB circuits were rather simple with minimal details (Han, 2021).

Recently, a spiking-neuron model of the EB-PB circuits was proposed. The model used a more realistic spiking-neuron model and synaptic dynamics to elucidate how the circuits can maintain a stable activity bump when fruit flies switch between forward movement and rotation states in the absence of landmarks. The model suggested the involvement of two subcircuits: one forms an attractor network and maintains (or stabilizes) an activity bump; the other forms a shifter network and shifts (or updates) the bump position in accordance with changes in body orientation. The model successfully demonstrated the angular errors when a fly moved in darkness and predicted the asymmetric activity in the PB during rotation (Han, 2021).

The model made an important and unique prediction: the function of spatial orientation working memory requires coordinated activation of the bump-maintaining (or stabilizing) and bump-shifting (or updating) circuits that are controlled by the upstream ring neurons (Han, 2021).

However, most of the experimental studies used tethered flies in a virtual reality setting and focused on how manipulation of neurons affects the bump activity. It is not clear how these neurons, in particular those involved in stabilizing and updating the activity bump, play roles in cognition-relevant behavior such as spatial orientation memory in free-moving flies with a more realistic behavioral setting. This study aimed to address these questions and designed a behavioral task of spatial orientation working memory based on the classic Buridan's paradigm. Specifically, two types of GABAergic ring neurons were manipulated that are hypothesized to control these neurons. Ring neurons project their axons into EB and inhibit neurons including those display the activity bump. Previous studies have reported the roles of the ring neurons in visually-guided behavior, ethanol sensitivity, sleep regulation, olfactory memory and mating behavior. However, their roles in the working memory of spatial orientation in the presence or absence of visual cues remain unclear. In addition to the neural functional experiments, computer simulations were performed using the EB-PB model, which produced neural activities that were consistent with the behavioral changes observed in the fruit flies with different experimental conditions. The present study provides a detailed picture on how coordinated activation between the neural processes of stabilization and update plays a crucial role in spatial orientation working memory (Han, 2021).

It was hypothesized that the C circuit and P circuit in the EB circuits stabilize and update the orientation-encoding activity bump and they are regulated by corresponding GABAergic ring neurons. We tested this hypothesis by manipulating two types of GABAergic ring neurons in a spatial orientation working memory task with free moving fruit flies, and discovered manipulating each ring neuron type led to different behavioral abnormality. By performing computer simulations on a previously proposed EB-PB neural circuit model, it was possible to explain the results of the experiments and provided a picture of the neural circuit mechanism underlying spatial orientation working memory: the orientation-encoding bump is maintained through two alternately activated neural processes: one that stabilizes the position of the activity bump and one that updates the position of the bump. The former is activated when a fruit fly maintains a steady head direction and the latter is activated when the fly rotates its body. The control of this process is performed through specific GABAergic ring neurons. Therefore, overactivating or suppressing the ring neurons disrupts the alternation of the two processes and leads to incorrect or even loss of orientation memory (Han, 2021).

There are a few more interesting discoveries worth discussing. Performing fixation toward previous landmark directions requires two things to be remembered: the earlier event of the landmark presentation (what) and the directions of the landmarks (where). Flies that fail to remember the former would not exhibit the fixation behavior at all, while flies that forget the latter would still perform the fixation but toward incorrect directions. Discoveries of strong fixation but with large deviation from the true directions of the landmarks for flies with EIP-ring neuron suppression during the third stage may imply the segregation of the neural mechanisms of orientation memory regarding the 'where' and 'what' of a landmark (Han, 2021).

One interesting finding of the present study is a long duration of spatial orientation working memory during the poststimulus stage. Previous studies reported the occurrence of poststimulus fixation behavior that lasted only for a few seconds immediately following the offset of the landmarks. Indeed, this study observed that the flies tended to stop their movement a few seconds after the sudden disappearance of the landmarks in the third stage. But they usually resumed the movement in a few seconds. This might be the reason why earlier studies only claimed a few seconds of fixation if their analyses did not include the resumed movement. Further studies are needed to investigate this issue (Han, 2021).

A couple issues regarding the choice of molecular tools should be discussed. In the present study tub-GAL80ts was used in combination with UAS-Kir2.1 or UAS-TNT to suppress targeted ring neurons. The method involves raising the temperature 1 d before the behavioral experiments and therefore taking effects on a much longer time scale than using optogenetic tools. This long-term suppression may induce other effects at the cellular or circuit levels, which are beyond what the model can simulate. Further study may be required to carefully examine the long-term effects. An ideal solution is to transiently suppress the ring neurons using optogenetic tools such as UAS-NpHR (peak sensitivity wavelength ~589 nm) or GtACRs (peak sensitivity wavelength ~ 527 nm for GtACR1 and ~457 nm for GtACR2). However, the wavelength of the required activation light is within the visible range of the fruit flies. Preliminary tests on UAS-NpHR showed that the onset of the activation light seriously disrupted the fixation pattern of wild-type flies. A new optogenetic tool or a carefully re-designed optical system is required to transiently suppress targeted ring neurons while not interfering the visual experiments. The second issue is related to the GAL4 lines. In the present study only two most specific lines, c105-GAL4 and VT5404-GAL4, were used to target the EIP-ring and P-ring neurons, respectively. There are several other less specific GAL4 lines available for the two types of neurons. It is necessary to conduct the same experiments using these overlapping lines to further confirm the results presented in this study (Han, 2021).

Several other important questions remain to be addressed. Previous studies showed that EB does not maintain a fixed retinotopic map and a bump can start from a random location in the beginning of a trial. For the sake of modeling simplicity, this study did not model the random starting point feature in the model. But this feature is easy to implement and does not affect the conclusion of this study. A global excitation needs to be applied to the entire EB to reset the system. The excitation will induce strong competition between the EIP neurons and a new bump will start at a random location through the winner-take-all dynamics. Following this issue, random regeneration of an activity bump also needs to be discussed. The model showed that photoactivation of either EIP-ring or P-ring neurons during the poststimulus stage permanently abolished the activity bump. However, based on the observation of spontaneous generation of activity bump in other studies, the bump is likely to be regenerated at a random location after the offset of photoactivation. The regeneration can be easily implemented in the current model using the same mechanism described above. Since the regenerated bump starts from a random location, the fruit flies lose the reference to the landmark locations. Thus, adding a spontaneous bump or not both lead to the same conclusion: the files fail to fixate on the previous landmark locations. Although not affecting the conclusion of the present study, the spontaneous bump feature may be crucial in future studies that involve modeling of the steering mechanism (Han, 2021).

Another issue is that a couple experimental and modeling studies suggested that ring neurons provides the mechanisms underlying flexible retinotopic mapping in EPG (or EIP) neurons rather than the simple suppression/activation mechanism as hypothesized in the present study. However, the ring neurons (R2 and R4d) tested in one study are of different types from what was tested (R1 and R6) in this study. Additional experiments that measure the activities of R2 and R4d using the setups described in previous studies are required to clarify this issue (Han, 2021).

It is important to compare and discuss differences between computational models of the central complex in terms of the functions investigated in the present study. However, most models focused on different aspects of the compass circuit functions. The major difference from previous models is that they proposed that the PB intrinsic neurons as the main source of inhibition that regulates the attractor dynamics, while in the current model this function is conducted by the EIP ring neurons with two additional ring neuron types (C-ring and P-ring neurons) modulating different subcircuits of the system. An in-depth model comparison and experimental manipulation of PB intrinsic neurons and ring neurons under the present behavioral task may be able to clarify this issue (Han, 2021).

A final issue is related to the function of the activity bump which is commonly thought to represent the fly's sense of orientation in a manner similar to that of the head-direction system found in rodents. However, as aforementioned 'what' and 'where' mechanisms, performing the fixation behavior as an indication of orientation working memory may require several serial or parallel neural components beyond EB and PB. For example, how is this innate fixation behavior initiated (motivation)? When a fly stops fixating, it is not clear whether the fly forgets the landmark directions or simply enters a different behavior state (but still remembers the landmark directions). It also remains unclear whether the memory is stored in another neural circuit and the EB merely provides a reference frame for orientation, or whether the activity bump in the EB represents the actual memory of the landmarks. The current experimental setup is not able to address this issue. A novel task that can disassociate these two components is required for further investigation (Han, 2021).

The present study concludes the following. First, the experiment indicated that long-term suppression of EIP-ring neurons reduced the accuracy of orientation working memory (fixation with an increased deviation angle), whereas long-term suppression of P-ring neurons abolished the memory completely (no fixation). Similarly, transiently activating either ring neuron types in the absence of landmark immediately abolished the memory. Second, the experimental observation can be explained by the EB-PB neural circuit model in which the EIP-ring neurons are responsible for controlling the width of the bump and the P-ring neurons are responsible for shifting (updating) the position of the bump. Third, put the experiment and the theory together, the present study suggests that coordinated activation of the two ring neuron types which control the downstream EB-PB subcircuits is crucial for spatial orientation working memory (Han, 2021).

Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. This study presents behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and two alternative models are suggested for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offers a framework to dissect MBON circuits and interpret DAN activity during olfactory learning (Zhao, 2021).

Predicting the future from sensory input is fundamental for survival. Co-appearing stimuli can be used for improving a prediction, or for predicting important events themselves, as observed in classical conditioning. In fruit fly odor conditioning, an odor that will become the conditioned stimulus (CS), is paired with the unconditioned stimulus (US), in this study an electroshock, that triggers an avoidance behavior and in internal representation of a negative value (valence). After conditioning, and the negative value representation (although not the full unconditioned response) previously elicited by the electroshock will be reproduced by the odor itself. Classical conditioning theories posit that throughout learning the odor becomes predictive for the electroshock. During learning, the prediction error decreases, and learning stops when the predictive odor value matches the strength of the electroshock (Zhao, 2021).

Predictive olfactory learning in fruit flies is a widely recognised concept in the experimental literature, and dopaminergic neurons (DANs) in the mushroom body (MB) have been suggested to predict punishment or reward. Yet, despite the acknowledgment of its predictive nature, computational models on fruit fly conditioning are mostly guided by the formation of associations, a notion that relates more to memories rather than predictions. Similarly, the concept of predictive learning is well recognized for olfactory conditioning in insects in general, but synaptic plasticity models are not formulated in terms of explicit predictions, but rather in terms associations and correlations, with plasticity being driven by two or three factors, each representing a temporal nonlinear function of the pre- or postsynaptic activities or of a modulatory signal, sometimes combined with homeostatic plasticity. This type of associative models exist for fruit flies, locusts or honey bees. They differ from target learning, where the unconditioned stimulus sets a target that is learned to be reproduced by the conditioned stimulus. Target learning becomes predictive learning when including a temporal component. It involves a difference operation, and learning stops when the target is reached. The stop-learning feature is difficult to be reproduced by purely correlation-based associative learning, while a purely predictive model also intrinsically captures associative properties (Zhao, 2021).

Associative learning was suggested to be implemented through spike- or stimulus-timing dependent plasticity (STDP) that would underlie conditioning. STDP strengthens or weakens a synapse based on the temporal correlation between the US (electroshock) and the CS (odor), both on the neuronal time scale of 10s of milliseconds and on the behavioral time scale of 10s of seconds. Whether an association is strengthened by just repeating the pairing until the behavioral saturation is reached, or the association saturates due to a faithful prediction, however, has not been investigated in the fruit fly so far. This study shows that olfactory conditioning in Drosophila is better captured as predictive plasticity that stops when a US-imposed target is reached, rather than by correlation-based plasticity, such as STDP, that does not operate with an explicit error or a target. According to this scheme, it is only the aversive/appetitive value of the US that is predicted by the CS after faithful learning, not the US itself. Based on the common value representation in the mushroom body output neurons (MBONs), the corresponding avoidance/approach reaction as one aspect of the unconditioned response is elicited by the CS alone (Zhao, 2021).

The Drosophila olfactory system represents a unique case for studying associative /predictive learning, and the MB is known to be essential in olfactory learning. The Kenyon cells (KCs) receive olfactory input from olfactory projection neurons and form a sparse representation of an odor. The parallel axons of the Kenyon cells (KCs) project to the MB lobes, along which the compartmentalized dendritic arbors of the MB output neurons (MBONs) collect the input from a large number of KCs. Reward or punishment activates specific clusters of DANs PAM and PPL1, respectively which project to corresponding compartments of the MB lobes, modulating the activity of the MBONs and the behavioral response. Recently, a detailed mapping of the MB connectome has been accomplished for larvae and of the vertical lobe for the adult Drosophila. Several studies show that not only the feedforward modulation from DANs to MBONs, but also the feedback from MBONs to DANs play an important role in olfactory learning (Zhao, 2021).

Previous studies have given insights into the possible cellular and subcellular mechanisms of olfactory conditioning. Yet, the suggested learning rules remain correlation-based and miss the explicit predictive element postulated by the classical conditioning theories. This study presents distinctive conditioning experiments showing that olfactory learning is best explained by predictive plasticity. These experiments, in contrast, could not be reproduced by various types of correlation-based associative learning rules. A mathematical model captures the new and previous data on olfactory conditioning, including trace conditioning. The model encompasses the odor/shock encoding and the learning of the aversive odor value with the stochastic response. It is further suggested how the predictive plasticity could be implemented in the MB circuit, with MBONs encoding the value ('valence') of the odor stimulus, and DANs calculating either the error or the target that drives the KC-to-MBON plasticity. The predictive plasticity rule for the KC-to-MBON synapses is shown to be consistent with the experimental results showing the involvement of these synapses in the novelty-familiarity representation (Zhao, 2021).

This study has reconsidered classical odor conditioning in the fruit fly and presents experimental and modeling evidence showing that olfactory learning, also on the synaptic level, is better described as predictive rather than associative. The key observation is that repetitive and time-continuous odor-shock pairing stops strengthening the conditioned response after roughly 1 minute of pairing, even if the shock intensity is below the behavioral saturation level. During conditioning, the odor is learned to predict the co-applied shock stimulus. As a consequence, the odor-evoked avoidance reaction stops strengthening at a level that depends on the shock strength, irrespective of the pairing time beyond 1 min. Associative synaptic plasticity, defined by a possibly nonlinear function of the CS-US correlation strength, as suggested by STDP models, fails to reproduce the early saturation of learning (Zhao, 2021).

A simple phenomenological model for predictive plasticity is suggested according to which synapses change their strength proportionally to the prediction error. This error is expressed as a difference between the internal shock representation and the value representation of the odor. The model encompasses a description of the shock and value representation, the stochastic response behavior of individual flies, and the synaptic dynamics (using a total of 5 parameters). It faithfully reproduces the conditioning experiments (with a total of 28 data points from 3 different types of experiments) as well as previously studied trace conditioning experiments (without need for further fitting). As compared to the associative rules (Hebbian, linear and nonlinear STDP, covariance rule), the predictive plasticity rule obtained the best fits with the least number of parameters. The model was further compared by the Akaike information criterion that considers the number of parameters beside the fitting quality. This criterion yields a likelihood for the predictive plasticity rule to be the best one that is at least 7 orders of magnitude larger as compared to the other four associative rules considered (Zhao, 2021).

The same phenomenological model of predictive learning may be implemented in two versions by the recurrent MB circuitry. In both versions the MBONs code for the odor value ('valence') that drives the conditioned response. For the error-driven predictive plasticity, the DANs directly represent the shock-prediction error by comparing the shock strength with its MBON estimate, and this prediction error modulates the KC-to-MBON plasticity (see Suggested implementation of error- and target-driven predictive plasticity). For the target-driven predictive plasticity, the DANs represent the shock stimulus itself that is then provided as a target for the KC-to-MBON plasticity. In this target-driven predictive learning, the DANs may also learn to predict the shock stimulus based on the MBON feedback, preventing a fast extinction of the KC-to-MBON memory (Zhao, 2021).

Predictive plasticity for both types of implementation has its experimental support. In general, MBON activity is well recognized to encode the aversive or appetitive value of odors and to evoke the corresponding avoidance or approach behavior, while KC-to-MBON synapses were mostly shown to undergo long-term depression, but also potentiation. DAN responses are shown to be involved in both the representation of punishment and reward that drive the aversive or appetitive olfactory conditioning. This conditioning further involves the recurrent feedback from MBONs to DANs that may be negative or positive. Moreover, the connectome from the larvae and adult fruit fly MBON circuit reveals feedback projections from DANs to the presynaptic side on the KC and the postsynaptic side on the MBONs at the KC-to-MBON synaptic connection giving different handles to modulate synaptic plasticity (Zhao, 2021).

With regard to the specific implementations, the error-driven predictive plasticity is consistent with the observation that DAN activity decreases during the conditioning. The two models have opposite predictions for learning while blocking MBON activity. The error-driven predictive plasticity would yield a higher learning index (LI) while the target-driven predictive plasticity would yield a lower LI. It was also shown that some DANs increased their activity with learning while other DANs, in the same PPL1 cluster that is supposed to represent aversive valences, decreased their activity. In fact, error- and target-driven predictive plasticity may both act in concert to enrich and stabilize the representations. DAN activity would decrease in those DANs involved in error-driven predictive plasticityand increase in those involved in target-driven predictive plasticity (Zhao, 2021).

While error-driven predictive plasticity offers access to an explicit error representation in DANs, target-driven predictive plasticity has its own merits. If DANs and MBONs code for similar information, they can support a positive feedback-loop to represent a short-term memory beyond the presence of an odor or a shock, as it was observed for aversive valences in PPL1 DANs and for appetitive PAM DANs. A positive feedback-loop between MBONs and DANs is further supported by the persistent firing between these cells after a rejected courtship that may consolidate memory of the rejection, linked to a specific pheromone (Zhao, 2021).

Target-driven plasticity has further functional advantages in terms of memory retention time. Any odor-related input to the DANs, arising either through a forward hierarchy from KC48 or a recurrence via MBONs to the DANs, will extend the memory life-time in a 2-stage prediction process: the unconditioned stimulus (s) that drives the DAN activity (d) to serve as a target for the value learning in the MBONs via KC-to-MBON synapses, will itself be predicted in the DANs. Extending the memory life-time through circuit plasticity might be attractive under the light of energy efficiency, showing that long-term memory in a synapse involving de novo protein synthesis can be costly, while cheaper forms of individual synaptic memories likely have limited retention times. Moreover, distributed memory that includes the learning of an external target representation offers more flexibility, including the regulation of the speed of forgetting (Zhao, 2021).

Target-driven predictive plasticity may also explain the novelty-familiarity representation observed in the recurrent triple of KCs, DANs and MBONs. The distributed representation of valences allows for expressing temporal components of the memories. Spontaneous activity in the KCs and their downstream cells injures a minimal strength of the KC-to-MBON synapses through predictive plasticity. A novel odor that drives KCs will then also drive MBONs and, to a smaller extent (as is assumed in this study), also DANs. If the DANs that represent the target for the KC-to-MBON plasticity are only weakly activated by the odor, the KC-to-MBON synapses learn to predict this weaker activity and depress. The depression results in a repetition suppression of MBONs and the corresponding familiarization of the fly to the ongoing odor. However, when the odor is cleared away, the MBON activity induced by spontaneously active KCs via depressed synapses now becomes lower than the spontaneous DAN activity, and predictive plasticity recovers the original synaptic strength. Eventually the spontaneous MBON and DAN activites match again and the response to the originally novel odor is also recovered, as seen in the experiment (Zhao, 2021).

Olfactory learning is likely distributed across several classes of synapses in the MB. The acquisition of olfactory memories was shown to be independent of transmitter release in KC-to-MBON synapses, although the behavioral recall of these memories required the intact transmission. In fact, learning may also be supported by plasticity upstream of the MBONs such that the effect of blocking KC-to-MBON transmission during learning is behaviorally compensated. Predictive plasticity at the KC-to-MBON synapses requires the summed synaptic transmissions across all synapses to be compared with the target d, also during the memory acquisition. This type of plasticity would therefore be impaired by blocking the release (Zhao, 2021).

Distributed learning also offers flexibility in acquiring predictions from new cues. While the original Rescorla-Wagner rule would predict blocking, this has not been observed in the fruit fly. Blocking refers to the phenomenon that, if the first odor of a compound-CS is pre-conditioned, the second odor of the compound will not be learned to become predictive for the shock. Because the predictive plasticity rules are expressed at the neuronal but not at the phenomenological level, predictions about blocking will depend on the neuronal odor representation. If the two odors activate the same MBONs, blocking would be observed since the MBONs are already driven to the correct value representation by the first odor. If they activate different MBONs, however, blocking would not be observed since the MBONs of the second odor did not yet have the chance to learn the correct value during the first conditioning. Hence, since blocking has not been observed in the fruit fly, it is postulated that the odors of the compound-CS in these experiments were represented by different groups of MBONs (Zhao, 2021).

How does this model relate to the concentration-specificity and the timing-specifity of odor conditioning? First, olfactory learning was found to be specific to the odor concentration, with different concentrations changing the subjective odor identity. The response behavior was described to be non-monotonic in the odor intensity, with the strongest response for the specific concentration the flies were conditioned with. It was suggested that this may arise from a non-monotonic odor representation in the KC population as a function of odor intensity. Given such a presynaptic encoding of odor concentrations, the predictive olfactory learning in the KC-to-MBON connectivity would also inherit the concentration specificity from the odor representation in the KCs. This predictive plasticity, and also the Rescorla-Wagner model, further predicts that learning with a higher odor concentration (but the same electroshock strength) only speeds up learning, but would not change the asymptotic performance (Zhao, 2021).

Second, olfactory conditioning was also shown to depend on the timing of the shock application before or after the conditioning odor. While a shock application 30s after an odor assigns this odor an aversive valence, an appetitive valence is assigned if the shock application arises 30s before the odor presentation. Modeling the approaching behavior in the context of predictive plasticity would require duplicating this model to also represent appetitive valences, and the action selection would depend on the difference between aversive and appetitive valences. Inverting the timing of CS and US may explain 'relief learning' if a stopping electroshock would cause a decrease of the target for aversive MBONs and an increase of the target for appetitive MBONs. An odor presented after the shock would then predict the increased appetitive target and explain the relief from pain behavior, similar to the model of relief learning in humans (Zhao, 2021).

Overall, these behavioral experiments and the plasticity model for the KC-to-MBON synapses support the notion of predictive learning in olfactory conditioning, with the DANs representing either the CS-US prediction error or the prediction itself. While predictive coding is recognized as a hierarchical organization principle in the mammalian cortex that explains animal and human behavior it may also offer a framework to investigate the logic of the MB and the multi-layer MBON readout network as studied by various experimental work (Zhao, 2021).

The mushroom body (MB) is a well-characterized associative memory structure within the Drosophila brain. Analyzing MB connectivity using multiple approaches is critical for understanding the functional implications of this structure. Using the genetic anterograde transsynaptic tracing tool, trans-Tango, this study identified divergent projections across the brain and convergent downstream targets of the MB output neurons (MBONs). This analysis revealed at least three separate targets that receive convergent input from MBONs: other MBONs, the fan-shaped body (FSB), and the lateral accessory lobe (LAL). A multilayer circuit is described, both anatomically and functionally, in which inhibitory and excitatory MBONs converge on the same genetic subset of FSB and LAL neurons. This circuit architecture enables the brain to update and integrate information with previous experience before executing appropriate behavioral responses. This use of trans-Tango provides a genetically accessible anatomical framework for investigating the functional relevance of components within these complex and interconnected circuits (Scaplen, 2021).

The MB is a high-level integration center in the Drosophila brain with an established role in learning and memory. The iterative nature of converging and diverging MB neural circuits provides an excellent example of the anatomical framework necessary for complex information processing. For instance, on a rapid timescale, interactions between MB compartments could generate different output patterns to drive behavior, whereas on a slower timescale, interactions between MB compartments could reevaluate memories of a context (Scaplen, 2021).

This study sought to map the projections from the MB using the genetic anterograde transsynaptic technique, trans-Tango. The connectivity of MBONs is reported across multiple subjects in both males and females and the variability in connectivity is highlighted that potentially exists across animals. This study complements the ongoing efforts of EM reconstruction of a whole brain of a single female fruit fly and confirms previous anatomical predictions. Although the complete EM dataset of an adult fly brain has been an invaluable resource that significantly accelerated the mapping of the neural circuits underlying innate and learned behaviors, the massive undertaking of acquiring a full EM dataset renders it impractical to perform for multiple individuals. Thus, trans-Tango, expands the value of the EM reconstruction data by examining circuit connectivity across multiple individuals. Further, trans-Tango can be readily adapted to functional studies in which the activity of the postsynaptic neurons is altered by expressing optogenetic/thermogenetic effectors or monitored by expressing genetically encoded sensors. Tracing studies reported in this study serve as the foundation for these future experiments (Scaplen, 2021).

These studies reveal that the MB circuits are highly interconnected with multiple regions of converging projections both within and downstream of the MB. These experiments also show diverging projections in the downstream postsynaptic targets. A multilayer circuit is identified that includes GABAergic and cholinergic MBONs that converge on the same subset of FSB and LAL neurons. This circuit architecture allows for rapid updating of the online processing of sensory information before executing behavior. Further, this circuit organization is likely a conserved motif among insects (Scaplen, 2021).

Successive levels of convergence and divergence across the brain permit functional flexibility. Like the mushroom body, cerebellar circuits in mammals exhibit large divergence in connectivity, and this can support diverse types of synaptic plasticity. Previous neuroanatomical work in insects described divergent afferent and efferent MB neurons, although the extent of this divergence was unknown. The data revealed varying levels of divergence of postsynaptic connections of MBONs across the brain. Every one of the analyzed MBONs had postsynaptic partners projecting to multiple brain regions. Further, nearly the entire superior protocerebrum as well as portions of the inferior protocerebrum received input from at least one MBON, providing opportunities for comprehensive integration of signals from the MBON network (Scaplen, 2021).

Multiple feedforward and feedback circuits exist within the MB. The current data revealed at least two MBONs that receive convergent input from multiple MBONs and are also reciprocally connected. The convergent MBON input to β'2mp is especially interesting as cholinergic (MBON γ2α'1), GABAergic (MBON γ3β'1), and glutamatergic (MBON γ5β'2a) MBONs drive opposing behaviors. For instance, activation of the cholinergic or GABAergic MBON results in naive odor preference, whereas activation of the glutamatergic MBON results in robust naive avoidance. Similarly, the cholinergic MBON activity mediates aversive associations, whereas glutamatergic MBON activity mediates appetitive associations and extinction of aversive (Scaplen, 2021).

Considering that MBON β'2mp receives convergent input from these parallel and opposing pathways, it likely serves as a decision hub by integrating activity to modulate cue-induced approach and avoidance behavior. How MBON β'2mp integrates information across MBONs and drives behavioral responses remains to be determined. Naive activation of MBON β'2mp does not appear to influence behavioral choice, it instead acts as a sleep suppressor. Inhibition of MBON β'2mp during sleep enhances long-term memory. Separately, local protein synthesis within MBON β'2mp, has been implicated in the consolidation of long-term memory. This makes MBON β'2mp an ideal model for understanding how sleep and memory signals might be integrated at a molecular level. It should be mentioned that MBON γ3β'1 reportedly acts as a sleep activator and local protein synthesis within this MBON is also important for the consolidation of long-term memory. Thus, MBON γ3β'one likely also plays a role in integrating sleep and memory signals through its reciprocal connections MBON β'2mp (Scaplen, 2021).

This provides a well-characterized anatomical framework to understand how opposing memories are acquired, consolidated, expressed and updated. Since the roles of these converging MBONs in naive and learned behaviors are state dependent, it is hypothesized that MBON γ3β'1 and MBON β'2mp, both receiving convergent input from other MBONs, providing opportunities for feedforward networks to update information processing depending on the state of the animal (Scaplen, 2021).

Some of the feedback connections originally hypothesized to exist in the MB were between MBONs and DANs. The current analysis revealed neurons postsynaptic to MBONs that are TH positive. Recent studies that combined EM annotation and calcium imaging to identify specific MBON-DAN connections suggest extensive recurrent connectivity between MBONs and DANs, validating these findings. For example, previous studies using both GFP Reconstitution Across Synaptic Partners (GRASP) and EM annotation revealed that MBON α1 and DAN α1 are synaptically connected. This study similarly identified a few DAN neurons that innervate the horizontal MB lobes within the MBON α1 postsynaptic signal. A recent study showed that the 20 DANs that innervate the γ5 MB compartment are clustered into five different subtypes that innervate distinct anatomical regions within the γ5 compartment. According to this study, only one of the γ5 DANs receives direct recurrent feedback from γ5β'2a MBONs. Based on these recent anatomical characterizations, it is believed that the TH+ neurons within the postsynaptic signal of γ5β'2a are the γ5 DANs (Scaplen, 2021).

The FSB is the largest substructure of the central complex, and it serves as a sensory-motor integration center. The FSB comprises nine horizontal layers that are innervated by large-field neurons. Previous work in blow flies and, later work in Drosophila, predicted that the FSB was postsynaptic to output neurons of the MB. The current data confirm that the large-field, tangential neurons of the dorsal FSB are postsynaptic to the majority of MBONs. Although there exists some variation across brains, glutamatergic and GABAergic MBONs predominately project to FSB layers 4 and 5, whereas cholinergic MBONs mainly project to FSB layer 6. Connections between MBONs and FSB were consistent across different split-GAL4 lines that have overlapping expression patterns. Similar extensive direct connectivity between these MBONs and the dorsal FSB, especially layers 4 and 5, were found in the recently annotated EM hemibrain dataset. Together, these observations suggest that the connectivity between the MB and FSB are structurally, and perhaps in some cases functionally, conserved across insect species (Scaplen, 2021).

How are FSB layers 4/5 and 6 functionally distinct? The dorsal FSB has a well-established role in modulating sleep and arousal, locomotor control, courtship, and visual memory. FSB layer 5 has been specifically implicated in processing information regarding elevation in a foraging- and rutabaga-dependent manner. More recent studies have implicated the dorsal FSB in processing nociceptive information. FSB layer 6 plays a specific role in avoidance of a conditioned odor, whereas layers 4 and 5 respond to aversive stimuli and are responsible for innate, but not conditioned, avoidance. Moreover, recent connectome data suggest that differences exist in the postsynaptic connections of layers 4/5 and 6 as well. Overall, there is high degree of interconnectivity within the FSB. The predominate output of FSB layer 6 neurons are other FSB neurons. In fact, many FSB layer 6 neurons project exclusively to other FSB neurons. In contrast, FSB layer 4 neurons send direct projections to other brain structures -- CRE, SMP, and LAL -- in addition to projecting to other FSB neurons. The connections with the LAL position the FSB layer 4 to directly influence downstream motor output signals prior to executing behavior. Recent EM analysis also suggests that some FSB layer 6 neurons synapse back onto PAM DAN neurons. This connectivity is in line with the associative role in conditioned nociception avoidance described for FSB layer 6 (Scaplen, 2021).

Interestingly, this study found that the pattern of FSB postsynaptic targets of the MBONα1 is dissimilar to other glutamatergic MBONs. FSB layers 4/5 and 6 are not present in the MBON α1 postsynaptic signal. Instead, MBON α1 project to neurons that innervate the ventral and most dorsal aspect of the FSB. The ventral FSB is implicated in innate avoidance of electric shock, and recent data suggest that its activity is tuned to airflow cues for orientation during flight. Artificial activation of MBON α1 does not result in significant avoidance behavior. However, it has been implicated in the acquisition, consolidation, and expression of 24 hr long-term sucrose memory. It is possible that MBON α1 provides appetitive valence signals to the ventral FSB to guide goal-directed flight. Functionally validating the role of MBON α1 and its relationship with its putative downstream neurons is key to appreciating how learning signals can drive behavioral decisions (Scaplen, 2021).

More research is necessary to further understand the functional role of different FSB layers and how information is integrated across these layers. Based on the anatomical data, it is clear that although the MB and FSB can function in parallel during memory formation, they act as parts of a dynamic system to integrate information and adjust behavioral responses (Scaplen, 2021).

The LAL is an important premotor waystation for information traveling from the central complex to descending neurons innervating thoracic motor centers across insects. Accordingly, the LAL has been implicated in orientation to pheromones in the moth, flight in the locust and dragonfly, locomotion in Drosophila stimulus-directed steering in Drosophila, the cockroach, cricket, and moth and in response to mechanosensory stimuli in the locust. In the moth, recordings from neurons innervating the LAL have a characteristic 'flip-flop' firing property, which is thought to mediate walking command. More recent work has suggested a functional organization whereby the neurons in the upper division of the LAL receive convergent input from the protocerebrum and neurons in the lower division generate locomotor command (Scaplen, 2021).

The current data show that the MB network converges with the protocerebrum input, thereby providing an opportunity for MBONs to indirectly influence descending motor outputs. It was also demonstrated that two MBONs (γ3β'1 and γ2α'1) synapse on the same subset of LAL and FSB cells, revealing a convergent circuit that connects both structures. Further, in support of anatomical observations, optogenetic activation of MBON γ2α'1 resulted in activation of both LAL and FSB layer four neurons. Given that MBON γ3β'1 is GABAergic, the equivalent experiment was not performed for this neuron. Thus, understanding the functional consequences of these inhibitory connections will require further investigation. Interestingly, despite the fact that MBON γ3β'1 and γ2α'1 express different neurotransmitters and innervate different MB compartments, their manipulation has similar behavioral phenotypes: both promote sleep, and artificial activation of either results in naive preference. Further, activation of both MBON γ3β'1 and γ2α'1 together has an additive effect, which results in a significant increase in preference (Aso et al., 2014b) (Scaplen, 2021).

The FSB and LAL have a well-established structural and functional connectivity. The LAL integrates information from the central complex, including the FSB, and provides a premotor signal to motor centers. However, the behavioral significance of MBON γ3β'1 and γ2α'1 projections to both the FSB and LAL is less clear. Previous work demonstrated that activation of these MBONs while the flies explored an open arena did not significantly affect average speed or angular speed of individual flies. By contrast, this study found that inactivation of the putative downstream LAL neurons significantly increased overall activity of behaving flies in a social context and locomotor assay. Thus, the γ3β'1 and γ2α'1 MBONs may play a modulatory rather than required role in influencing behavioral response to an associated cue (Scaplen, 2021).

Recent work in Drosophila has demonstrated that the DANs that innervate MBON γ2α'1 regulate flight bout durations, and may provide a motivation signal via MBONs to the FSB and LAL to regulate motor activities. The LAL neurons receive multisensory input, and some LAL neurons make direct connections to descending neurons that control movement. Thus, this circuit organization enables integration of sensory signals with punishment or reward to direct the motion of the animal. In contrast, MBON connections with the FSB might play a role in providing context for flexible navigation, goal-directed actions, and memory-based navigation (Scaplen, 2021).

If homology can be defined by shared expression of transcription factors and similar functional roles, the MB-FSB connection may be an appropriate model for understanding functional connections between the hippocampus and striatum and serve as an accessible model for understanding connectivity between more complex brain structures associated with memory. Further, given that the integrative relay role of the LAL is somewhat reminiscent of the vertebrate thalamus, the complex connectivity between the MBONs, FSB, and LAL may also serve as an effective model for predicting and understanding functional connections between the hippocampus, striatum, and thalamus in the context of memory formation and action selection (Scaplen, 2021).

Insects exhibit a great variety of complex behaviors, and significant effort has been devoted to understand the neural circuits that underlie these behaviors. The genetically accessible Drosophila is a great model for studying the interplay between circuit architecture and behavior owing to their complex yet tractable brains. The MB circuits and their role in learning and memory are among the most studied circuits in Drosophila. Although, the majority of these studies have focused on olfactory memory, it is clear that the MB plays a much broader role in insect behavior. In Drosophila, the MB is important for courtship memory, taste aversive memory as well as visual memory. In cockroaches, the MB has a role in place memory and recent data in two different species of ants implicate the MB in spatial navigation to learned locations using visual cues. In mammals, the hippocampus is similarly required for multiple forms of associative memory, including spatial navigation using visual cues. Thus, cross-species similarity in circuit organization and function may exist between the mushroom body and the hippocampus. However, such anatomical and functional cross-species comparisons can also be made between the mushroom body and the cerebellum, suggesting that similar convergent-divergent architecture may be a general principle of structures that encode and update memories (Scaplen, 2021).

In this context, the implementation of trans-Tango to study the MB has high potential in the era of EM reconstruction of the Drosophila brain. Through examination of the circuit connectivity in several individuals, easily afforded by trans-Tango, the value of the EM reconstruction data could be augmented by overlaying on it potential nuanced differences between individuals. In addition, trans-Tango-mediated discoveries in the fly could help illuminate principles of circuit organization in other species. Further, due to the modular design of trans-Tango, it could be readily reconfigured for other types of studies beyond circuit tracing. For example, only minimal modifications are required for implementing a configuration of trans-Tango for identifying the molecular composition of the postsynaptic partners. This strategy could be used to examine the evidence that MBONs stratify the FSB through different classes of peptidergic neurons. Confirmation of these observations would suggest that the MB plays a critical role in regulating modulatory systems of a midbrain region that shares structural and functional commonalities with the vertebrate basal ganglia. Finally, through combining it with new genome editing strategies, trans-Tango could become a useful tool for comparative anatomy in other insects. This would enable the study of synaptic connections in non-model organisms and lead to deeper understanding of biological diversity (Scaplen, 2021).

Understanding how memories are formed, stored, and retrieved necessitates knowledge of the underlying neural circuits. This characterization of the architecture of the neural circuits connecting the MB with downstream central complex structures lays the anatomical foundation for understanding the function of this circuitry.These studies may also provide insight into general circuitry principles for how information is processed to form memories and update them in more complex brains (Scaplen, 2021).

Active forgetting is an essential component of the memory management system of the brain. Forgetting can be permanent, in which prior memory is lost completely, or transient, in which memory exists in a temporary state of impaired retrieval. Temporary blocks on memory seem to be universal, and can disrupt an individual's plans, social interactions and ability to make rapid, flexible and appropriate choices. However, the neurobiological mechanisms that cause transient forgetting are unknown. This study identified a single dopamine neuron in Drosophila that mediates the memory suppression that results in transient forgetting. Artificially activating this neuron did not abolish the expression of long-term memory. Instead, it briefly suppressed memory retrieval, with the memory becoming accessible again over time. The dopamine neuron modulates memory retrieval by stimulating a unique dopamine receptor that is expressed in a restricted physical compartment of the axons of mushroom body neurons. This mechanism for transient forgetting is triggered by the presentation of interfering stimuli immediately before retrieval (Sadandal, 2021).

Memory formation, consolidation and retrieval are well-known functions that support memory expression; however, the processes that limit these functions -- including forgetting -- are less understood. Forgetting has been characterized as either passive or active, and is crucial for memory removal, flexibility and updating. Memory may be removed completely, resulting in permanent forgetting; or temporarily irretrievable, resulting in transient forgetting (Sadandal, 2021).

One form of active forgetting-known as intrinsic forgetting-involves one dopamine neuron (DAN) that innervates the γ2α'1 compartment of the axons of mushroom body neurons (MBNs) and the dendrites of the downstream, compartment-specific mushroom-body output neurons (MBONs). This DAN resides in a cluster of 12 DANs in each brain hemisphere that is known as the protocerebral posterior lateral 1 (PPL1) cluster. Current evidence indicates that the ongoing activity of these DANs after aversive olfactory conditioning slowly and chronically erodes labile and nonconsolidated behavioural memory, as well as a corresponding cellular memory trace that forms in the MBONs. This intrinsic forgetting mechanism is shaped by external sensory stimulation and sleep or rest, and is mediated by a signalling cascade in the MBNs that is initiated by the activation of the dopamine receptor DAMB, which leads to the downstream activation of the actin-binding protein Cofilin and the postulated reorganization of the synaptic cytoskeleton (Sadandal, 2021).

By contrast, there is little understanding of the mechanisms that arbitrate transient forgetting. Neuropsychological studies of failures or delays in retrieval in humans have primarily focused on lexical access. Phonological blockers or interfering stimuli produce a tip-of-the-tongue state-the failure to recall the appropriate word or phrase. Tip-of-the-tongue states are resolved when the distracting signals dissipate. Several brain regions have been implicated in tip-of-the-tongue states from functional magnetic resonance imaging studies, but the neurobiological mechanisms that produce a temporary state of impaired retrieval are unknown. This study offers an entry point into this area of brain function (Sadandal, 2021).

Memory retrieval has been proposed to consist of an interplay between internal or external cues and memory engrams, with cue-induced reactivation of engrams across multiple regions of the brain facilitating memory expression. But a central question about this process is how interfering stimuli temporarily block memory retrieval, resulting in transient forgetting. This study offers insights into one such mechanism. Behavioural and functional imaging data reveal that PPL1-α2α'2, working through the DAMB receptor expressed in the α2α'2 MBN axonal compartment, mediates the transient forgetting of PSD-LTM. This effect occurs without altering a cellular memory trace in the postsynaptic MBON-α2sc. This process can be triggered by distracting stimuli, illustrating a neural-genetic-environmental interplay that modifies memory expression (Sadandal, 2021).

This study considered why the cellular memory trace remains unaffected by DAN stimulation despite the occurrence of behavioural forgetting. Because blocking synaptic output from MBON-α2sc reduces PSD-LTM expression, the simplest hypothesis posits that cellular memory traces form with conditioning in the MBON in addition to the cytoplasmic Ca2+-based memory trace that was detected in this study. This is expected: neurons undergo broad changes in physiology as they adopt new states, so it is plausible that such plastic mechanisms-especially ones that gate synaptic release-are inactivated by DAN activity while leaving the Ca2+-based memory trace intact (Sadandal, 2021).

The discovery that loss of function of DAMB leads to enhanced PSD-LTM was surprising, because of a previous study reporting that this insult attenuates PSD-LTM. The experiments argue strongly that DAMB functions normally to suppress expression of PSD-LTM. However, this leads to the question of why a receptor involved in transient forgetting would lead to enhanced PSD-LTM when inactivated. Previous experiments have shown that PPL1-α2α'2-similar to PPL1-γ2α'1-exhibits ongoing activity, leading to a slow release of dopamine onto MBNs. This activity should slowly degrade or suppress existing memory so that when the receptor is inactivated memory expression is enhanced (Sadandal, 2021).

PPL1-α2α'2 has no important role in the forgetting of labile nonconsolidated memory. Instead, previous studies have identified a different DAN (PPL1-γ2α'1) as having a role in this process and the apparent erasure of the downstream cellular memory trace-perhaps an indication of 'permanent forgetting'. This process is modulated by internal and external factors, and is mediated by key molecules expressed in the MBN that receive PPL1-γ2α'1 input. No robust decrement was found in expression of PSD-LTM after PPL1-γ2α'1 stimulation, which points to the existence of two separate dopamine-based circuits for permanent and transient forgetting. This functional separation may indicate a fundamental principle in the organization of circuits that mediate several forms of forgetting (Sadandal, 2021).

However, the DAMB receptor is used for both permanent and transient forgetting. DAMB is widely expressed across the MBN axons but alters synaptic plasticity differently across MBN compartments. It is possible that DAMB signalling may be distinct for the two forms of forgetting. DAMB preferentially couples with Gq, the knockdown of which inhibits the potent erasure of memory, but its potential role in transient forgetting is unknown. The scaffolding protein Scribble orchestrates the activities of Rac, Pak and Cofilin, all of which are important for the permanent forgetting pathway. However, Scribble knockdown or inhibition of Rac1 does not enhance the PSD-LTM as is the case in DAMB-knockdown flies, which suggests that this scaffolding signalosome does not have a large role in transient forgetting. In summary, the two distinct forms of forgetting-transient and permanent-share a dopaminergic mechanism and a common dopamine receptor, but differ in upstream and downstream neural circuits and in downstream signalling pathways within MBNs (Sadandal, 2021).

Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) is well positioned for developing and testing such an approach due to its conserved neuronal architecture, recently completed dense connectome, and extensive prior experimental studies of its roles in learning, memory and activity regulation. This study identified new components of the MB circuit in Drosophila, including extensive visual input and MB output neurons (MBONs) with direct connections to descending neurons. Unexpected structure was found in sensory inputs, in the transfer of information about different sensory modalities to MBONs, and in the modulation of that transfer by dopaminergic neurons (DANs). This study provides insights into the circuitry used to integrate MB outputs, connectivity between the MB and the central complex and inputs to DANs, including feedback from MBONs. The results provide a foundation for further theoretical and experimental work (Li, 2020).

Understanding how memories of past events are formed and then used to influence ongoing behavior are key challenges in neuroscience. It is generally accepted that parallel changes in connection strength across multiple circuits underlie the formation of a memory and that these changes are integrated to produce net changes in behavior. Animals learn to predict the value of sensory cues based on temporal correlations with reward or punishment. Such associative learning entails lasting changes in connections between neurons. It is now clear that different parts of the brain process and store different aspects of the information learned in a single event. In both flies and mammals, dopaminergic neurons play a key role in conveying information about whether an event has a positive or negative valence, and there are compelling parallels between the molecular diversity of dopaminergic cell types across these evolutionarily distant animals. However, there is only a limited understanding of how information about the outside world or internal brain state reaches different dopaminergic populations. Nor is the nature of the information that is stored in each parallel memory system understood, nor how these parallel memories interact to guide coherent behavior. It is believed such processes are governed by general and evolutionarily-conserved principles. In particular, it is believed the circuit logic that allows a brain to balance the competing demands of rapid learning with long-term stability of memory are likely to be the same in flies and mammals. Developing a comprehensive understanding of these circuits at the resolution of individual neurons and synapses will require the synergistic application of a variety of experimental methods together with theory and modeling. Many of the required methods are well developed in Drosophila, where the circuits underlying learning and memory are less complex than in mammals, and where detailed anatomical knowledge of the relevant circuits, which are believed will be essential, has just now become available. This study provide analysis of the complete connectome of a circuit involved in parallel processing of associative memories in adult fruit flies. The core architecture of this circuit is strikingly similar to that of the vertebrate cerebellum (see The shared circuit architecture of the mushroom body and the cerebellum) (Li, 2020).

The MB is the major site of associative learning in insects, and species that perform more complex behavioral tasks tend to have larger MBs. In the MB of each brain hemisphere, sensory stimuli are represented by the sparse activity of ~2000 Kenyon cells (KCs) whose dendrites form a structure called the MB calyx and whose parallel axonal fibers form the lobes of the MB (see Anatomy of the adult Drosophila MB) (Li, 2020).

The major sensory inputs to the Drosophila MB are olfactory, delivered by ~150 projection neurons (PNs) from the antennal lobe to the dendrites of the KCs in the MB calyx. KCs each receive input from an average of six PNs. For a KC to fire a spike, several of its PN inputs need to be simultaneously activated. This requirement, together with global feedback inhibition, ensures a sparse representation where only a small percentage of KCs are activated by an odor. The MB has a three layered divergent-convergent architecture in which the coherent information represented by olfactory PNs is expanded and decorrelated when delivered to the KCs. But the degree to which the structure of the sensory input representation is maintained by the KCs has been debated. This issue is explored, taking advantage of a nearly comprehensive dataset of KC inputs and outputs (Li, 2020).

While best studied for its role in olfactory associative learning, the MB also receives inputs from several other sensory modalities. A subset of projection neurons from the antennal lobe delivers information about temperature and humidity in both the larva. Taste conditioning also requires the MB and is believed to depend on specific KC populations, although the relevant inputs to these KCs have not yet been reported. This study identified one likely path for gustatory input to the MB (Li, 2020).

Drosophila MBs are also known to be able to form memories based on visual cues. Until a few years ago, it was thought that visual input reached the Drosophila MBs using only indirect, multisynaptic pathways as direct visual input from the optic lobes to the MBs, well known in Hymenoptera, had not been observed in any dipteran insect. A previous study identified two types of visual projection neurons (VPNs) connecting the optic lobes and the MB and additional connections have been observed recently by light microscopy. This study found that visual input was much more extensive than previously appreciated, with about 8% of KCs receiving predominantly visual input, and present in this study is a detailed description of neuronal pathways connecting the optic lobe and the MB. Visual sensory input appears to be segregated into distinct KC populations in both the larva and the adult, as is the case in honeybees. This study found two classes of KCs that receive predominantly visual sensory input, as well as MBONs that get the majority of their input from these segregated KC populations (Li, 2020).

MBONs provide the convergence element of the MB's three layer divergent-convergent circuit architecture. Previous work has identified 22 types of MBONs whose dendrites receive input from specific axonal segments of the KCs. The outputs of the MBONs drive learned behaviors. Approximately 20 types of dopaminergic neurons (DANs) innervate corresponding regions along the KC axons and are required for associative olfactory conditioning. Specifically, the presynaptic arbors of the DANs and postsynaptic dendrites of the MBONs overlap in distinct zones along the KC axons, defining the 15 compartmental units of the MB lobes. A large body of evidence indicates that these anatomically defined compartments of the MB are the units of associative learning (Li, 2020).

The DANs innervating different MBON compartments appear to play distinct roles in signaling reward vs. punishment, novelty vs. familiarity, the presence of olfactory cues and the activity state of the fly. These differences between DAN cell types presumably reflect in large part the nature of the inputs that each DAN receives, but knowledge of these inputs is just emerging and is far from comprehensive. DANs adjust synaptic weights between KCs and MBONs with cell type-specific rules and, in at least some cases, these differences arise from the effects of co-transmitters. In general, a causal association of KC responses with the activation of a DAN in a compartment results in depression of the synapses from the active KCs onto MBONs innervating that compartment. Different MB compartments are known to store and update non-redundant information as an animal experiences a series of learning events. In rodent and primate brains, recent studies have revealed that dopaminergic neurons are also molecularly diverse and encode prediction errors and other information based on cell type-specific rules (Li, 2020).

MBONs convey information about learned associations to the rest of the brain. Activation of individual MBONs can cause behavioral attraction or repulsion, according to the compartment in which their dendrites arborize. The combined output of multiple MBONs is likely to be integrated in downstream networks, but it is not understood how memories stored in multiple MB compartments alter these integrated signals to guide coherent and appropriate behaviors. Prior anatomical studies implied the existence of multiple layers of interneurons between MBONs and descending motor pathways. What is the nature of information processing in those layers? Anatomical studies using light microscopy provided the first hints. MBONs from different compartments send their outputs to the same brain regions, suggesting that they might converge on shared downstream targets. DANs often project to these same brain areas, raising the possibility of direct interaction between MBONs and DANs. The functional significance of such interactions has just begun to be investigated, and studies of the Drosophila larva, where a connectome of a numerically less complex MB is available, are providing valuable insights (Li, 2020).

The recently determined connectome of a portion of an adult female fly brain (hemibrain) provides connectivity data for ~22,500 neurons. Among them, ~2600 neurons have axons or dendrites in the MB, while ~1500 neurons are directly downstream of MBONs (using a threshold of 10 synapses from each MBON to each downstream target) and ~3200 are upstream of MB dopaminergic neurons (using a threshold of five synapses from each upstream neuron to each DAN). Thus this study will consider approximately one-third of the neurons in the central brain in this analysis of the MB ensemble (Li, 2020).

Throughout the paper synaptic thresholds were set in order to focus the descriptions and analyses on the most strongly connected neurons. In the above analysis, a higher threshold for MBON connections was chosen to downstream targets than for DAN inputs because the typical MBON has many more output synapses than a DAN has input synapses. At a thresholds of five synapses, DANs have a median of 31 different input neurons, but if the threshold to 10 synapses were increased this would decrease to only six different neurons. In contrast, at the threshold of 10 synapses, MBONs are connected to a median of 90 downstream neurons. There were some limitations resulting from not having a wiring diagram of the full central nervous system, as complete connectivity information for neurons with processes that extended outside the hemibrain volume was lacking. It was generally possible to mitigate these limitations by identifying the corresponding neurons in other EM or light microscopic datasets when the missing information was important for the analyses. Thus the hemibrain dataset was able to support a nearly comprehensive examination of the full neural network underlying the MB ensemble (Li, 2020).

Studies of the larval MB are providing parallel information on the structure and function of an MB with most of the same cell types, albeit fewer copies of each. The microcircuits inside three MB compartments in the adult were previously described and this study reports that the overall organization of these three compartments is conserved in a second individual of a different gender. More importantly, this study extends the analysis of microcircuits within the MB lobes to all 15 compartments, revealing additional aspects of spatial organization within individual compartments (Li, 2020).

The current study led to discovery of new morphological subtypes of KCs and to determine the sensory inputs delivered to the dendrites of each of the ~2000 KC. Considerable structure was found in the organization of those inputs and unexpectedly high levels of visual input, which was the majority sensory input for two classes of KCs. This segregation of distinct sensory representations into channels is maintained across the MB, such that MBONs, by sampling from different KCs, have access to different sensory modalities and representations. A new class of 'atypical' MBONs was discovered, consisting of 14 cell types, that have part of their dendritic arbors outside the MB lobes, allowing them to integrate input from KCs with other information; at least five of them make strong, direct synaptic contact onto descending neurons that drive motor action. How MBONs from different compartments interact with each other to potentially integrate and transform the signals passed from the MB to the rest of the brain is described, revealing a number of circuit motifs including multi-layered MBON-to-MBON feedforward networks and extensive convergence both onto common targets and onto each other through axo-axonal connections. Finally, this study analyzed the inputs to all 158 DANs that innervate the MB. Extensive direct feedback was found from MBONs to the dendrites of DANs, providing a mechanism of communication within and between MB compartments. Groups of DANs were found that share common inputs, providing mechanistic insights into the distributed parallel processing of aversive and appetitive reinforcement and other experimental observations (Li, 2020).

The hemibrain dataset contains the most comprehensive survey of the cell types and connectivity of MB neurons available to date. This connectome allowed probing in fine detail the circuitry underlying canonically proposed functions of the MB, including the representation of olfactory information by KCs, computation of valence by MBONs, and reinforcement of associations by DANs. Patterns in the input to DANs, MBON-to-DAN and MBON-to-MBON connectivity were found that suggest how associative learning in the MB can affect both the acquisition of new information through learning and the expression of previously learned responses. The connectome also reveals circuitry that supports non-canonical MB functions, including selective structure in non-olfactory pathways, a network of atypical MBONs, extensive heterogeneity in DAN inputs, and connections to central brain areas involved in navigation and movement (Li, 2020).

This analysis of the hemibrain connectome relied heavily on an extensive catalog of previously identified and genetically isolated cell types and on decades of study illuminating the link between MB physiology and fly behavior. It is worth emphasizing the interdependency of anatomy, physiology and behavior at the beginnning the post-connectomic era in fly research. Some of the neurons described that appear similarly connected may turn out to have diverse functions due to different physiology and, conversely, neurons that are morphologically distinct may turn out to have similar functions. In addition, a set of inputs with high synapse counts might appear, at the connectome level, to represent a major pathway for activating a particular neuron, but this will not be true if these inputs rarely fire at the same time. Likewise, a set of highly correlated inputs can be effective even if their individual synapse counts are modest. Lack of knowledge about correlated activity is probably the most significant uncertainty when attempting to map synapse counts onto circuit function, likely larger than possible errors in synapse identification and the effects of imposing various thresholds (see below). Finally, the connectome does not reveal gap junction connections or identify more distant non-synaptic modulation. These caveats should be kept in mind when interpreting connectome data (Li, 2020).

A number of these studies involved imposing a cutoff on the number of synapses required to include a particular connection in the analysis. The intent of these thresholds is to focus the analysis on what are likely to be the strongest inputs and outputs. Whether this cutoff should be based on a fixed number of synapses or on a percentage of total synapse counts is open to debate as, of course, is the actual value that the cutoff should take. Neurons differ widely in their numbers of inputs and outputs and these differences need to be taken into account when choosing thresholds. That is why information is presented about percentage of total inputs and outputs as well as synapse numbers. An approach to thresholding that takes such information into account has been used in a previous stdu; in the case of MBON to CX connections explored both here and in the previous study, differences in the connectivity map obtained with different thresholding methods were limited to the weakest connections (Li, 2020).

Most neurons make a large number of weak connections to other neurons, often involving just a single synapse. Some cases of low synapse number may be the result of incomplete reconstruction of neuronal arbors. In the MB lobes, an extensive effort was made to fully reconstruct all neurites and >80% of all computationally predicted synapses were assigned to identified neurons. Likewise, the arbors of MBONs and DANs outside the MB were extensively proofread. However, in other brain regions, where MBON outputs and DAN inputs lie, reconstruction was often much less complete. In such regions, it is difficult to estimate the extent to which the mapped synapses are representative of the full set of connections. A previous study reports an analysis of connectivity determined at two stages of reconstruction in the CX and, while the number of assigned synapses increased with additional proofreading, there was little difference in the connectivity maps. Errors in either synapse prediction or developmental wiring are also likely to produce some false connections represented by only one or two synapses. Of course, real, but sparse, connections might still be impactful if they fire concurrently, but this is not something that can be judged from the available information. Therefore, it seemed reasonable to ignore weak connections in this analyses. Thresholds must obviously be chosen with care and the effects of any particular cutoff value on results and conclusions should be assessed, preferably in conjunction with experimental data (Li, 2020).

Fly research has been greatly facilitated by the development of numerous fly lines that provide cell-type specific genetic access. This analysis has revealed, particularly in the case of DANs, subtypes within groups of neurons that would previously have been considered a single type. Thus, the higher resolution view of cell types that connectomics provides points out the need to develop driver lines or other experimental methods for more fine-tuned genetic access (Li, 2020).

The cell type constituents and circuit motifs of the MB in the adult fly have many similarities with its precursor at the larval stage of development. Both the larval and adult MBs support associative learning and, in both, PNs from the antennal lobe that convey olfactory information provide the majority of the sensory input, complemented by thermal, gustatory and visual sensory information that is segregated into distinct KC populations. However, the multi-layered organization of non-olfactory inputs in the main and accessory calyces (including integration of diverse input sources by LVINs) suggests that the KC representation in the adult is more highly enriched and specialized for non-olfactory sensory features. It is worth noting that the earliest born types of each of the three main adult KC classes (KCγs, KCγd, KCγt, KCα'β'ap1, KCαβp) appear to be specialized for non-olfactory sensory cues, and in most cases their dendrites lie in the accessory calyces (Li, 2020).

In the first instar larval MB, the only larval stage for which a connectome is available, there are roughly 70 mature KCs, which is increased nearly 30-fold in the adult. This enrichment likely increases odor discrimination and olfactory memory capacity. The larval MB has only eight compartments in its horizontal and vertical lobes. Although the increase in the adult to 15 compartments is only about a factor of two, the extent of their DAN modulation is greatly expanded. The larva has only seven confirmed DANs and five additional cells of unknown neurotransmitter thought to provide modulatory input, a factor of 15-fold less than the adult. Whereas each larval KC innervates all eight compartments, individual adult KCs innervate only five out of 15 compartments. Therefore the DANs in the larva are capable of modulating all KCs, whereas in the adult, DANs in different compartments modulate specific subtypes of KCs. The expansion of the number of DANs within many compartments in the adult MB, and subcompartmental targeting of individual DANs within a compartment, further increases the difference in granularity of DAN modulation between the larva and adult (Li, 2020).

MBONs feedback onto DANs and converge onto common downstream targets in both the larva and adult, implying some shared computational strategies. However, the greatly increased DAN complexity in the adult fly and the presence of subcompartmental organization of DAN axon targets not present in the larva suggest a substantial increase in the specificity of the learning signals involved in memory formation and raises the possibility of modality-specific learning signals to complement the multimodal KC representation (Li, 2020).

Previous theoretical work has emphasized the advantages of mixing sensory input to the KCs so that they provide a high-dimensional representation from which MBONs, guided by DAN modulation, can derive associations between sensory input and stimulus-valence. The connectome data modifies this viewpoint in two ways. First, although olfactory input to the KCs is highly mixed, various structural features reduce the dimensionality of the KC odor representation. A recent analysis of EM data from an adult Drosophila MB identified groups of PNs thought to represent food odors that are preferentially sampled by certain KCs. Consistent with this observation, the current analysis revealed subtype-specific biases in PN sampling by KCs, including an overrepresentation of specific glomeruli by α/β and α'/β' KCs. These biases, as well as other structural features, appear to arise from the stereotyped arrangement of PN axons within the calyx and their local sampling by KCs. This may reflect a developmental strategy by which the KC representation is organized to preferentially represent particular PN combinations. Further analyses of KC connectomes across hemispheres and animals, as well as experimental studies, will help evaluate the impact of this structure (Li, 2020).

The hemibrain connectome also revealed a second, more dramatic, structural feature of sensory input to the MB: non-olfactory input streams corresponding to visual and thermo-hygro sensation are strongly segregated. This organization may reflect the nature of stimulus-valence associations experienced by flies. In purely olfactory learning, when valence is associated with particular sets of olfactory receptors, MBONs need to be able to sample those combinations to successfully identify the stimulus. This requires that KCs mix input from multiple glomeruli within the olfactory stream. However, KCs that mix across streams corresponding to different sensory modalities may not be necessary if each modality can be used separately to identify valence. For example, either the visual appearance of an object or its odor may individually be sufficient to identify it as a food source. This study asked whether there is an advantage in having separate modality-specific sensory pathways, as seen in the connectome data, when valences can be decomposed in this way (Li, 2020).

To address this question a model is considered in which KC input is divided into two groups, visual and olfactory. In one version of the model ('shared KC population'), all KCs receive sparse random input from both types of PNs, corresponding to a high degree of mixing across modalities. In the other ('separate KC populations'), half of the KCs receive sparse random input exclusively from visual PNs and half only from olfactory PNs, corresponding to no cross-modal mixing. Two tasks were defined that differ in the way valences are defined. In the first (factorizable) version of the task, each olfactory stimulus and each visual stimulus is assigned a positive or neutral subvalence, and the net valence of the combined stimulus is positive if either of these components is positive (olfactory OR visual). In the second (unfactorizable) version, a valence is randomly assigned independently to each olfactory+visual stimulus pair. The ability of a model MBON, acting as a linear readout, was evaluated to determine valence from these two different KC configurations. Separate modality-specific KC populations are indeed beneficial when the valence can be identified from either one modality or the other. Dedicating different KC subtypes to distinct sensory modalities allows the predictive value of each modality to be learned separately. This result suggests that the divisions into KCs specialized for visual, thermo/hygro and olfactory signals may reflect how natural stimuli of different modalities are predictive of valence (Li, 2020).

On the basis of light-level studies, DAN modulation of KC-to-MBON synapses has been considered to operate at the resolution of MB compartments. However, taken with other recent studies, the morphology and connectivity data indicate that functionally distinct PAM-DAN subtypes operate within a MB compartment. DAN subtypes receive different inputs and likely modulate different KC-MBON synapses within a compartment (Li, 2020).

A prior analysis showed that PAM01-fb DANs were required to reinforce the absence of expected shock during aversive memory extinction, whereas a different set of γ5 DANs were needed for learning with sugar reinforcement. It is conceivable that another γ5 DAN subtype is required for male flies to learn courtship rejection. The connectome data revealed DAN subtypes in every compartment that is innervated by PAM DANs (Li, 2020).

The γ3 compartment provides an interesting example of subcompartmental targeting of modulation by DANs and of KC input onto MBONs. There are two subtypes of PAM DANs and three types of MBONs in the γ3 compartment. MBON09 and MBON30 primarily receive olfactory information from γm KCs whereas MBON33 primarily receives visual information from γd KCs. PAM12-dd and PAM12-md DANs appear to modulate KC inputs to MBON09/MBON30 or MBON33, respectively. Although existing driver lines do not separate the dd and md subtypes, PAM-γ3 DAN activity is suppressed by sugar and activated by electric shock. This study found that PAM12-dd DANs share input with PPL1 DANs conveying punishment signals, while PAM12-md DANs are co-wired with PAM08 (γ4) DANs conveying reward signals. Thus, synaptic transmission from two sets of modality-specific KCs to different MBONs can be independently modulated by DANs signaling different valences, all within a single compartment (Li, 2020).

To explore the implications of DAN modulation that is specific to sensory modality,the model presented above was expanded by including two DANs, one conveying the visual component of valence and the other the olfactory component. KCs were divided into visual and olfactory modalities, and two configurations for DAN modulation, one, 'shared reward signal', that is compartment-wide and non-specific, and the other, 'separate reward signals' were considered, in which each DAN only induces plasticity onto KC synapses matching its own modality. This latter case models a set of DANs that affect synapses from visual KCs onto the MBON and another set that affect olfactory synapses (alternatively, it could model two MBONs in different compartments that are modulated independently and converge onto a common target). This study found that, when KCs are divided into separate populations and separate modalities can be used to identify valence, learning is more efficient if the pathways are modulated individually (Li, 2020).

This analysis revealed that DANs receive very heterogeneous inputs but, nonetheless, some DANs both within and across compartments often share common input. This combination of heterogeneity and commonality provides many ways of functionally combining different DAN subtypes. For example, it is expected that this combination allows DANs to encode many different combinations of stimuli, actions and events in a state-dependent manner and to transmit this information to specific loci within the MB network (Li, 2020).

In addition to heterogeneous inputs from a variety of brain regions, the DAN network receives a complex arrangement of within and across-compartment monosynaptic input from a variety of MBONs, using both excitatory and inhibitory neurotransmitters. It was found that nearly all MB compartments contain at least one direct within-compartment MBON-DAN feedback connection. MBON feedback onto these DAN subtypes allows previously learned associations that modify MBON activity to affect future learning. MBONs that feedback onto the same DANs that modulate them could, if the result of learning is reduced DAN activity, prevent excess plasticity for an already learned association. In cases where MBON activity excites the DAN, the self-feedback motif could assure that learning does not stop until the MBON has been completely silenced. More generally, MBON inputs to DANs imply that dopaminergic signals themselves reflect learned knowledge and the actions it generates. This could, in turn, allow MBON modulation of DAN activity to support a number of learning paradigms beyond pure classical conditioning, including extinction, second-order conditioning, operant conditioning and reinforcement learning (Li, 2020).

Flies can perform second-order conditioning, in which a stimulus that comes to be associated with reinforcement may itself act as a pseudo-reinforcement when associated with other stimuli. This computational motif of learning the value of sensory states and using the inferred value as a surrogate reinforcement to guide behavioral learning is the core principle behind a class of machine learning techniques known as actor-critic algorithms. These algorithms consist of two modules: the 'actor', whose job is to map sensory inputs to behavioral outputs and the 'critic', whose job is to map sensory inputs to their inferred values and provide these values as a learning signal for the actor. In the mammalian basal ganglia, the dorsal and ventral striatum, the latter of which strongly influences the activity of dopamine neurons in areas including VTA, have been proposed to represent actor and critic modules, respectively. In the MB, this perspective suggests a possible additional 'critic' function for some MBONs beyond their known 'actor' role in directly driving behaviors. Consistent with this view, activation of individual MBONs can excite DANs in other compartments. This study found strong direct monosynaptic connections between some MBONs and DANs in other compartments. Functional studies will be needed to determine which MBONs, if any, participate in an actor-critic arrangement, and which circuit mechanisms-for example, release from inhibition, or reduction of excitation-are at work (Li, 2020).

Another potential role of cross-compartment MBON-DAN feedback is to gate the learning of certain associations so that the learning is contingent on other associations having already been formed. Such a mechanism could support forms of memory consolidation in which long-term memories are only stored after repeated exposure to a stimulus and an associated reward or punishment. Prior studies have linked plasticity in the γ1 and γ2 compartments to short-term aversive memory and plasticity in the α2 and α3 compartments to long-term aversive memory. The cross-compartmental MBON-to-DAN connections observed in this study suggest an underlying cicuit mechanism for this 'transfer' of short to long term memory. Aversion drives PPL101 (γ1pedc) and depresses the conditioned odor-drive to the GABAergic MBON11 (γ1pedc>α/β). MBON11 is strongly connected with PPL1-γ1pedc and is more weakly connected with PPL105 and PPL106. Depression of MBON11 will therefore also release the PPL105 and PPL106 DANs from MBON11-mediated inhibition, increasing their activity in response to the conditioned odor and making them more responsive during subsequent trials. The net result is that short-term aversive learning by MBON11 (γ1pedc>α/β) promotes long-term learning in MBON18 (α2sc) and MBON14 (α3) by releasing the inhibition on the dopamine neurons that innervate the α2 and α3 compartments. Indeed pairing inactivation of MBON11 with odor presentation can form an aversive memory that requires output from PPL1-DANs and optogenetic stimulation of MBON11 during later trials of odor-shock conditioning impairs long-term memory formation (Li, 2020).

Cross-compartment MBON-DAN feedback may also enable context-dependent valence associations, such as the temporary association of positive valence with neutral stimuli when a fly is repeatedly exposed to aversive conditions. Multiple, spaced aversive conditioning trials were recently shown to form, in addition to an aversive memory for the shock-paired odor, a slowly emerging attraction, a 'safety' memory, for a second odor that was presented over the same training period without shock. The GABAergic MBON09 (γ3β'1) appears to play a critical role in the formation of this safety memory. The synapses from KCs conveying the shock-paired odor are depressed in that portion of MBON09's dendrite that arborizes in γ3, while the synapses from KCs conveying the safety odor are depressed in its β'1 arbor. These combined modulations should gradually release downstream neurons from MBON09 feedforward inhibition, consistent with the proposed mechanism for PAM13/14 (β'1) and PAM05/06 (β'2m and β'2p) DANs becoming more responsive to the safe odor. The connectome data revealed that MBON09 is directly connected to the PAM13/14 (β'1ap) and PAM05 (β'2p) DANs, consistent with release from inhibition underlying the delayed encoding of safety (Li, 2020).

DANs also make direct connections with MBONs in each MB compartment, and optogenetic activation of PAM11 DANs can directly excite MBON07 with slow dynamics. This might provide a mechanism to temporarily suppress memory expression without impairing the underlying memory, which is stored as depressed KC-to-MBON synapses. For example, DANs are known to control hunger and thirst-dependent memory expression, and their excitation of MBONs could provide a possible mechanism (Li, 2020).

Feedforward MBON-MBON connections were postulated, based on behavioral and light microscopic anatomical observations, to propagate local plasticity between compartments. The GABAergic MBON11 is poised to play a key role in such propagation as it is connected with 17 other MBONs (at a threshold of 10 synapses). Thus local depression of KC-MBON11 synapses by the shock responsive PPL1-γ1pedc DAN would be expected to result in disinhibition of MBONs in other compartments. Indeed enhanced CS+ responses of MBON01 and MBON03 after odor-shock conditioning have been ascribed to release from MBON11 inhibition. The consequence of releasing other strongly connected MBONs (MBON07, MBON14 and MBON29) from MBON11 inhibition awaits future study. As discussed above, MBON11 is also connected to DANs innervating its cognate compartment and to DANs innervating other compartments. MBON11 may therefore coordinate MBON network activity via both direct and indirect mechanisms. Analogous feedforward inhibitory connections were found from MBON09 (γ3β'1) to MBON01 and MBON03. Aversive learning therefore reduces both MBON11- and MBON09-mediated inhibition of these MBONs, which further skews the MBON network toward directing avoidance of the previously punished odor (Li, 2020).

Disinhibition likely also plays an important role in appetitive memory. The connectivity of MBON06 (β1>α) revealed here indicates that local plasticity in the β1 compartment can propagate to other MBONs. Similar to MBON11, MBON06 is directly connected with nine other MBONs with a threshold of 10 synapses. MBON06 gradually increases its response to repeated odor exposure and odor-evoked responses of β1 DANs vary with metabolic state. More compellingly, artificially triggering PAM10 (β1) DANs can assign appetitive valence to odors. However, the role of MBON06 in appetitive memory and how the PAM10 (β1) DANs modulate KC synapses to MBON06 has not been investigated. The glutamatergic MBON06 (β1>α) makes a large number of reciprocal axoaxonic connections with the GABAergic MBON11, whose activation favors approach. This reciprocal network motif and the positive sign of behavior resulting from β1 DAN-driven memory suggests that MBON06 released glutamate is likely to be inhibitory to MBON11 and its other downstream targets. MBON06 also makes twice as many connections onto the glutamatergic MBON07 and the cholinergic MBON14 as does MBON11. MBON14 (α3) and MBON07 (α1) have established roles in appetitive memory. Assuming that PAM10 (β1) DANs encode appetitive memory by depressing synapses from odor-specific KCs onto MBON06 (β1>α), MBON06 suppression will release the feedforward inhibition of MBON06 onto MBON07 (α1), freeing it to participate in driving the PAM11-aad (α1) DANs. Release of MBON06 inhibition should also simultaneously potentiate the responses of MBON14 (α3) to the conditioned odor. Lastly, releasing the strong inhibition from MBON06 frees MBON11 to provide weaker inhibition that would further favor odor-driven approach (Li, 2020).

Aversive learning will also alter the function of the MBON06:MBON11 network motif. Aversive reinforcement through the PPL101 (γ1pedc) DAN depresses KC-MBON11 connections. This depression releases MBON06 from MBON11-mediated suppression and allows MBON06 to then suppress output through MBON07 and MBON11, further favoring odor-driven avoidance (Li, 2020).

Several studies have described the influence of internal states such as hunger and thirst on the function and physiology of the MB. In essence, states appear to modulate the DAN-MBON network so that the fly preferentially engages in the pursuit of its greatest need. Since current knowledge suggests these deprivation states employ volume release of modulatory peptides or monoamines to control specific DANs and their downstream MBONs, the connectome analyzed in this study does not provide a complete description of this circuit. Nevertheless, direct connectivity does provide some interesting new insight concerning hunger and thirst-dependent control (Li, 2020).

The MBON06 (β1>α):MBON11 (γ1pedc>α/β) cross-inhibitory network motif is likely to be relevant for the dependence of learning and memory expression on hunger state. PPL101 (γ1pedc) DANs and MBON11 (γ1pedc>α/β) are sensitive to nutrient/satiety status, with MBON11 being more responsive in hungry flies. Therefore, the hungry state favors the activity of the MBONs that are normally repressed by MBON06 (Li, 2020).

Thirst-dependent seeking of water vapor requires the activity of DANs innervating the β'2 compartment. The current work shows that these DANs are likely to directly modulate thermo/hygrosensory KCs. In addition, a recent study showed that thirst-dependent expression of water memory required peptidergic suppression of the activity of both the PPL103(γ2α'1) and PAM02 (β'2a) DANs. Interestingly, blocking the PAM02 (β'2a) DANs released memory expression in water-sated flies whereas blocking the PPL103 (γ2α'1) DANs had no effect. However, if the PPL103 (γ2α'1) DANs were blocked together with PAM02 (β'2a) they further facilitated water memory expression. The connectome suggests circuit mechanisms that could reconcile these observations: MBON12 (γ2α'1) provides strong cholinergic input to the PAM02 (β'2a) DANs, suggesting that PPL103 (γ2α'1) DANs might facilitate water memory expression by suppressing MBON12's excitatory input onto PAM02 (β'2a) DANs (Li, 2020).

A role for the MB in guiding locomotion and navigation in ants and other insects has been proposed. The strong and direct connections observed from the majority of MBONs to the CX, the fly's navigation and locomotion control center, provide one circuit path for the MB to exert influence on motor behaviors. Discovering how this input is utilized by the CX will require additional experimental work (Li, 2020).

Optogenetic activation of Drosophila MBONs can promote attraction or avoidance by influencing turning at the border between regions with and without stimulating light. The effect of MBON activation is additive: coactivation of positive-valence MBONs produced stronger attraction, whereas coactivation of positive and negative-valence MBONs cancelled each other out. Because the fly needs to balance the outputs of different compartments, it is expected that those downstream neurons that integrate inputs from multiple MBONs will have a privileged role in motor control (Li, 2020).

The activity of some DANs has been shown to correlate with motor activity, and the optogenetic activation of PAM-β'2 or PPL1-α3 DANs can attract flies, indicating that DAN activity can itself in some circumstances drive motor behavior. The circuit mechanisms generating the correlation between DAN activity and motor behavior remain to be discovered. Downstream targets of MBONs provide extensive input to DANs, and this study found that neurons downstream of multiple MBONs are twice as likely as other MBON targets to provide such direct input to DANs (Li, 2020).

This study also discovered a direct pathway mediated by atypical MBONs that connects to the descending neurons (DNs) that control turning, an observation that provides additional support for the importance of the MB in the control of movement. The connections of these MBONs appear to be structured so as to promote directional movement, often involving a push-pull arrangement of MBONs signaling approach and avoidance. In addition to direct connections to DNs, there is a network of connections mediated by both local LAL interneurons and interneurons that connect the right and left hemisphere LALs. The atypical MBONs that connect directly to the descending steering system, MBON26, MBON27, MBON31 and MBON32, appear to be among the most integrative neurons in the MB system in the sense that they combine direct KC input from the MB compartments with both input from many other MBONs and non-MB input. At the level of the descending neurons, the highly processed signals from these MBONs are combined with inputs from many other sources, including the central complex, to affect a decision to turn. This high degree of integration presumably reflects the complexity and importance of this decision, with many factors involved that might act individually or in combination (Li, 2020).

Visual input to the MB is over-represented in the output to the descending neurons, predominantly through MBON27. Short- and long-term learning based on features in a visual scene has been reported to involve the CX. Plasticity in the CX enables visual feature input from the sky and surrounding scenery to be mapped flexibly onto the fly's internal compass. The visual input conveyed to the MB and, presumably, the learning at the synapses between visual KCs and MBON27, may be of lower resolution, encoding broader features such as color and contrast. An early study demonstrated that the MB is dispensable for flying Drosophila to learn shapes but that it is required for them to generalize their learning if the visual context changes between training and testing. Memory of visual features and the ability to generalize context could allow visual landmarks to help guide navigation either through the CX or by directly influencing descending neurons. The thermo/hygrosensory features conveyed by MBON26 could play a similar role, as could the large amount of odor-related information present in this MBON output pathway (Li, 2020).

The MB has an evolutionarily-conserved circuit architecture and uses evolutionarily-conserved molecular mechanisms of synaptic plasticity. The dense connectome analysed in this report has uncovered many unanticipated circuit motifs and suggested potential circuit mechanisms that now need to be explored experimentally. Drosophila provides access to many of the required tools such as cell-type-specific driver lines, genetically encoded sensors and microscopy methods to observe whole-brain neuronal activity and fine ultrastructure. These features make the fly an excellent system in which to study many general issues in neuroscience, including: the functional diversity of dopaminergic neurons that carry distributed reinforcement signals, the interactions between parallel memory systems, and memory-guided action selection, as well as the mechanisms underlying cell-type-specific plasticity rules, memory consolidation, and the influence of internal state. It is expected that studies of the MB will provide insight into general principles used for cognitive functions across the animal kingdom (Li, 2020).

As mentioned in the introduction, the MB shares many features with the vertebrate cerebellum, and the results should be informative for studies of the cerebellum proper as well as other cerebellum-like structures such as the dorsal cochlear nucleus and the electrosensory lobe of electric fish. A distinctive feature of these systems, and of the MB, is that learning is driven by a particular mechanism; for example DAN modulation in the MB or complex spiking driven by climbing fiber input in the cerebellum. Studies of learning in cortical circuits have traditionally focused on Hebbian forms of learning driven by the ongoing input and output activity of a neuron. However, recent results from both hippocampal circuits have stressed the importance of plasticity that is driven by dendritic plateau potentials or bursts that resemble the distinct learning events seen in cerebellar and MB circuits. Thus, the form of plasticity seen in the MB and its control by output and modulatory circuits may inform studies of learning in the cerebral cortex as well (Li, 2020).

Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. This study provides a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. Afferent sensory pathways and a large population of neurons were discovered that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). This was combined with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. It was found that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. This study provides the most detailed view to date of biological circuit motifs that support associative learning (Eschbach, 2020).

To behave adaptively in an ever-changing environment, animals must be able to learn new associations between sensory cues (conditioned stimuli, CS) and rewards or punishments (aversive and appetitive unconditioned stimuli, US), and continuously update previous memories, depending on their relevance and reliability (Eschbach, 2020).

Modulatory neurons (for example, DANs) convey information about rewards and punishments, and provide the teaching signals for updating the valence associated with CS in learning circuits across the animal kingdom (for example, the vertebrate basal ganglia or the insect mushroom body, MB). The co-occurrence of CS and modulatory neuron activity tuned only to the received US can support simple associative memory formation. To account for more complex behavioral phenomena, theories have been developed in which learning can be regulated by previously formed associations. According to reinforcement learning theories, learning is driven by errors between predicted and actual US (prediction errors) which are thought to be represented by the activity of DANs. Indeed, in many model organisms, the responses of modulatory neurons have been shown to be adaptive, including monkeys, rodents and insects. Despite recent progress the basic principles by which DAN activity is adaptively regulated and teaching signals are computed are not well understood (Eschbach, 2020).

A prerequisite for the adaptive regulation of modulatory neuron activity is convergence of afferent pathways that convey information about received US with feedback pathways that convey information about previous experiences. A comprehensive synaptic-resolution connectivity map of the feedback circuits that regulate modulatory neurons would provide a basis for understanding how learning is adaptively regulated by prior learning, but it has previously been out of reach (Eschbach, 2020).

Insects, especially in their larval stages, have small and compact brains that have recently become amenable to large-scale electron microscopy (EM) circuit mapping. Both adult and larval insect stages possess a brain center that is essential for associative learning, the MB. The MB contains neurons called Kenyon cells (KCs) that sparsely encode CS, MB modulatory neurons (collectively called MB input neurons, MBINs) that provide the teaching signals and MB output neurons (MBONs) whose activity represents learnt valences of stimuli. In the Drosophila larva, most modulatory neurons are DANs, some are octopaminergic neurons (OANs) and some have unidentified neurotransmitters (simply called MBINs). Modulatory neurons and MBONs project axon terminals and dendrites, respectively, onto the KC axons in a tiled manner, defining MB compartments, in both adult and larval Drosophila. In adult Drosophila, it has been shown that coactivation of KCs and DANs reduces the strength of the KC-MBON synapse in that compartment. Different compartments have been implicated in the formation of distinct types of memories, for example, aversive and appetitive, or short and long term. However, despite a good understanding of the structure and function of the core components of the MB in both adult and larval Drosophila, the circuits presynaptic to modulatory neurons that regulate their activity have remained relatively uncharacterized (Eschbach, 2020).

This study therefore reconstructed all neurons presynaptic to all modulatory neurons in an EM volume that spans the entire nervous system of a first instar Drosophila larva, in which all the core components of the MB were previously reconstructed. This study also determined which individual modulatory neurons are activated by punishments and reconstructed their afferent US pathways from nociceptive and mechanosensory neurons. The neurotransmitter profiles of some of the neurons in the network were characterized and some of the identified structural connections were functionally confirmed. Finally, a model was developed of the circuit constrained by the connectome, the neurotransmitter data and the functional data, and it was used to explore the computational advantages offered by the recently discovered architectural motifs for performing distinct learning tasks (Eschbach, 2020).

Modulatory neurons (for example, DANs) are key components of higher-order circuits for adaptive behavioral control, and they provide teaching signals that drive memory formation and updating. This study provides a synaptic-resolution connectivity map of a recurrent neural network that regulates the activity of modulatory neurons in a higher-order learning center, the Drosophila larval MB. Some of the recently identified structural pathways were functionally tested, and a model of the circuit was developed to explore the roles of these motifs in different learning tasks (Eschbach, 2020).

A large population was discovered of 61 feedback neuron pairs that provide one- and/or two-step feedback from the MBONs to modulatory neurons. Strikingly, it was found that many modulatory neurons receive more than 50% of their total dendritic input from feedback pathways. These results suggest that prior memories as represented by the pattern of MBON activity can strongly influence modulatory neuron activity (Eschbach, 2020).

Learning and memory systems in vertebrates and insects are often organized into distinct compartments implicated in forming distinct types of memories (for example, aversive and appetitive or short and long term). Interestingly, it was found that the majority of the discovered feedback pathways link distinct memory systems, suggesting that the MB functions as an interconnected ensemble during learning. Thus, prior memories formed about an odor in one compartment can influence the formation and updating of distinct types of memories about that odor in other compartments (Eschbach, 2020).

In adult Drosophila, functional connections between some MBONs and DANs have been reported, and some have been shown to play a role in short-term memory formation, long-term memory consolidation, extinction and reconsolidation, or in synchronizing DAN ensemble activity in a context-dependent manner. In some cases, direct MBON-to-DAN connections have been demonstrated. Although direct connections from several MBONs onto DANs exist in the larva, this study found that indirect connections via the feedback neurons account for a much larger fraction of a modulatory neuron's dendritic input than direct MBON synapses. This suggests that adaptive DAN responses may be largely driven by such indirect feedback (Eschbach, 2020).

Some of the one-step within-compartment feedback motifs that were found are analogous to the feedback motifs so far described for the DANs in the vertebrate midbrain. Although the diversity and the inputs of striatal feedback neurons have not yet been fully explored, in the future it will be interesting to determine whether many of the striatal feedback neurons also link distinct memory systems (Eschbach, 2020).

The use of internal predictions can dramatically increase the flexibility of a learning system. This study reveals candidate circuit motifs that could compute integrated predicted value signals across appetitive and aversive memory systems. A prominent motif that was identified is convergence of excitatory and inhibitory connections from MBONs from compartments of opposite valence onto DANs. In naive animals, odor-evoked MBON excitation in all compartments is thought to be similar. However, associative learning selectively depresses conditioned odor drive to MBONs in compartments where modulatory neuron activation has been paired with the odor. It is proposed that by comparing the conditioned odor-evoked MBON excitation in compartments of opposite valence via cross-compartment feedback connections, modulatory neurons compute an integrated predicted value signal across appetitive and aversive domains (Eschbach, 2020).

Convergence of feedback and US pathways could allow the computation of prediction errors An important aspect of reinforcement learning theories is the idea that modulatory neurons compare predicted and actual US (to compute so-called prediction errors) and drive memory formation or extinction depending on the sign of the prediction error. Although Drosophila modulatory neurons have not yet been directly shown to represent prediction errors, adult and larval Drosophila are capable of extinction, and the current study reveals candidate motifs that could support the comparison of expected and actual US. Modulatory neurons were found to receive convergent input from feedback pathways from MBONs and from US pathways. Modulatory neurons could therefore potentially compute prediction errors by comparing inhibitory drive from the feedback pathways with the excitatory drive from the US pathways, or vice versa. Consistent with this idea, some DANs were observed in this model that are inhibited by US alone and activated by CS+ alone, or vice versa (Eschbach, 2020).

This study also reveals that US pathways and feedback pathways converge at two levels: not only at the modulatory neurons themselves, but also at the FB2Ns. Actual and expected outcomes could therefore also be compared by FB2Ns. A recent study in the mouse ventral tegmental area has found that some pre-DAN neurons encoded only actual or only expected reward, whereas others encoded both variables. Thus, both in vertebrates and in insects, comparing predicted and actual outcomes may be a complex computation involving multiple levels of integration that eventually converge onto an ensemble of modulatory neurons (Eschbach, 2020).

An assumption in many reinforcement learning models is that all modulatory neurons receive a global scalar reward prediction error signal. The current study was able to analyze the comprehensive set of inputs of every individual uniquely identifiable modulatory neuron in a learning center. This revealed that each modulatory neuron receives a unique set of feedback inputs that could enable each neuron to compute a unique set of features. Consistent with this, a diversity of adaptive response types in the modulatory neurons was observed in this model This suggests that instead of computing a single global reward prediction error that is distributed to all modulatory neurons, the network uses a range of distinct compartmentalized and distributed teaching signals (Eschbach, 2020).

The connectivity and modeling studies revealed two architectural features of the circuit that provide input to the modulatory neurons that increase its performance and flexibility on learning tasks. The first is the multilevel feedback architecture that includes not only the previously known direct MBON feedback, but also multiple levels of indirect feedback. The second is the extensive set of cross-compartment connections. Modeling suggests that these motifs support improved performance on complex tasks that require the computation of variables such as predictions, prediction errors and context (Eschbach, 2020).

In summary, this study presents a complete circuit diagram of a recurrent network that computes teaching signals in a biological system, providing insights into the architectural motifs that increase its computational power and flexibility. The connectome-constrained model provides numerous predictions that can be tested in the future in a tractable model organism, for which genetic tools can be generated to monitor and manipulate individual neurons. The connectome, together with the functional and modeling studies, therefore provides exciting opportunities for elucidating the biological implementation of reinforcement learning algorithms (Eschbach, 2020).

Many neurons show compartmentalized activity, in which activity does not spread readily across the cell, allowing input and output to occur locally. However, the functional implications of compartmentalized activity for the wider neural circuit are often unclear. This problem was addressed in the Drosophila mushroom body, whose principal neurons, Kenyon cells, receive feedback inhibition from a non-spiking interneuron called APL. This study used local stimulation and volumetric calcium imaging to show that APL inhibits Kenyon cells' dendrites and axons, and that both activity in APL and APL's inhibitory effect on Kenyon cells are spatially localized (the latter somewhat less so), allowing APL to differentially inhibit different mushroom body compartments. Applying these results to the Drosophila hemibrain connectome predicts that individual Kenyon cells inhibit themselves via APL more strongly than they inhibit other individual Kenyon cells. These findings reveal how cellular physiology and detailed network anatomy can combine to influence circuit function (Amin, 2020).

Different types of Drosophila dopaminergic neurons (DANs) reinforce memories of unique valence and provide state-dependent motivational control. Prior studies suggest that the compartment architecture of the mushroom body (MB) is the relevant resolution for distinct DAN functions. This study used a recent electron microscope volume of the fly brain to reconstruct the fine anatomy of individual DANs within three MB compartments. The 20 DANs of the γ5 compartment, at least some of which provide reward teaching signals, can be clustered into 5 anatomical subtypes that innervate different regions within γ5. Reconstructing 821 upstream neurons reveals input selectivity, supporting the functional relevance of DAN sub-classification. Only one PAM-γ5 DAN subtype γ5(fb) receives direct recurrent feedback from γ5β'2a mushroom body output neurons (MBONs) and behavioral experiments distinguish a role for these DANs in memory revaluation from those reinforcing sugar memory. Other DAN subtypes receive major, and potentially reinforcing, inputs from putative gustatory interneurons or lateral horn neurons, which can also relay indirect feedback from MBONs. The single aversively reinforcing PPL1-γ1pedc DAN was similarly reconstructed. The γ1pedc DAN inputs mostly differ from those of γ5 DANs and they cluster onto distinct dendritic branches, presumably separating its established roles in aversive reinforcement and appetitive motivation. Tracing also identified neurons that provide broad input to γ5, β'2a, and γ1pedc DANs, suggesting that distributed DAN populations can be coordinately regulated. These connectomic and behavioral analyses therefore reveal further complexity of dopaminergic reinforcement circuits between and within MB compartments (Otto, 2020).

An adaptive transition from exploring the environment in search of vital resources to exploiting these resources once the search was successful is important to all animals. The neuronal circuitry that allows larval Drosophila melanogaster of either sex to negotiate this exploration-exploitation transition was examined. This was done by combining Pavlovian conditioning with high-resolution behavioral tracking, optogenetic manipulation of individually identified neurons, and EM data-based analyses of synaptic organization. Optogenetic activation of the dopaminergic neuron DAN-i1 was found to both establish memory during training and acutely terminate learned search behavior in a subsequent recall test. Its activation leaves innate behavior unaffected, however. Specifically, DAN-i1 activation can establish associative memories of opposite valence after paired and unpaired training with odor, and its activation during the recall test can terminate the search behavior resulting from either of these memories. These results further suggest that in its behavioral significance DAN-i1 activation resembles, but does not equal, sugar reward. Dendrogram analyses of all the synaptic connections between DAN-i1 and its two main targets, the Kenyon cells and the mushroom body output neuron MBON-i1, further suggest that the DAN-i1 signals during training and during the recall test could be delivered to the Kenyon cells and to MBON-i1, respectively, within previously unrecognized, locally confined branching structures. This would provide an elegant circuit motif to terminate search on its successful completion (Schleyer, 2020).

The patterns of neuronal connectivity underlying multisensory integration, a fundamental property of many brains, remain poorly characterized. The Drosophila melanogaster mushroom body-an associative center-is an ideal system to investigate how different sensory channels converge in higher order brain centers. The neurons connecting the mushroom body to the olfactory system have been described in great detail, but input from other sensory systems remains poorly defined. This study used a range of anatomical and genetic techniques to identify two types of input neurons that connect visual processing centers-the lobula and the posterior lateral protocerebrum-to the dorsal accessory calyx of the mushroom body. Together with previous work that described a pathway conveying visual information from the medulla to the ventral accessory calyx of the mushroom body, this study defines a second, parallel pathway that is anatomically poised to convey information from the visual system to the dorsal accessory calyx (Li, 2020).

Sensory systems use different strategies to detect specific physical features of the outside world. For instance, the olfactory system contains many different types of sensory neuron that are each specialized in detecting a specific class of volatile chemicals. Through only two neuronal layers, olfactory information-the identity of an odor and its concentration-is relayed to higher brain centers. In contrast, the visual system contains far fewer types of sensory neuron, but through numerous neuronal layers, it relays a range of highly processed information-for instance, color, brightness, motion, and shape-to higher brain centers. Thus, higher brain centers have to integrate different types of processed information, bind that information into a coherent representation of the outside world, and use such representations to guide behavior. How higher brain centers achieve this feat remains largely unknown. This gap in knowledge mainly stems from the fact that higher brain centers are formed by a large number of neurons and that the projection neurons conveying information from different sensory systems to these centers often remain poorly characterized. This makes it difficult to understand whether there are specific patterns of neuronal connectivity that enable multisensory integration and what the nature of these patterns are. Deciphering the fundamental neuronal mechanisms that underlie multisensory integration requires a model system such as the Drosophila melanogaster mushroom body, which consists of a relatively small number of neurons whose connections can be charted reliably (Li, 2020).

The Drosophila mushroom body is formed by ∼2,000 neurons-called the Kenyon cells-and has long been studied for its essential role in the formation of olfactory associative memories. The identity of the projection neurons that connect the olfactory system to the mushroom body-and the way Kenyon cells integrate input from these neurons-has been characterized in great detail, highlighting fundamental connectivity patterns that enable this higher brain center to represent olfactory information efficiently. Evidence in Drosophila melanogaster shows that the mushroom body is more than an olfactory center, as it is also required for the formation of visual and gustatory associative memories. However, the identity of the neurons that connect the mushroom body to other sensory systems remains poorly characterized. Thus, a first step toward understanding how the mushroom body integrates multisensory information is to identify such non-olfactory mushroom body input neurons and the genetic tools necessary to manipulate these neurons (Li, 2020).

The mushroom body receives its input through its calyx and sends its output through its lobes. The calyx-a morphologically distinct neuropil containing the synapses formed between projection neurons and Kenyon cells-can be divided into four, non-overlapping regions: one main calyx as well as three accessory calyces named the dorsal, lateral, and ventral accessory calyces. The five output lobes-the α, α', β, β', and γ lobes-contain the synapses formed between Kenyon cells, mushroom body output neurons, and dopaminergic neurons. With respect to these input and output regions, Kenyon cells can be divided into seven distinct types. Of these seven types, five types-the α/βc, α/βs, α'/β'ap, α'/β'm, and γmain Kenyon cells-extend their dendrites only into the main calyx and their axons along one or two lobes. Most of the neurons that project to the main calyx emerge from the antennal lobe, the primary olfactory center in the Drosophila brain. Thus, α/βc, α/βs, α'/β'ap, α'/β'm, and γmain Kenyon cells receive input primarily from the olfactory system (Li, 2020).

In contrast, the two other classes of Kenyon cells do not extend their dendrites into the main calyx. Instead, the α/βp Kenyon cells extend their dendrites into the dorsal accessory calyx-avoiding completely the main, lateral, and ventral accessory calyces-and their axons along the α and β lobes. Likewise, the γd Kenyon cells extend their dendrites exclusively into the ventral accessory calyx and their axons along the γ lobe. Thus, both the α/βp and γd Kenyon cells are anatomically poised to receive non-olfactory input. There is evidence suggesting that the ventral accessory calyx receives input from the medulla, a region of the optic lobe that specializes in processing brightness and color. Furthermore, a recent study suggests that the dorsal accessory calyx is a multisensory center that integrates input from multiple sensory pathways, including the olfactory, gustatory, and visual systems (Li, 2020).

This study reports a strategy that uses a combination of genetic tools-including transgenic lines that drive expression in few neurons and a photo-labeling technique used to identify individual neurons and their pre-synaptic partners-to characterize the input neurons of the α/βp Kenyon cells. Two types of mushroom body input neuron were identified in that, together, form about half of the total input the α/βp Kenyon cells receive in the dorsal accessory calyx. The first neuronal type-henceforth referred to as LOPNs-consists of a neuron that projects from the lobula, a region of the optic lobe specialized in detecting visual features, such as shape and motion. The second type of neuron-henceforth referred to as PLPPNs-consists of projection neurons that emerge from the posterior lateral protocerebrum, a brain region that receives input from the optic lobe. Interestingly, LOPN and PLPPNs do not project to the ventral accessory calyx and do not connect to the γd Kenyon cells. Based on these findings, it is concluded that there are two parallel pathways that convey visual information to the mushroom body: a pathway projecting from the medulla to the γd Kenyon cells and another pathway projecting from the lobula and posterior lateral protocerebrum to the α/βp Kenyon cells (Li, 2020).

This study has identified and characterized neurons projecting to the dorsal accessory calyx of the mushroom body and show that these neurons are pre-synaptic to the α/β_p Kenyon cells. Using a combination of genetic and anatomical techniques, it was possible to distinguish two different types of projection neuron: _LOPN projecting from the lobula-an area of the optic lobe processing visual features, such as shape and motion-and the PLPPNs projecting from the posterior lateral protocerebrum. Although the posterior lateral protocerebrum remains poorly characterized in D. melanogaster, evidence from other insects shows that this brain region receives input from the optic lobe. Interestingly, it was found that the dendrites formed by the _PLPPNs in the posterior lateral protocerebrum are in close proximity to neurons that project from the ventral medulla. Based on these results-and considering insights from the connectome-it is estimated that LOPNs and PLPPNs account for half of total input that α/βp Kenyon cells receive in the dorsal accessory calyx. LOPNs and PLPPNs do not extend axonal terminals into the ventral accessory calyx, the other calyx known to receive visual input, but rather extend axonal terminals into the dorsal accessory calyx and into the superior lateral protocerebrum. Likewise, the α/βp Kenyon cells do not connect to the visual projection neurons that are associated with the ventral accessory calyx. These findings suggest that the visual system is connected to the mushroom body via two parallel pathways: the α/βp Kenyon cells receive input from the lobula and the posterior lateral protocerebrum, whereas the γd Kenyon cells receive input directly from the medulla. Further functional studies are necessary to determine what kind of visual information is processed by the α/βp Kenyon cells (Li, 2020).

In Drosophila melanogaster, the mushroom body has long been studied as an olfactory processing center. However, evidence from many insects, including the honeybee Apis mellifera, shows that the mushroom body integrates sensory information across different modalities. In honeybees, the input region of the mushroom body, also called the calyx, is divided into different layers, and each layer receives input from either the olfactory or visual system. Because the dendrites of Kenyon cells are also restricted to specific layers, it has been suggested that, in the honeybee, multisensory integration does not occur at the level of individual Kenyon cells but rather at the population level. Although the honeybee mushroom body differs greatly from the Drosophila mushroom body-it contains about a hundred times as many Kenyon cells and its input region is divided in multiple complex layers-it appears that both mushroom bodies share a common fundamental connectivity principle: the segregation of input based on sensory modality. This connectivity mechanism is immediately apparent in the structural organization of the Drosophila melanogaster mushroom body: the Kenyon cells receiving input from the olfactory system all extend their dendrites into the main calyx, whereas the Kenyon cells receiving input from the visual system extend their dendrites either in the dorsal accessory calyx or the ventral accessory calyx. Many studies have demonstrated that the Kenyon cells that process olfactory information-those associated with the main calyx-integrate input broadly across the different types of olfactory projection neuron. Interestingly, it appears that the Kenyon cells that process visual information are wired differently (Li, 2020).

A thorough understanding is available of how olfactory Kenyon cells integrate input from the antennal lobe: most Kenyon cells receive, on average, input from seven projection neurons, and the projection neurons connecting to the same Kenyon cell share no apparent common features. Theoretical studies have shown that this random-like connectivity pattern enables the mushroom body to form sparse and decorrelated odor representations and thus maximizes learning. Randomization of sensory input is a connectivity pattern that is well suited for representing olfactory information-as an odor is encoded based on the ensemble of olfactory receptors it activates-and might not be suitable for representing visual information. Indeed, the results of this study suggest that specific visual features-the signals processed by the medulla and the ones processed by the lobula and the posterior lateral protocerebrum-need to be represented by two separate subpopulations of Kenyon cells. This observation mirrors anatomical studies of the honeybee brain: the neurons projecting from the lobula terminate in a different layer than the neurons projecting from the medulla. This arrangement might be essential to preserve distinct visual features when forming associative memories. Functional and behavioral studies are required to determine whether indeed the mushroom body represents multisensory stimuli in this manner (Li, 2020).

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Many neurons show compartmentalized activity, in which activity does not spread readily across the cell, allowing input and output to occur locally. However, the functional implications of compartmentalized activity for the wider neural circuit are often unclear. This problem was addressed in the Drosophila mushroom body, whose principal neurons, Kenyon cells, receive feedback inhibition from a non-spiking interneuron called APL. This study used local stimulation and volumetric calcium imaging to show that APL inhibits Kenyon cells' dendrites and axons, and that both activity in APL and APL's inhibitory effect on Kenyon cells are spatially localized (the latter somewhat less so), allowing APL to differentially inhibit different mushroom body compartments. Applying these results to the Drosophila hemibrain connectome predicts that individual Kenyon cells inhibit themselves via APL more strongly than they inhibit other individual Kenyon cells. These findings reveal how cellular physiology and detailed network anatomy can combine to influence circuit function (Amin, 2020).

This study has shown that activity in APL is spatially restricted intracellularly for both sensory-evoked and local artificial stimulation. This local activity in APL translates into a spatially non-uniform inhibitory effect on Kenyon cells that is strongest locally and becomes weaker farther from the site of APL stimulation. Finally, combining physiological and anatomical data -- APL's estimated space constant and the spatial arrangement of KC-APL and APL-KC synapses -- predicts that each Kenyon cell disproportionately inhibits itself more than other individual Kenyon cells (Amin, 2020).

This study showed that APL can locally inhibit Kenyon cells in multiple locations in the mushroom body. Remarkably, inhibition of Ca2+ influx in Kenyon cell axons could be stronger closer to the axon initial segment (in the posterior peduncle than farther from it, for example, stronger at the lower vertical lobe than the tip of the vertical lobe, both when stimulating APL with ATP or when directly applying GABA. At first glance, this observation is puzzling given the direction of action potential travel. GABA should act on Kenyon cells by suppressing depolarization through shunting inhibition in both dendrites and axons, as the GABAA receptor Rdl is expressed in both. Therefore, the observation of stronger proximal than distal inhibition appears to suggest that as action potentials travel from the axon initial segment toward the distal tip of the axon, they can enter a zone of shunting inhibition and be suppressed, yet recover on the other side. Such a scenario could occur if depolarization is suppressed enough to reduce voltage-gated Ca2+ influx in a local zone, yet remains sufficient on the other side of the inhibitory zone to trigger enough voltage-gated Na+ channels to regenerate the action potential. However, this interpretation seems unlikely. More likely, local inhibition might particularly suppress Ca2+ influx rather than depolarization, perhaps by acting through GABAB receptors; Kenyon cells express GABABR1 and GABABR2 and APL inhibits KCs partly via GABAB receptors, although their subcellular localization is unknown. Given that synaptic vesicle release requires Ca2+ influx, such inhibition would still locally suppress Kenyon cell synaptic output (Amin, 2020).

Local inhibition of Kenyon cell output predicts that activity of MBONs near the site of APL activation would be more strongly inhibited than MBONs far away. This prediction may be tested in future experiments, for example locally stimulating APL in the tip of the vertical lobe and comparing the inhibitory effect on MBONs in nearby compartments like α3 and α'3 vs. on MBONs in distant compartments like γ5 and β2 (Amin, 2020).

While APL inhibits Kenyon cells locally, the inhibitory effect spreads somewhat more widely than APL's own activity, though weakly. Local GABA application produces similar results to locally activating APL with ATP, suggesting that GCaMP6f signals in APL accurately predict GABA release. How can APL inhibit Kenyon cells where it itself is not active? Wider inhibition when inhibiting Kenyon cell dendrites can be easily explained as blocking action potentials, but the wider inhibition when inhibiting the axons is more puzzling. It is speculated that this result might arise from wider network activity. For example, Kenyon cells form recurrent connections with DPM and dopaminergic neurons and form synapses and gap junctions on each other. Through such connections, an odor-activated Kenyon cell might excite a neighboring Kenyon cell's axon; the neighbor might passively spread activity both forward and backward or, not having fired and thus not being in a refractory period, it might even be excited enough to propagate an action potential back to the calyx. Alternatively, Kenyon cells indirectly excite antennal lobe neurons in a positive feedback loop. In these scenarios, locally puffing GABA or activating APL artificially in the vertical lobe tip would block the wider network activity, thus reducing Ca2+ influx in the calyx. Although the calyx was not activated when stimulating Kenyon cells in the vertical lobe tip, this might be because simultaneous activation of all Kenyon cells (but not odor-evoked activation of ~10% of Kenyon cells) drives strong-enough local APL activation to block wider network activity. Future experiments may test these possibilities (Amin, 2020).

By combining physiological measurements of localized activity with the detailed anatomy of the hemibrain connectome, a model was built that predicts that the average Kenyon cell inhibits itself more than it inhibits other individual Kenyon cells. This prediction is supported by previous experimental results that some Kenyon cells can inhibit some Kenyon cells more than others, and that an individual Kenyon cell can inhibit itself. The model goes beyond these results in predicting that the average Kenyon cell actually preferentially inhibits itself, and by explaining how differential inhibition can arise from local activity in APL and the spatial arrangement of KC-APL and APL-KC synapses (Amin, 2020).

Note that these results do not contradict previous findings that Kenyon cell lateral inhibition is all-to-all. The finding of all-to-all inhibition was based on findings that whereas blocking synaptic output from all Kenyon cells vastly increases Kenyon cell odor responses (by blocking negative feedback via APL), blocking output from one subset of Kenyon cells has no effect on (for α'β' and γ Kenyon cells), or only weakly increases (for αβ Kenyon cells), odor responses of that subset. This finding is consistent with findings that predict only preferential, not exclusive, self-inhibition. For example, while blocking output from only γ Kenyon cells would remove γ-to-γ inhibition, γ Kenyon cells (only 1/3 of all Kenyon cells) should still get enough lateral inhibition from the other 2/3 of Kenyon cells to suppress their activity to normal levels (Amin, 2020).

The findings show that localized activity within APL has two broader implications for mushroom body function. First, local activity in APL leads to stronger inhibition of Kenyon cells nearby than far away, to the point that different compartments of the mushroom body lobes defined by dopaminergic and output neuron innervation can receive different inhibition. This local inhibition would allow the single APL neuron to function effectively as multiple inhibitory interneurons, much like mammalian and fly amacrine cells. Each 'sub-neuron' of APL could locally modulate the function of one mushroom body 'compartment', that is, a unit of dopaminergic neurons/Kenyon cells / mushroom body output neurons. Different compartments are innervated by different dopaminergic neurons (e.g., reward vs. punishment) and different mushroom body output neurons (e.g., avoid vs. approach), and they govern synaptic plasticity and hence learning by different rules (e.g., different speeds of learning/forgetting). Thus, APL could locally modulate different compartment-specific aspects of olfactory learning, especially given that different regions of APL respond differently to dopamine and electric shock punishment. If such local inhibition is important for learning, the fact that spatial attenuation of APL's inhibitory effect is gradual and incomplete could explain why mushroom body compartments are arranged in their particular order, with reward and punishment dopaminergic neurons segregated into the horizontal and vertical lobes, respectively. Under this scenario, APL would serve two distinct, spatially segregated functions: enforcing Kenyon cell sparse coding in the calyx, and modulating learning in the compartments of the lobes (Amin, 2020).

Second, the finding of disproportionate self-inhibition compared to other-inhibition provides a new perspective on APL's function. Inhibition from APL sparsens and decorrelates Kenyon cell odor responses to enhance learned odor discrimination but in general, decorrelation is better served by all-to-all lateral inhibition than by self-inhibition. Self-inhibition does help decorrelate population activity by pushing some neurons' activity below spiking threshold; this could occur if APL can be activated by subthreshold activity in Kenyon cells, for example, from KC-APL synapses in Kenyon cell dendrites. However, this effect of self-inhibition is better thought of, not as decorrelation per se, but rather as gain control, effectively equivalent to adjusting the threshold or gain of excitation according to the strength of stimulus. Of course, lateral inhibition is still a strong force in the mushroom body: given that there are ~2000 Kenyon cells, the sum total lateral inhibition that an individual Kenyon cell receives would still be stronger than its own self-inhibition, even with the predicted imbalance. Why might the predicted 'bonus' self-inhibition be useful? Beyond its role in sparse coding, APL inhibition is also thought to function as a gating mechanism to suppress olfactory learning; for such a function it would make sense for Kenyon cells to disproportionately inhibit themselves. Future work will address how lateral inhibition interacts with other functions for APL in this local feedback circuit (Amin, 2020).

It has been evidenced that vision-based decision-making in Drosophila consists of both simple perceptual (linear) decision and value-based (non-linear) decision. This paper proposes a general computational spiking neural network (SNN) model to explore how different brain areas are connected contributing to Drosophila linear and nonlinear decision-making behavior. First, the SNN model could successfully describe all the experimental findings in fly visual reinforcement learning and action selection among multiple conflicting choices as well. Second, the computational modeling shows that dopaminergic neuron-GABAergic neuron-mushroom body (DA-GABA-MB) works in a recurrent loop providing a key circuit for gain and gating mechanism of nonlinear decision making. Compared with existing models, this model shows more biologically plausible on the network design and working mechanism, and could amplify the small differences between two conflicting cues more clearly. Finally, based on the proposed model, the unmanned aerial vehicle (UAV) could quickly learn to make clear-cut decisions among multiple visual choices and flexible reversal learning resembling to real fly. Compared with linear and uniform decision-making methods, the DA-GABA-MB mechanism helps UAV complete the decision-making task with fewer steps (Zhao, 2020).

In pursuit of food, hungry animals mobilize significant energy resources and overcome exhaustion and fear. How need and motivation control the decision to continue or change behavior is not understood. Using a single fly treadmill, this study shows that hungry flies persistently track a food odor and increase their effort over repeated trials in the absence of reward suggesting that need dominates negative experience. It was further shown that odor tracking is regulated by two mushroom body output neurons (MBONs) connecting the MB to the lateral horn. These MBONs, together with dopaminergic neurons and Dop1R2 signaling, control behavioral persistence. Conversely, an octopaminergic neuron, VPM4, which directly innervates one of the MBONs, acts as a brake on odor tracking by connecting feeding and olfaction. Together, these data suggest a function for the MB in internal state-dependent expression of behavior that can be suppressed by external inputs conveying a competing behavioral drive (Sayin, 2019).

Flexibility is an important factor in an ever in-flux environment, where scarcity and competition are the norm. Without persistence to achieve its goals, however, an animal's strive to secure food, protect its offspring, or maintain its social status is in jeopardy. Therefore, sensory cues related to food or danger often elicit strong impulses. However, these impulses must be strictly controlled to allow for coherent goal-directed behavior and to permit behavioral transitions when sensible. Inhibition of antagonistic behavioral drives at the cognitive and physiological level has been proposed as a major task of a nervous system. Which sensory cues and ultimately which behaviors are prioritized and win depends on the animal's metabolic state, internal motivation, and current behavioral context. How this is implemented at the level of individual neurons, circuit motifs, and mechanisms remains an important open question (Sayin, 2019).

Like most animals, energy-deprived flies prioritize food seeking and feeding behavior. To find food, flies can follow olfactory or visual cues over long distances. External gustatory cues provide information about the type and quality of the eventually encountered food. However, only internal nutrient levels will provide reliable feedback about the quality and quantity of a food source and ultimately suppress food-seeking behaviors. Therefore, food odor, the taste of food, and post-ingestive internal feedback signals induce sequential and partly antagonistic behaviors. Interestingly, chemosensory and internal feedback systems typically mediated by distinct neuromodulators appear to converge in the mushroom body (MB). How neurons and neural circuits signal and combine external and internal cues to maintain or suppress competing behavioral drives is not well understood (Sayin, 2019).

In mammals, norepinephrine (NE) released by a brain stem nucleus, the locus coeruleus, has been implicated in controlling the balance between persistence and action selection. The potential functional counterpart of NE in insects could be octopamine (OA). Flies lacking OA indeed show reduced arousal, for instance upon starvation. Additionally, OA neurons (OANs) gate appetitive memory formation of odors and also modulate taste neurons and feeding behavior. OANs are organized in distinct clusters and project axons to diverse higher brain regions in a cell type-specific manner. The precise roles and important types of OA and NE neurons in state-dependent action selection remain to be elucidated (Sayin, 2019).

Similar to NE and OA, dopamine (DA) is being studied in many aspects of behavioral adaptation and flexibility. Different classes of DA neurons (DANs) innervating primarily the MB signal negative or positive context, or even wrong predictions (Sayin, 2019 and references therein).

This study took advantage of the small number and discrete organization of neuromodulatory neurons in the fly brain to analyze the mechanistic relationship between motivation-dependent persistence in one behavior and the decision to disengage and change to another behavior. Using a single fly spherical treadmill assay, this study found that hungry flies increase their effort to track a food odor with every unrewarded trial. MB output through two identified MBONs (MBON-γ1pedc>α/β and MBON-α2sc) is required for persistent odor tracking. MBON-α2sc provides a MB connection to the lateral horn (LH), where it can modify innate food odor attraction. Furthermore, this study pinpoints a specific type of OAN, VPM4 (ventral paired medial), which connects feeding centers directly to MBON-γ1pedc>α/β and disrupts food odor tracking. Finally, the experimental data suggest that persistent tracking depends on DANs, including PPL1-γ1pedc, and signaling through dopamine receptor Dop1R2 in αβ-type KCs. Based on these results, it is proposed that MB output and a direct external input, depending on internal state and motivation, gradually promote or interrupt ongoing behavior (Sayin, 2019).

What drives gradually increasing persistence in behavior? For the fly, a model is proposed by which a circuit module of KCs, MBONs, and DANs drive gradually increasing odor tracking, which can be efficiently suppressed by extrinsic MBON-innervating feeding-related OANs. Behavioral persistence has been previously analyzed in flies in a different context. For instance, courtship of fly males and copulation with a female are maintained by dopaminergic neurons in the ventral nerve cord, where they counteract GABAergic neurons. In that scenario, DANs in the ventral nerve cord maintain an ongoing behavior and prevent male premature disengagement before successful insemination (Sayin, 2019).

The experimental data also implicate DANs, primarily from within the PPL1 (e.g., PPL1-γ1pedc) and PPL2ab clusters, and Dop1R2 signaling. In particular, inactivation of synaptic output of DANs positive for TH-Gal4 as well as loss of Dop1R2 in αβ-type KCs reduced the increase in odor tracking from trial to trial, while not affecting the speed at first odor stimulation. These data suggest that TH+ DANs promote goal-directed movement, i.e., odor tracking, through a Dop1R2-dependent mechanism in KCs (Sayin, 2019).

MBON-γ1pedc>αβ, which receives dopaminergic input by PPL1-γ1pedc, is required for odor tracking. Moreover, this study also observed a trial-to-trial decrease in odor response of this MBON, matching the dopamine-induced synaptic depression previously observed in MBONs upon learning. Notably, PPL1-γ1pedc activates Dop1R2 in MBON-γ1pedc>αβ, a signal recently found to be critical for appetitive long-term memory. Nevertheless, it appears that, in addition to PPL1-γ1pedc, other DANs regulate behavioral persistence by modulating in particular αβ-KCs. It is intriguing to speculate about a common function of Dop1R2 in the formation of long-lasting aversive memory induced by repeatedly pairing odor with an aversive experience and the behavior examined in this study: increased and persistent expression of a behavior induced by the experience of repeated failure to reach a goal (Sayin, 2019).

The experimental data further implicated MBON-α2sc, which is connected to MBON-γ1pedc>αβ. Calcium imaging data are consistent with an inhibitory interaction between the two MBONs. However, some of the behavioral data and prior imaging data do not support an inhibitory connection. Furthermore, MBON-γ1pedc>αβ projects to other brain regions and downstream targets, and similarly MBON-α2sc receives additional inputs—all of which could be equally or more important for persistent behavior than a direct connection between these two MBONs. Finally, some DANs respond to movement, including PPL1-γ2α'1/MV1. Although no essential role of this particular neuron was found in odor tracking persistence, movement might contribute to the activity of MBONs responding the odorant (Sayin, 2019).

Remarkably, MBON-α2sc connects the MB to neurons within the LH. Thus, it is speculated that the LH might assign an odor to its corresponding behavioral category, such as 'food-related' for vinegar, while the MB acts as a top-down control to gauge the expression of an innate behavior (i.e., tracking an appetitive odor) according to state and experience (Sayin, 2019).

The behavioral data led to the proposal of a circuit model. Using computational modeling, this study tested whether the MB network including DANs and MBONs could, in theory, produce the observed behavior. Indeed, it was found that a simplified recurrent circuit of KCs, DANs, and MBONs can account for the observed behavioral persistence and also the measured MBON-γ1pedc>αβ odor responses. While this model cannot replace experimental evidence, it forms a useful theoretical framework for future studies on the role of the MB in behavioral persistence (Sayin, 2019).

Based on the present data and computational predictions, a model is proposed by which the recurrent circuit architecture of the MB, in addition to storing information for future behavior, is ideally suited to maintain and gradually change ongoing behavior, for instance by modulating output of the LH, according to the animal's internal state and needs (Sayin, 2019).

The use of an olfactory treadmill has allowed dissection of the different aspects of a food search. In particular, how does food and feeding suppress food search if the sensory cue, the odor, is still present? OA-VPM4 connects feeding centers (i.e., SEZ) directly with odor tracking-promoting MBON-γ1pedc>αβ and inhibits its activity suggesting an inhibitory connection between VPM4 and the MBON. Nevertheless, it cannot be excluded that OA-VPM4 signals through multiple mechanisms including OA and possibly other neurotransmitters. In addition, a recent study showed that activation of VPM4 promotes proboscis extension to sugar. Although a direct role in taste detection through pharynx or labellum appears unlikely, it is possible that feeding behavior itself (e.g., lymphatic sugar, food texture, activity of feeding muscles) are detected and/or promoted by these neurons and then brought to the MB. It is proposef that VPM4 is a direct mediator between olfactory-guided food search and the rewarding experience of feeding and related behavior (Sayin, 2019).

The data provide a neural circuit mechanism empowering flies to express and prioritize behavior in a need- and state-dependent manner. It is exciting to speculate that fundamentally similar circuit motifs might exist in NE and DA neuron-containing circuits in the mammalian brain, governing the organization of behavior in a flexible and context-dependent manner by integrating internal and external context. For instance, noradrenergic neurons of the brainstem nucleus of the solitary tract (NST) receive taste information, and input from the gastrointestinal tracts, lungs, and heart. Neurons in the NST project to multiple brain regions including the amygdala, hypothalamus, and insular cortex, all of which receive internal state as well as other sensory information (Sayin, 2019).

The data in the fly provide an experimental and theoretical framework for a better understanding of the fundamental circuit mechanisms underpinning neuromodulation of context-dependent behavioral persistence and withdrawal (Sayin, 2019).

Animals employ diverse learning rules and synaptic plasticity dynamics to record temporal and statistical information about the world. However, the molecular mechanisms underlying this diversity are poorly understood. The anatomically defined compartments of the insect mushroom body function as parallel units of associative learning, with different learning rates, memory decay dynamics and flexibility. This study shows that nitric oxide (NO) acts as a neurotransmitter in a subset of dopaminergic neurons in Drosophila. NO's effects develop more slowly than those of dopamine and depend on soluble guanylate cyclase in postsynaptic Kenyon cells. NO acts antagonistically to dopamine; it shortens memory retention and facilitates the rapid updating of memories. The interplay of NO and dopamine enables memories stored in local domains along Kenyon cell axons to be specialized for predicting the value of odors based only on recent events. These results provide key mechanistic insights into how diverse memory dynamics are established in parallel memory systems (Aso, 2019).

An animal's survival in a dynamically changing world depends on storing distinct sensory information about their environment as well as the temporal and probabilistic relationship between those cues and punishment or reward. Thus it is not surprising that multiple distributed neuronal circuits in the mammalian brain have been shown to process and store distinct facets of information acquired during learning. Even a simple form of associative learning such as fear conditioning induces enduring changes, referred to as memory engrams, in circuits distributed across different brain areas. Do these multiple engrams serve different mnemonic functions, what molecular and circuit mechanisms underlie these differences, and how are they integrated to control behavior? Localizing these distributed engrams, understanding what information is stored in each individual memory unit and how units interact to function as one network are important but highly challenging problems. The Drosophila mushroom body (MB) provides a well-characterized and experimentally tractable system to study parallel memory circuits. Olfactory memory formation and retrieval in insects requires the MB. In associative olfactory learning, exposure to an odor paired with a reward or punishment results in formation of a positive- or negative-valence memory, respectively. In the MB, sensory stimuli are represented by the sparse activity of ~2,000 Kenyon cells (KCs). Each of 20 types of dopaminergic neurons (DANs) innervates compartmental regions along the parallel axonal fibers of the KCs. Similarly, types of mushroom body output neurons (MBONs) arborize their dendrites in specific axonal segments of the KCs; together, the arbors of the DANs and MBONs define the compartmental units of the MB. Activation of individual MBONs can cause behavioral attraction or repulsion, depending on the compartment in which their dendrites arborize, and MBONs appear to use a population code to govern behavior (Aso, 2019).

A large body of evidence indicates that these anatomically defined compartments of the MB are also the units of associative learning. Despite the long history of behavioral genetics in fly learning and memory, many aspects of the signaling pathways governing plasticity -- especially whether they differ between compartments -- remain poorly understood. Nevertheless, dopaminergic neurons and signaling play a key role in all MB compartments, and flies can be trained to form associative memories by pairing the presentation of an odor with stimulation of a single dopaminergic neuron. Punishment or reward activates distinct sets of DANs that innervate specific compartments of the MB. Activation of the DAN innervating a MB compartment induces enduring depression of KC-MBONs synapses in those specific KCs that were active in that compartment at the time of dopamine release. Thus, which compartment receives dopamine during training appears to determine the valence of the memory, while which KCs were active during training determines the sensory specificity of the memory (Aso, 2019).

Compartments operate with distinct learning rules. Selective activation of DANs innervating specific compartments has revealed that they can differ extensively in their rates of memory formation, decay dynamics, storage capacity, and flexibility to learn new associations. For instance, the dopaminergic neuron PAM-α1 can induce a 24h memory with a single 1-minute training session, whereas PPL1-α3 requires ten repetitions of the same training to induce a 24h memory. PPL1-γ1pedc (aka MB-MP1) can induce a robust short-lasting memory with a single 10-second training, but cannot induce long-term memories even after 10 repetitions of a 1-minute training. PAM-α1 can write a new memory without compromising an existing memory, whereas PPL1-γ1pedc extinguishes the existing memory when writing a new memory. What molecularand cellular differences are responsible for the functional diversity of these compartments? Some differences might arise from differences among KC cell types, but memory dynamics are different even between compartments that lie along the axon bundles of the same Kenyon cells (for example, α1 and α3). This paper shows that differences in memory dynamics between MB compartments can arise from the deployment of distinct cotransmitters by the DAN cell types that innervate them (Aso, 2019).

Evidence from a wide range of organisms establishes that dopaminergic neurons often release a second neurotransmitter, but the role of such cotransmitters in diversifying neuronal signaling is much less clear. In rodents, subsets of dopaminergic neurons co-release glutamate or GABA. In mice and Drosophila, single-cell expression profiling reveals expression of diverse neuropeptides in dopaminergic neurons. EM connectome studies of the mushroom body in adult and larval Drosophila reveal the co-existence of small-clear-core and large- dense-core synaptic vesicles in individual terminals of dopaminergic neurons; moreover, the size of the observed large-dense-core 02 vesicles differs between DAN cell types (Aso, 2019).

This study found that NOS, the enzyme that synthesizes NO, was located in the terminals of a subset of DAN cell types. NOS catalyzes the production of nitric oxide (NO) from L-arginine. Drosophila NOS is regulated by Ca2+/calmodulin, raising the possibility that NO synthesis might be activity dependent. Furthermore, the localization of the NOS1 protein in the axonal terminals of DANs is consistent with NO serving as a cotransmitter. The conclusion that NO acts as a neurotransmitter is supported by the observation that NO signaling requires the presence of a putative receptor, soluble guanylate cyclase, in the postsynaptic Kenyon cells. This role contrasts with the proposed cell-autonomous action of NOS in the ellipsoid body, in which NO appears to target proteins within the NOS-expressing ring neurons themselves, rather than conveying a signal to neighboring cells. The valence-inversion phenotype observed when PPL1-γ1pedc was optogenetically activated in a dopamine-deficient background can be most easily explained if NO induces synaptic potentiation between odor-activated KCs and their target MBONs. Modeling work is consistent with this idea, but testing this idea and 19 other possible mechanisms for NO action will require physiological experiments (Aso, 2019).

During olfactory learning, the concentration of Ca2+ in KC axons represents olfactory information. The coincidence of a Ca2+ rise in spiking KCs and activation of the G-protein-coupled Dop1R1 dopamine receptor increases adenylyl cyclase activity. The resultant cAMP in turn activates protein kinase A, a signaling cascade that is important for synaptic plasticity and memory formation throughout the animal phyla. In contrast, when DANs are activated without KC activity, and thus during low intracellular Ca2+ in the KCs, molecular pathways involving the Dop1R2 receptor, Rac1 and Scribble facilitate decay of memory (Aso, 2019).

This study found that NOS in PPL1-γ1pedc shortens memory retention, while facilitating fast updating of memories in response to new experiences. These observations could be interpreted as indicating that NO regulates forgetting. Indeed, NO-dependent effect requires scribble in KCs, a gene previously reported as a component of active forgetting. However, it is an open question whether the signaling pathways for forgetting, which presumably induce recovery from synaptic depression, are related to signaling cascades downstream of NO and guanylate cyclase, which appear to be able to induce memory without prior induction ofsynaptic depression by dopamine. Lack of detectable 1-day memory formation after spaced training with PPL1-γ1pedc can be viewed as a balance between two distinct, parallel biochemical signals, one induced by dopamine and the other by NO, rather than the loss of information (that is, forgetting). Confirming this interpretation will require better understanding of the signaling pathways downstream of dopamine and NO. The search for such pathways will be informed by the prediction from modeling that dopamine and NO may alter two independent parameters that define synaptic weights with a multiplicative interaction (Aso, 2019).

In the vertebrate cerebellum, which has many architectural similarities to the MB, long-term-depression at parallel fiber-Purkinje cell synapses (equivalent to KC-MBON synapses) induced by climbing fibers (equivalent to DANs) can coexist with long-term-potentiation by NO. In this case, the unaltered net synaptic weight results from a balance between coexisting LTD and LTP rather than recovery from LTD. This balance was suggested to play an important role in preventing memory saturation in the cerebellum 59 and allowing reversal of motor learning. In the Drosophila MB, a similar facilitation was observed of reversal learning by NO. The antagonistic roles of NO and synaptic depression may be a yet another common feature of the mushroom body and the cerebellum (Aso, 2019).

Opposing cotransmitters have been observed widely in both invertebrate and vertebrate neurons. A common feature in these cases is that the transmitters have distinct time courses of action. For instance, hypothalamic hypocretin-dynorphin neurons that are critical for sleep and arousal synthesize excitatory hypocretin and inhibitory dynorphin. When they are released together repeatedly, the distinct kinetics of their receptors result in an initial outward current, then little current, and then an inward current in the postsynaptic cells. In line with these observations, this study found that dopamine and NO show distinct temporal dynamics: NO-dependent memory requires repetitive training and takes longer to develop than dopamine-dependent memory. What molecular mechanisms underlie these differences? Activation of NOS may require stronger or more prolonged DAN activation than does dopamine release. Alternatively, efficient induction of the signaling cascade in the postsynaptic KCs might require repetitive waves of NO input. Direct measurements of release of dopamine and NO, and downstream signaling events by novel sensors will be needed to address these open questions (Aso, 2019).

Decades of behavioral genetic studies have identified more than one hundred genes underlying olfactory conditioning in Drosophila. Mutant andtargeted rescue studies have been used to map the function of many memory-related genes encoding synaptic or intracellular signaling proteins (for example, rutabaga, DopR1/dumb, DopR2/DAMB, PKA-C1/DC0, Synapsin, Bruchpilot, Orb2 and Rac1) to specific subsets of Kenyon cells. However, it is largely unknown if these proteins physically colocalize at the same KC synapses to form intracellular signaling cascades. Some of 97 these proteins might preferentially localize to specific MB compartments. Alternatively, they may distribute uniformly along the axon of Kenyon cells, but be activated in only specific compartments. Identification of cell-type-specific cotransmitters in DANs enabled a beginning to the exploration of this question (Aso, 2019).

Optogenetic activation of specific DANs was used to induce memory in specific MB compartments, while manipulating genes in specific types of KCs. This approach allowed mapping and characterization of the function of memory-related genes at a subcellular level. For example, the Gycbeta100B gene, which encodes a subunit of sGC, has been identified as 'memory suppressor gene' that enhances memory retention when pan-neuronally knocked down, but the site of its action was unknown. Gycbeta100B appears to be broadly dispersed throughout KC axons, based on the observed distribution of a Gycbeta100B-EGFP fusion protein. The experiments ectopically expressing NOS in PPL1-α3 DANs that do not normally signal with NO is most easily explained if sGC is available for activation in all MB compartments. What are the molecular pathways downstream to cGMP? How do dopamine and NO signaling pathways interact in regulation of KC synapses? Previous studies and RNA-Seq data suggest several points of possible crosstalk. In cultured KCs from cricket brains, cGMP-dependent protein kinase (PKG) mediates NO-induced augmentation of a Ca2+ channel current. However, no expression of either of the genes encoding Drosophila PKGs (foraging and Pkg21D) was detected in KCs in RNA-Seq studies. On the other hand, cyclic nucleotide-gated channels and the cGMP-specific phosphodiesterase Pde9 are expressed in KCs. Biochemical studies have shown that the activity of sGC is calcium dependent and that PKA can enhance the NO-induced activity of sGC by phosphorylating sGC; sGC isolated from flies mutant for adenylate cyclase, rutabaga, show lower activity than sGC from wild-type brains, suggesting crosstalk between the cAMP and cGMP pathways (Aso, 2019).

All memory systems must contend with a tension between the strength and longevity of the memories they form. The formation of a strong immediate memory interferes with and shortens the lifetimes of previously formed memories, and reducing this interference requires a reduction in initial memory strength that can only be overcome through repeated exposure. Theoretical studies have argued that this tension can be resolved by memory systems that exhibit a heterogeneity of timescales, balancing the need for both fast, labile memory and slow consolidation of reliable memories. The mechanisms responsible for this heterogeneity, and whether they arise from complex signaling within synapses themselves), heterogeneity across brain areas, or both, have not been identified (Aso, 2019).

NO acts antagonistically to dopamine and reduces memory retention while facilitating the rapid updating of memory following a new experience. Viewed in isolation, the NO-dependent reduction in memory retention within a single compartment may seem disadvantageous, but in the presence of parallel learning pathways, this shortened retention may represent a key mechanism for the generation of multiple memory timescales that are crucial for effective learning. During shock conditioning, for example, multiple DANs respond to the aversive stimulus, including PPL1-γ1pedc, PPL1-γ2α'1, PPL1-α3. This study has shown that optogenetic activation of these DAN cell types individually induces negative-valence olfactory memory with distinct learning rates. The NOS-expressing PPL1-γ1pedc induces memory with the fastest learning rate in a wild-type background, and this study shows that it induces an NO-dependent memory trace when dopamine synthesis is blocked, with a much slower learning rate and opposite valence (Aso, 2019).

Robust and stable NO-dependent effects were only observed when training was repeated 10 times. Under such repeated training, compartments with slower learning rates, such as α3, form memory traces in parallel to those formed in γ1pedc. Thus, flies may benefit from the fast and labile memory formed in γ1pedc without suffering the potential disadvantages of 58 shortened memory retention, as long-term memories are formed in parallel in other compartments. The Drosophila MB provides a tractable experimental system to study the mechanisms and benefits of diversifying learning rate, retention, and flexibility in parallel memory units, as well as exploring how the outputs from such units are integrated to drive behavior (Aso, 2019).

The alpha'beta' subtype of Drosophila mushroom body neurons (MBn) is required for memory acquisition, consolidation and early memory retrieval after aversive olfactory conditioning. However, in vivo functional imaging studies have failed to detect an early forming memory trace in these neurons as reflected by an enhanced G-CaMP signal in response to presentation of the learned odor. This study shows that aversive olfactory conditioning suppresses the calcium responses to the learned odor in both alpha'3 and alpha'2 axon segments of alpha'beta' MBn and in the dendrites of alpha'3 MBOn immediately after conditioning using female flies. Notably, the cellular memory traces in both alpha'3 MBn and alpha'3 MBOn are short-lived and persist for less than 30 min. The suppressed response in alpha'3 MBn is accompanied by a reduction of acetylcholine (ACh) release, suggesting that the memory trace in postsynaptic alpha'3 MBOn may simply reflect the suppression in presynaptic alpha'3 MBn. Furthermore, this study shows that the alpha'3 MBn memory trace does not occur from the inhibition of GABAergic neurons via GABAA receptor activation. Since activation of the alpha'3 MBOn drives approach behavior of adult flies, the results demonstrate that aversive conditioning promotes avoidance behavior through suppression of the alpha'3 MBn-MBOn circuit (Zhang, 2019).

Animals learn to avoid a neutral stimulus that is repeatedly coupled with an unpleasant one. This type of learning, aversive associative learning, induces cellular memory traces in engram cells in the brain and changes the representation of the neutral stimulus. In Drosophila, several memory traces detected with the calcium indicator G-CaMP have been observed in the mushroom body (MB), a brain region critical for olfactory learning and memory. These memory traces are detectable across discrete time periods extending from 30 min to several days after training. However, memory traces that form immediately in the MB after conditioning have not been detected with in vivo Ca2+ imaging (Zhang, 2019).

The MB is composed of ~2000 intrinsic neurons in each hemisphere that integrates olfactory cues received from antennal lobe projection neurons with aversive or rewarding stimuli from two clusters (PPL1, PAM) of dopamine neurons. MBn are classified into three major subtypes: α'β', αβ, and γ neurons, based on their birth order and projection patterns of their axons in the brain. The axons of α'β' and αβ MBn bifurcate and project within the vertical α'/α lobe and horizontal β'/β lobe neuropil, whereas the axons of γ neurons project only within the horizontal γ lobe neuropil. Although each of these MBn subtypes contributes to aversive olfactory memory, they do so at different times after conditioning, with synaptic transmission from the α'β' and γ MBn required for robust expression of early and intermediate-term memory (immediate to 3 h) and synaptic transmission from the αβ MBn having a more pronounced role for memory expression after 3 h. Importantly, although the α'β' MBn are required for memory acquisition, consolidation and early memory retrieval, no immediate memory trace in α'β' MBns has been detected using in vivo Ca2+ imaging (Zhang, 2019).

Five different types of MB output neurons (MBOns) tile the α'β' lobe with their dendritic trees into five discrete compartments, matching the tiling by axon terminals from presynaptic DAns. Several of these MBOns are required for aversive memory or appetitive memory expression, and intermediate-term memory traces (~1-2 h after conditioning) have been detected in some of these neurons. However, early memory traces have not been documented in these MBOns, and the relationship between such putative traces and those in the presynaptic MBn is unexplored. Connectome studies revealed that DAns make direct connection with MBOns, opening the possibility that MBOns form traces independently of the MBn (Zhang, 2019).

This study shows that a cellular memory trace forms immediately after conditioning in the MBn axons occupying the α'3 compartment and in the downstream α'3 MBOn. Functional Ca2+ imaging reveals that aversive conditioning suppresses subsequent responses to the learned odor in both the presynaptic α'3 compartment and the postsynaptic α'3 MBOn across a similar time period, suggestive of a causal relationship. In vivo ACh imaging revealed that the suppressed Ca2+ responses are accompanied by reduced ACh release in the α'3 compartment, supporting the model that the α'3 MBOn memory trace occurs from suppressed presynaptic activity. This study also shows that the conditioning-induced suppression in the α'3 compartment does not occur from increased inhibition through the Resistance to dieldrin (Rdl) GABAA receptor, indicating that mechanisms other than Rdl receptor activation are responsible for the suppression of activity (Zhang, 2019).

This study provides evidence for the existence of immediate cellular memory traces that form in at least two adjacent segments of the axons in the vertical lobe neuropil of the α'β' MBn and at least one (α'3 MBOn) of the corresponding output neurons. These memory traces, detected as decreased Ca2+ responses to the CS+ odor immediately after conditioning when compared with preconditioning responses, and persisting for >30 min before the response properties return to the naive state, are consistent with the fact that α'β' MBn are required for memory acquisition, consolidation and early memory retrieval. Several other previously characterized early memory traces due to odor conditioning provide an interesting background to these newly discovered traces. The neurites of the DPM neurons innervating the vertical MB lobe neuropil exhibit an increased Ca2+ response to the learned odor from ~30-70 min after conditioning. A memory trace forms in the antennal lobe, registered as the recruitment of new projection neuron activity in response to the learned odor, that lasts <10 min after conditioning. The activity of GABAergic APL neurons that synapse in the vertical lobe neuropil of the MBn is suppressed for a period of a few minutes after conditioning. Further, in vivo functional imaging of the α'β' MBn axons revealed an early memory trace displayed as increased Ca2+ influx by 30 min after conditioning that persists for at least 1 h. The action of these five memory traces, together along with other unknown traces, may provide the cellular modifications required for behavioral performance gains to be made across the first hour after conditioning. Memory traces in compartments other than α'3 and/or their MBOns may underlie the requirement of α'β' MBn for memory retrieval beyond the first hour (Zhang, 2019).

However, the developmental trajectory of the memory traces forming in the α'β' MBn lobe is of additional interest. As indicated above, a cellular memory trace forms in these neurons by 30 min after conditioning that is manifested as an increased Ca2+ response to the conditioned odor. The data presented in this study show that the α'β' MBn axons become suppressed across the first ~15 min after conditioning. The combined studies thus indicate that the CS+ odor response properties in the α'β' MBn axons are initially suppressed after conditioning but then become enhanced at later times. The time courses for the two cellular memory traces do not match exactly (0-15 min for the suppression and ~30-60 min for the increase) given the current data showing no detectable increase at 30 or 45 min, but this is easily explained by variation in the strength of conditioning or minor technical differences between the two studies. Thus, the most parsimonious conclusion is that the vertical axon compartments of the α'β' MBn initially exhibit a suppressed response to the CS+ followed by an increased response with the transition from suppression to enhancement occurring somewhere between ~30-45 min after conditioning. How this evolution in response properties from negative to positive with time translates into behavioral memory expression remains unclear (Zhang, 2019).

The suppressed responses to the CS+ odor were found in both the axon segments of the α'β' MBn and the dendrites of α'3 MBOn. Given that activation of α'3 MBOn drives approach behavior, the results are consistent with the model that aversive conditioning promotes avoidance through suppressing the MBn-MBOn circuits that signal positive valence, at least across the time that the MBOn responses are suppressed. Notably, the memory traces in α'3 MBn and α'3 MBOn persisted for the similar time, raising the question of whether the suppressed responses form independently or whether the α'3 MBOn memory trace simply reflects the presynaptic one. The data support the model in which the suppressed α'β' MBn responses are simply transmitted to the MBOn from reduced synaptic activity: the suppressed Ca2+ response in α'3 MBn axon compartment is correlated with reduced ACh release and the suppressed response in the α'3 MBOn dendrites. Behavioral data suggest that if the early cellular memory traces that form in the α'3 MBn-MBOn circuit cannot be readout precisely, the expression of behavioral memory early after conditioning becomes impaired. However, the possibility that memory traces can be formed independently in α'3 MBOn cannot be excluded (Zhang, 2019).

This study formulated the hypothesis that the immediate suppression in α'3 MBn axons after aversive conditioning might be due to enhanced GABAergic input to the α'3 compartment in an effort to delineate the underlying mechanism. However, attempts to detect any impairment of the immediate suppression in α'3 axonal compartment failed when Rdl GABAA receptor was knocked down in α'β' neurons. Thus, the data argue against attributing the suppression in the α'3 compartment to GABAergic inhibition through GABAA receptor (Zhang, 2019).

Two neuronal populations, c673a and Fru-GAL4, regulate fat storage in fruit flies. Both populations partially overlap with a structure in the insect brain known as the mushroom body (MB), which plays a critical role in memory formation. This overlap prompted an examination of whether the MB is also involved in fat storage homeostasis. Using a variety of transgenic agents, the neural activity of different portions of the MB and associated neurons were selectively manipulated to decipher their roles in fat storage regulation. The data show that silencing of MB neurons that project into the &alpha:'β' lobes decreases de novo fatty acid synthesis and causes leanness, while sustained hyperactivation of the same neurons causes overfeeding and produces obesity. The &alpha:'β' neurons oppose and dominate the fat regulating functions of the c673a and Fru-GAL4 neurons. It was also shown that MB neurons that project into the γ lobe also regulate fat storage, probably because they are a subset of the Fru neurons. It was possible to identify input and output neurons whose activity affects fat storage, feeding, and metabolism. The activity of cholinergic output neurons that innervating the β'2 compartment (MBON-β'2mp and MBON-γ5β'2a) regulates food consumption, while glutamatergic output neurons innervating α' compartments (MBON-γ2α'1 and MBON-α'2) control fat metabolism. This study has identified a new fat storage regulating center, the α'β' lobes of the MB. The study also delineated the neuronal circuits involved in the actions of the α'β' lobes, and showed that food intake and fat metabolism are controlled by separate sets of postsynaptic neurons that are segregated into different output pathways (Al-Anzi, 2018).

Current thought envisions dopamine neurons conveying the reinforcing effect of the unconditioned stimulus during associative learning to the axons of Drosophila mushroom body Kenyon cells for normal olfactory learning. This study shows, using functional GFP reconstitution experiments, that Kenyon cells and dopamine neurons form axoaxonic reciprocal synapses. The dopamine neurons receive cholinergic input via nicotinic acetylcholine receptors from the Kenyon cells; knocking down these receptors impairs olfactory learning revealing the importance of these receptors at the synapse. Blocking the synaptic output of Kenyon cells during olfactory conditioning reduces presynaptic calcium transients in dopamine neurons (DAn), a finding consistent with reciprocal communication. Moreover, silencing Kenyon cells decreases the normal chronic activity of the dopamine neurons. These results reveal a new and critical role for positive feedback onto dopamine neurons through reciprocal connections with Kenyon cells for normal olfactory learning (Cervantes-Sandoval, 2017).

The results demonstrate that DAn are both pre- and post-synaptic to KC through axoaxonic reciprocal connections, in contrast to current models which envision them as providing only pre-synaptic input. The DAn>KC half of the reciprocal synapse employs DA as neurotransmitter, although the possibility cannot be ruled out that other neurotransmitters are co-released with DA. The KC>DAn half of the reciprocal synapse is cholinergic. However, the fact that it was not possible to observe both cAMP and calcium responses in DAn with KC stimulation suggests that there may be other mediators of this reciprocal connection. Blocking the cholinergic input to DAn attenuates aversive olfactory learning, providing evidence that its function is, at least in part, to provide an amplification signal for the initial DA release due to activating the US pathway. Consistent with this role it was found that silencing KC impairs DAn presynaptic calcium responses to conditioning, odor and shock stimuli, presumably influencing dopamine release and explaining the learning phenotype. Overall, results support the existence of a positive feedback loop required for optimal learning. It is envisioned that DAn receive direct input from the US during conditioning which is conveyed to KC. The KCs also receive coincident olfactory input and this coincidence provides positive feedback onto the DAn through cholinergic synapses to further increase DAn activity (Cervantes-Sandoval, 2017).

The results also show that KC input to DAn shapes their ongoing or chronic activity. It is plausible that ongoing activity in the DAn provides a moment-by-moment update of the external environment and internal states and the behavioral status of the fly that appropriately reconfigures the KC>MBOn flow of information. Thus, the DAn/KC/MBOn circuit may form a recurrent network that serves as the insect's brain center for the rapid integration of sensory information and decision-making. Local feedback loops, achieved by reciprocal connectivity like that described in this study, may provide computational benefits to fine tune and optimize the output. Behavioral flexibility may be achieved by passing information through local reentrant loops with constant updating from the external or internal state of the organism (Cervantes-Sandoval, 2017).

A caveat in this and other studies is that it is not possible to exclude that other, non-KC, cholinergic input to DAn contributes to memory acquisition. Massive efforts to generate 'connectomes' in multiple species may offer resolution to this issue at some point in the future. Alternatively, they may continue to reveal additional connections and complexities that defy an immediate understanding. Studies like the present one, that reveal unexpected relationships between synaptic partners in difficult-to-untangle circuits, expose the need to advance beyond 'connectomics' and develop new tools that allow silencing or activation of specific channels and specific synaptic connections between neurons of interest without affecting other functions in the same cells (Cervantes-Sandoval, 2017).

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Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higher-order circuit supporting associative memory has not been previously available. This study reconstructed one such circuit at synaptic resolution, the Drosophila larval mushroom body. Most Kenyon cells were found to integrate random combinations of inputs, but a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. A novel canonical circuit in each mushroom body compartment with previously unidentified connections is reported: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections found between output neurons could enhance the selection of learned behaviours. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory centre (Eichler, 2017).

Massively parallel, higher-order neuronal circuits such as the cerebellum and insect mushroom body (MB) serve to form and retain associations between stimuli and reinforcement in vertebrates and evolutionarily complex invertebrates. Although these systems provide a biological substrate for adaptive behaviour, no complete synapse-resolution wiring diagram of their connectivity has been available to guide analysis and aid understanding. The MB is a higher-order parallel fibre system in many invertebrate brains, including hemimetabolous as well as holometabolous insects and their larval stages. MB function is essential for associative learning in adult insects and in Drosophila larvae from the earliest larval stages onwards. Indeed, the basic organization of the adult and the larval MB and their afferent circuits is very similar; however, larvae have about an order of magnitude fewer neurons. Thus, to systematically investigate the organizational logic of the MB, this study used serial section electron microscopy to map with synaptic resolution the complete MB connectome in a first-instar Drosophila larva. L1 are foraging animals capable of all behaviours previously described in later larval stages including adaptive behaviours dependent on associative learning. Their smaller neurons enable fast electron microscopy imaging of the entire nervous system and reconstruction of complete circuits (Eichler, 2017).

Models of sensory processing in many neural circuits feature neurons that fire in response to combinations of sensory inputs, generating a high-dimensional representation of the sensory environment. The intrinsic MB neurons, the Kenyon cells (KCs), integrate in their dendrites inputs from combinations of projection neurons (PNs) that encode various stimuli, predominantly olfactory in both adult and larva1, but also thermal, gustatory, and visual in adult and larva. Previous analyses in adults and larvae suggest that the connectivity between olfactory PNs and KCs is random, but they do not eliminate the possibility of some degree of bilateral symmetry, which requires access to the full PN-to-KC wiring diagram in both hemispheres (Eichler, 2017).

The MB contains circuitry capable of associating reward or punishment with the representation of the sensory environment formed by KCs. KCs have long parallel axons that first run together, forming the peduncle, and then bifurcate, forming the so-called lobes, in both larvae and adults. KCs receive localized inputs along their axonal fibres from dopaminergic as well as octopaminergic modulatory neurons (DANs and OANs, respectively) that define separate compartments. DANs and OANs have been shown to convey reinforcement signals in adult insects and larval Drosophila. The dendrites of the mushroom body output neurons (MBONs) respect the DAN compartments in adults and larvae. It has been shown in adult Drosophila that co-activation of KCs and DANs can associatively modulate the KC-MBON synapse. Thus, the compartments represent anatomical and functional MB units where sensory input (KCs) is integrated with internal reinforcement signals (DANs/OANs) to modulate instructive output for behavioural control (MBONs). However, the synaptic connectivity of KCs, DAN/OANs, and MBONs at this crucial point of integration was previously unknown (Eichler, 2017).

Furthermore, studies in adult Drosophila have shown that, despite the compartmental organization of the MB, many MBONs interact with MBONs from other compartments, suggesting that the MBON network functions combinatorially during memory formation and retrieval. However, a comprehensive account of all MB neuron connections is lacking. Thus, to provide a basis for understanding how the MB, a prototypical parallel fibre learning and memory circuit, functions as an integrated whole, this study provides a full, synapse-resolution connectome of all MB neurons of an L1 Drosophila larvaxxWe provide the first complete wiring diagram of a parallel fibre circuit for adaptive behavioural control. Such circuits exist in various forms, for example the cerebellum in vertebrates and the MB in insects. They contribute to multiple aspects of behavioural control including stimulus classification, the formation and retrieval of Pavlovian associations, and memory-based action selection. A comprehensive wiring diagram of such a multi-purpose structure is an essential starting point for functionally testing the observed structural connections and for elucidating the circuit implementation of these fundamental brain functions (Eichler, 2017).

Even though individual neurons may change through metamorphosis, many of the basic aspects of the MB architecture are shared between larval and adult Drosophila stages and with other insects. It is therefore expected that the circuit motifs identified in this study are not unique to the L1 developmental stage, but instead represent a general feature of Drosophila and insect MBs (Eichler, 2017).

Electron microscopy reconstruction revealed a canonical circuit in each MB compartment featuring two unexpected motifs in addition to the previously known MBIN-to-KC and KC-to-MBON connections. First, it was surprising to observe that the number of KC-to-MBIN and KC-to-MBON synapses are comparable. As KCs were shown to be cholinergic in adults, KC-to-MBIN connections could be potentially depolarizing. Untrained, novel odours can activate DANs in adult Drosophila and OANs in bees. Similar brief short-latency activations of dopamine neurons by novel stimuli are observed in monkeys, too, and are interpreted as salience signals. Learning could potentially modulate the strength of the KC-to-MBIN connection, either weakening it or strengthening it. The latter scenario could explain the increase in DAN activation by reinforcement-predicting odours observed in adult Drosophila, bees, and monkeys. In addition, dopamine receptors have been shown to be required in Drosophila KCs for memory formation. Another unexpected finding was that MBINs synapse directly onto MBONs, rather than only onto KCs. Such a motif could provide a substrate for neuromodulator-gated Hebbian spike-timing dependent plasticity, which has been observed in the locust MB (Eichler, 2017).

In addition to random and bilaterally asymmetric olfactory and structured non-olfactory PN-to-KC connectivity, the analysis identified single-claw KCs whose number and lack of redundancy are inconsistent with random wiring. Random wiring has previously been shown to increase the dimension of sensory representations when the number of neurons participating in the representation is large compared with the number of afferent fibres, as in the cerebellum or adult MB. However, the current model shows that when the number of neurons is limited, random wiring alone is inferior to a combination of random and structured connectivity that ensures each input is sampled without redundancy. The presence of single-claw KCs may reflect an implementation of such a strategy. In general, the results are consistent with a developmental program that produces complete and high-dimensional KC sensory representations to support stimulus discrimination at both larval and adult stages (Eichler, 2017).

This study reveals the complete MBON-MBON network at synaptic resolution. Previous studies in the larva have shown that odour paired with activation of medial and vertical lobe DANs leads to learned approach8 and avoidance, respectively. This connectivity analysis reveals that glutamatergic MBONs from the medial lobe laterally connect to MBONs of the vertical lobe. The glutamatergic MBON-MBON connections could be inhibitory, although further studies are needed to confirm this. Furthermore, inhibitory GABAergic MBONs from the vertical lobe laterally connect to MBONs of the medial lobe. An example is the feedforward inhibition of medial lobe MBON-i1 output neuron by the vertical lobe GABAergic MBON-g1, -g2 output neurons. A similar motif has been observed in the Drosophila adult, where aversive learning induces depression of conditioned odour responses in the approach-promoting MBON-MVP2 (MBON-11), which in turn disinhibits conditioned odour responses in the avoidance-promoting MBON-M4/M6 (MBON-03) because of the MBON-MVP2 to MBON-M4/M6 feedforward inhibitory connection (Eichler, 2017).

Combining the present connectivity analysis of the MBON-MBON network in the larva and previous studies in the adult Drosophila, the rule seems to be that MBONs encoding opposite learned valance laterally inhibit each other. Such inhibitory interactions have been proposed as a prototypical circuit motif for memory-based action selection (Eichler, 2017).

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Animals exhibit a behavioral response to novel sensory stimuli about which they have no prior knowledge. This study has examined the neural and behavioral correlates of novelty and familiarity in the olfactory system of Drosophila. Novel odors elicit strong activity in output neurons (MBONs) of the α'3 compartment of the mushroom body that is rapidly suppressed upon repeated exposure to the same odor. This transition in neural activity upon familiarization requires odor-evoked activity in the dopaminergic neuron innervating this compartment. Moreover, exposure of a fly to novel odors evokes an alerting response that can also be elicited by optogenetic activation of α'3 MBONs. Silencing these MBONs eliminates the alerting behavior. These data suggest that the α'3 compartment plays a causal role in the behavioral response to novel and familiar stimuli as a consequence of dopamine-mediated plasticity at the Kenyon cell-MBONα'3 synapse (Hattori, 2017).

Novel stimuli can elicit a behavioral response that alerts an organism to unexpected, potentially salient events. An alerting response to sensory stimuli not previously encountered by an animal, such as the orienting response described by Pavlov, provides an organism the opportunity to explore the potential significance of the novel stimulus. A behavioral response to novelty is elicited by sensory cues about which an animal has no prior knowledge. Most behaviors, in contrast, are based upon past experience acquired either over long periods of evolutionary time (innate behaviors) or by learning over the life of an animal. The observation that sensory cues can be identified as novel and can evoke a behavioral response in the absence of prior knowledge poses an interesting problem (Hattori, 2017).

A neural circuit encoding novelty should respond to all novel stimuli, but this response should be suppressed upon familiarization. The memory of a familiar sensory cue should be stimulus specific and long-lasting, distinguishing it from sensory adaptation. Neural responses that correlate with novelty and familiarity are seen in a number of mammalian brain regions. The transition from novelty to familiarity is associated with suppression of neural responses in higher brain centers that appears distinct from intrinsic or sensory adaptation. Electrophysiologic recordings along the visual and auditory pathways reveal that neurons exhibit activity in response to novel or unexpected cues that diminish upon repeated exposure. In the auditory pathway, neurons in the inferior colliculus and the auditory cortex exhibit responses to novel or unexpected tones that attenuate upon repetition. Similarly, neurons in the perirhinal and inferior temporal cortices respond to novel visual stimuli, and this response attenuates rapidly upon repetition, a phenomenon known as repetition suppression (Hattori, 2017).

Dopaminergic neurons in the substantia nigra (SN) pars compacta and ventral tegmental area (VTA) also exhibit phasic bursting activity in response to novel or unexpected sensory events. Unexpected flashes of light or auditory tones evoke burst firing in 60%-70% of the dopaminergic neurons that attenuates as the novel stimulus becomes familiar. Related neural events may underlie attenuation in the BOLD signal observed in extra striate cortex as well as SN and VTA in fMRI studies of humans upon repeated exposure to sensory stimuli. Thus, mammals have evolved neural systems that distinguish novel from familiar sensory stimuli that may facilitate the determination of the potential salience of unfamiliar environmental events (Hattori, 2017).

This study has analyzed the behavioral and neural correlates of novelty and familiarity in the olfactory system of Drosophila. Olfactory perception in the fly is initiated by the binding of an odor to an ensemble of olfactory sensory neurons in the antennae that results in the activation of a unique and topographically fixed combination of glomeruli in the antennal lobe. Each antennal lobe projection neuron extends dendrites into one of the 54 glomeruli and extends axons that bifurcate to innervate two distinct brain regions, the lateral horn, and the mushroom body (MB). The invariant circuitry of the lateral horn is thought to mediate innate behaviors, whereas the unstructured projections to the MB translate olfactory sensory information into learned behavioral responses. In the MB, each odor activates a sparse representation (5%-10%) of principal neurons, the Kenyon cells (KCs). KCs extend axons that form en passant synapses in the compartments of the MB lobes. The KCs synapse on the MB output neurons (MBONs), which have distinct spatially stereotyped dendritic arbors within compartments that collectively tile the lobes. MBONs provide the only output of the MB and the activity of different MBON combinations biases behavior. Each of the 15 compartments is also innervated by the axons of one to three of 20 dopaminergic cell types (dopaminergic neurons, or DANs). Distinct DANs respond to different unconditioned stimuli and dopamine release elicits plasticity in the synapses between the KCs and MBONs. The alignment of DAN arbors with compartmentalized KC-MBON synapses creates a unit for learning that transforms the disordered KC representation into ordered MBON output to collectively bias behavioral responses to sensory stimuli (Hattori, 2017).

The transition from novelty to familiarity involves memory formation, and learning and memory in the fly are accomplished by the circuitry of the MB. This study has identified a neural circuit in the MB that appears to encode a representation of novelty and familiarity. MBONs were observed innervating the α'3 compartment respond to novel odors and that their activity is rapidly suppressed upon repeated exposure to the same stimulus. This suppression upon familiarization is observed for all novel odors tested regardless of innate valence, is stimulus specific, lasts for more than 20 min, and recovers in 1 hr. Repetition suppression of MBON-α'3 is distinct from sensory adaptation and requires odor-evoked activity in the DAN innervating the α'3 compartment. These data suggest that repeated exposure to an odor mediates dopamine-dependent plasticity at the KC synapses onto MBON-α'3 that suppresses MBON output. Moreover, behavioral experiments demonstrate that the α'3 MBONs mediate an alerting response to novel odors. Exposure of a fly to novel olfactory stimuli evokes an alerting behavior. This behavioral response can be elicited by optogenetic activation of α'3 MBONs and eliminated by α'3 MBON silencing. These observations suggest that the behavioral response to novelty and the transition to familiarity is mediated by the circuitry of KCs, DANs, and MBONs within the α'3 compartment (Hattori, 2017).

This study has examined the neural and behavioral correlates of novelty and familiarity in the olfactory system of Drosophila. Exposure of flies to a novel odor interrupts grooming. This alerting response is dependent upon the activity of output neurons of the α'3 compartment of the MB. Optogenetic activation of the α'3 MBONs elicits an alerting response, whereas silencing these neurons eliminates the behavioral response to novel odors. A neural correlate of this behavioral response is observed in the activity of MBON-α'3. Novel odors elicit strong activity in these neurons that is rapidly suppressed upon repeated exposure to the same odor. This transition in MBON-α'3 response upon familiarization requires the activity of PPL1-α'3, the DAN innervating the α'3 compartment, and dopamine receptors in the MBONs. These data suggest that the α'3 compartment may play a causal role in the behavioral response to novel and familiar stimuli as a consequence of dopamine-mediated plasticity at the KC-MBONα'3 synapse. Although the circuitry of the α'3 compartment is central to the behavioral response to novel and familiar odors, the data do not exclude a contribution from other compartments (Hattori, 2017).

Plasticity at the KC-MBON synapses has been invoked to explain olfactory learning and memory (Heisenberg, 2003). In associative learning, exposure to a conditioned stimulus (CS), when paired with an unconditioned stimulus (US), imposes an associative memory upon the CS. The identity of the CS is represented by activity in a specific ensemble of KCs, whereas USs of different valence activate distinct DANs. Dopamine input depresses the KC-MBON synapse in specific compartments to transform the unstructured KC representation of an odor into an ordered MBON representation encoding behavioral bias. The neural mechanism governing the novelty response differs from this classical model of associative learning. In the α'3 compartment, a novel odor elicits strong MBON output and also activates the DAN. Dopamine release from PPL1-α'3 depresses the KC-MBONα'3 synapse, suppressing MBON output on further exposure to this odor. In this manner, a novel odor effectively serves as both a CS and a US to drive the transition from novelty to familiarity (Hattori, 2017).

A second distinction between the novelty response and associative learning emerges from the observation that the response to novelty is suppressed by learning whereas the conditioned response depends on learning. The stereotyped alerting behavior in response to novel odor does not require learning and is therefore innate. In associative learning models, exposure to odor prior to learning activates all MBONs examined, but this combinatorial of MBONs does not elicit a behavior. Rather, behavioral bias is imposed by the suppression of specific MBON output after learning. In the novelty response, innate alerting behavior is elicited by strong output from MBON-α'3 in response to novel odors prior to learning. Learning that accompanies the transition to familiarity suppresses MBON-α'3 activity and the behavioral response to novelty. If, however, the novel odor is accompanied by salient events in the environment, this will result in activation of additional DANs, leading to the formation of associations in other compartments. The alerting response evoked by MBON-α'3 may enhance the awareness of these environmental events. Thus, α'3 output may elicit an immediate and stereotyped response to an odor independent of its salience, which is then assessed by the remaining MB compartments to mediate more measured associative responses (Hattori, 2017).

Odor-evoked dopamine release by PPL1-α'3 appears to be essential to modulate MBON output in the transition from novelty to familiarity. Dopamine release also contributes to decay in the memory of familiar odors. MBON-α'3 activity in response to novel odors is suppressed upon repeated exposure, but activity is restored after 1 hr. Dopamine release in the absence of odor, following repetition suppression, accelerates this recovery process. These observations are consistent with recent experiments demonstrating that dopamine release within a compartment in the absence of odor can lead to synaptic facilitation and the restoration of MBON output. In this manner, familiarity in the fly is a transient phenomenon and the restoration of the perception of novelty may be accelerated by dopamine (Hattori, 2017).

It is suggested that the neural events responsible for the transition from novelty to familiarity involve depression of only those KC-MBON synapses activated by the novel odor. In this manner, novelty and familiarity can be both universal and odor specific. A given odor activates about 5%-10% of the KCs in the MB and by inference 5%-10% of the KC-MBON synapses. An organism is likely to encounter multiple odors that may be construed as novel in the course of hours. If a single novel odor suppresses 5%-10% of the KC-MBON synapses and this synaptic depression is long term, all KC-MBON synapses within the α'3 compartment would be depressed after exposure to roughly 20 novel odors. Depression of all the synapses would prevent subsequent response to novel odors. The data suggest this problem may be obviated in two ways. First, the depression is relatively short lived. Second, although novel odors result in the depression of active KC-MBON synapses, they also enhance the recovery of previously suppressed but currently inactive synapse. This implies that the rate of synaptic recovery is proportional to the rate of exposure to novel odors. Thus, the α'3 compartment has evolved a mechanism to assure that novelty responses can be generated in both dense and sparse odor environments without saturation (Hattori, 2017).

The MB is an associative center in invertebrate brains thought to impose valence on sensory representations. The current data suggest that the MB not only functions in classical learning paradigms, but also supports novelty detection and the transition to familiarity. An organism can have no knowledge of a novel stimulus, and hence it exhibits an indiscriminate alerting response. The MB also integrates information about the organism's internal state (hunger, satiety, sleep, wakefulness, roaming, dwelling) allowing the fly to more comprehensively contextualize the diverse sensory experiences it may encounter throughout its life. Thus, the MB may afford the fly an 'individuality' allowing different flies to respond differently to the same stimuli in accord with its unique history and current state (Hattori, 2017).

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Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In Drosophila, the mushroom body (MB) is the major site of associative learning. This study reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB's alpha lobe, using a dataset of isotropic 8 nm voxels collected by focused ion-beam milling scanning electron microscopy. It was found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple en passant synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only 6% of KC>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). Two unanticipated classes of synapses, KC>DAN and DAN>MBON, were identified. DAN activation produces a slow depolarization of the MBON in these DAN>MBON synapses and can weaken memory recall (Takemura, 2017).

Associative memory helps animals adapt their behaviors to a dynamically changing world. The molecular mechanisms of memory formation are thought to involve persistent changes in the efficiency of synaptic transmission between neurons. In associative learning, persistent changes in synaptic efficacy correlated with memory formation have been found at points of convergence between two neuronal representations: one providing information from sensory inputs about the outside world and a second indicating whether the current environment is punitive or rewarding. Such sites of convergence have been identified for multiple forms of associative learning. However, a comprehensive synaptic level description of connectivity at such a site of convergence is not available for an animal as complex as the fruit fly, Drosophila (Takemura, 2017).

The mushroom body (MB) is the center of associative learning in insects. Sensory information enters the MB via the calyx, where the dendritic claws of Kenyon cells (KCs) receive synaptic inputs from projection neurons of olfactory and other modalities including visual, gustatory and thermal. The parallel axonal fibers of the KCs form the MB-lobes, the output region of the MB. A pattern of sparse activity in the KC population represents the identity of the stimulus. This sparseness is maintained through two mechanisms. First, individual KCs generally only spike when they receive simultaneous inputs from multiple projection neurons. Second, overall KC excitability is regulated by feedback inhibition from a GABAergic neuron, MB-APL, that arborizes throughout the MB. Thus, only a small subset of KCs respond to a given sensory stimulus. Upon this representation of the sensory world, dopaminergic or octopaminergic neurons convey information of punishment or reward and induce memories that associate the sensory stimulus with its valence (Takemura, 2017).

The functional architecture of the MB circuit is best understood in adult Drosophila (see Diagram of the α lobe of the mushroom body). In each MB, the parallel axonal fibers of ~2000 KCs can be divided into 16 compartmental units by the dendrites of 21 types of MB output neurons (MBONs) and the axon terminals of 20 types of dopaminergic neurons (DANs). A large body of behavioral and physiological studies suggests that these anatomical compartments are also parallel units of associative learning. In each compartment, the dendrites of a few MBONs overlap with axon bundles of hundreds of KCs. Punishment and reward activate distinct sets of DANs. DAN input to a compartment has been shown to induce enduring changes in efficacy of KC>MBONs synapses in those specific KCs that were active in that compartment at the time of dopamine release. The valence of the memory appears to be determined by which compartment receives dopamine during training, while the sensory specificity of the memory is determined by which KCs were active during training (Takemura, 2017).

Compartments can have distinct rates of memory acquisition and decay, and the 16 compartments together appear to form a set of parallel memory units whose activities are coordinated through both direct and indirect inter-compartmental connections. The DANs which project to the α1 compartment, the ventral-most compartment of the vertical lobe, play a key role in the formation of appetitive long-term memory of nutritional foods. DANs that project to the other α lobe compartments, α2 and α3, play roles in aversive long-term memory. All three of these compartments receive feedforward inputs from GABAergic and glutamatergic MBONs whose dendrites lie in other MB compartments known to be involved in aversive or appetitive memory. In addition, two types of MB-intrinsic neurons send arbors throughout the MB-lobes: a large GABAergic neuron, MB-APL, which provides negative feedback important for sparse coding, and the MB-DPM neuron, which is involved in memory consolidation and sleep regulation (Takemura, 2017).

Previous EM studies in the MB lobes of cockroaches, locusts, crickets, ants, honey bees and Drosophila identified KCs by their abundance, fasciculating axons and small size. Additionally, large GABA immunoreactive neurons that contact KC axons were identified in the locust pedunculus. While these data provided early insights to guide modeling of the MB circuit, the volumes analyzed were limited and most neuronal processes could not be definitively assigned to specific cell types. This paper reports a dense reconstruction of the three compartments that make up the α lobe of an adult Drosophila male. Because a dense reconstruction was performed, with the goal of determining the morphology and connectivity of all cells in the volume, it can be confidently stated that all cell types with processes in the α lobe have been identified (Takemura, 2017).

Comprehensive knowledge of the connectivity in the α lobe has allowed addressing of several outstanding issues. The first concerns the nature of KC>MBON connectivity. Although each KC passes through all three compartments, it is not known if individual KCs have en passant synapses in each compartment. Thus, it remains an open question whether the sensory representation provided to each compartment and each MBON within a compartment is the same or whether different MBONs within a compartment might sample from non-overlapping sets of KCs, and thus use independent sensory representations for learning. It was also not known which, if any, other cell types are direct postsynaptic targets of KCs (Takemura, 2017).

The second concerns dopamine modulation. What are the locations of dopaminergic synapses and what does this distribution imply about the targets of dopaminergic modulation as well as volume versus local transmission? Cell-type-specific rescue of dopamine receptor mutants suggests that dopamine acts presynaptically in the KCs of KC>MBON synapses. However, postsynaptic mechanisms have also been proposed and a recent study detected expression of dopamine receptors in MBONs, raising the possibility that MBONs might also be direct targets of DAN modulation. Behavioral, imaging and electrophysiological data indicate that dopamine modulation respects the borders between compartments, but it is not known whether these borders have a distinct structure, such as a glial sheet (Takemura, 2017).

The third concerns the two MBON types that send feedforward projections into the α lobe. These MBONs have important roles in associative learning as revealed by behavioral assays and have been postulated to integrate memories of opposing valence and different time scales. However, it is not known which cell types these feedforward MBON projections targets within the MB (Takemura, 2017).

The fourth concerns the two neurons, MB-APL and MB-DPM, which arborize throughout the MB and are thought to regulate MB function globally. What is their local synaptic connectivity within the α lobe and what can this inform about how they perform their roles (Takemura, 2017)?

Finally, the three compartments of the α lobe differ in important aspects, including valence of the memory formed, the time course of memory formation and retrieval, and the numerical complexity of their DAN inputs and MBON outputs. Are there obvious differences in the microcircuits of different compartments (Takemura, 2017)?

In this paper reports the answers to these questions. In addition, the utility of detailed anatomy at the electron microscopic level to provide novel insights is demonstrated: It is shown that nearly all cell types in the α lobe contain more than one morphological class of synaptic vesicle, raising the possibility that these cells utilize multiple neurotransmitters. In addition, two prevalent sets of synaptic motifs - from DANs to MBONs and from KCs to DAN s- are described that were unanticipated despite the extensive anatomical, physiological, behavioral and theoretical studies that have been performed on the insect MB. These novel DAN to MBON connections are characterized using behavioral and physiological assays and find that DAN activation produces a slow depolarization of postsynaptic MBONs and can weaken memory recall (Takemura, 2017).

The connections between the neurons observed in this study are are summarized in a Summary diagram of the connectome reconstruction of the α lobe. In each of the lobe's three compartments, parallel axonal fibers of ~1000 KCs project through the dendrites of a few MBONs and the terminal arbors of a few DANs. The results provide support for several aspects of the generally accepted model for MB circuit function. First, it was found that each KC forms en passant synapses with multiple MBONs down the length of its axon, making it possible for parallel processing across the different compartments of the MB lobes. Secondly, with the assumption that released dopamine diffuses locally, KC>MBON synapses would receive dopaminergic input close to the sites of vesicle release, consistent with the prevailing hypothesis that plasticity occurs at the presynaptic terminals of KCs. However, several circuit motifs were found that were not anticipated by previous work. For example, synaptic connections were found from KCs to DANs, indicating that DANs get axo-axonal inputs within the MB lobes themselves. A recent report provides evidence that these KC>DAN synapses are functional (Cervantes-Sandoval, 2017). An even more unexpected motif was the direct synaptic contacts from DAN to MBON found in every compartment. Functional connectivity experiments confirmed that these connections are monosynaptic, and showed that they give rise to a slow depolarization in the MBON. Moreover, stimulating DANs in freely behaving flies yields effects consistent with a net excitatory DAN>MBON connection. Finally, the synaptic connections are described of two feedforward MBONs, which have been proposed to mediate the interaction of the various parallel memories within the MB lobes, as well as two intrinsic MB neurons, APL and DPM (Takemura, 2017).

This work not only provides definitive evidence for, and quantitative detail about, many previously observed circuit motifs, but also reveals several motifs not anticipated by prior anatomical, behavioral or theoretical studies. These additional circuit motifs provide new insights and raise new questions about the computations carried out by the MB. It is noted that these same novel connections were also found in a parallel study of the larval MB. Not only were the same circuit motifs found in the larval MB and adult α lobe, but also the relative prevalence of these connections was strikingly similar: DAN>MBON synapses were 4.5% the number of KC>MBON synapses in the adult α lobe and 3.4% in the larval MB. KC>DAN synapses were 1.5 times as prevalent as DAN>KC synapses in the adult α lobe, as compared with 1.1 in the larval MB. KCs make 48% of their synapses onto other KCs in the adult α lobe and 45% in the larval upper vertical lobe compartments. It is tempting to speculate that the conservation of the relative abundances of these connections across developmental stages reflects important functional constraints on the circuit (Takemura, 2017).

A large body of work supports the idea that individual KC>MBON synapses are the elemental substrates of associative memory storage in the MB. The dominant hypothesis in the field is that coincidence detection occurs within the presynaptic terminals of the KCs. The Conditioned Stimulus (CS, for example an odor) evokes a spiking response in a sparse subset of KCs, which in turn leads to Ca2+ influx. The Unconditioned Stimulus (US, for example electric shock) activates dopaminergic inputs to the MB lobes, where they likely activate G-protein-coupled dopamine receptors on the KC cell membrane. The coincidence of these two events is thought to be detected by the Ca2+ sensitive, calmodulin-dependent adenylate cyclase rutabaga, which initiates a cAMP signaling cascade that leads to the biochemical changes underlying synaptic plasticity (Takemura, 2017).

The tiling of MBON and DAN projections down the length of the KC axons suggests that each of these compartments serves as an independent module, with the association of reinforcement with sensory input taking place in parallel across several different modules. One important assumption in this model is that each KC sends parallel input to each compartment by making synapses all the way down the length of its axon. Light microscopic imaging established that the axons of individual α/β KCs do indeed run through all three compartments of the α lobe. However, they also revealed that the axonal branching patterns differ between KC classes. For example, the axons of α/βp KCs branch in α2, whereas those of α/βc and α/βs KCs do not, raising the question of how extensive KC outputs are across the different compartments. The dense EM reconstruction established that in fact all α/β KCs form en passant synapses on MBONs in each of the three α lobe compartments (Takemura, 2017).

In many cases, these synapses were found at enlarged boutons that contained the presynaptic machinery. However, output sites were also found on the smooth axons of the α/βc KCs, which lack obvious bouton-like swellings. Only occasional, short (generally <5 μm) segments of KC axons where the axon became thinner than 300 nm in diameter lacked presynaptic sites. Of course, it is not known whether all these synapses are functional. EM analysis showed that within each compartment, every KC passing through a layer of the compartment that was extensively innervated by an MBON made at least one synapse with that MBON. Previous electrophysiological measurements of connectivity in the α2 compartment indicated that only about 30% of KCs connect to MBON-α2sc, suggesting the possibility that the majority of KC>MBON synapses are functionally silent, as they are in cerebellar cortex, where 98% of the parallel fiber-to-Purkinje cell synapses are believed to be silent. However, a more trivial explanation cannot be ruled out: These measurements were made in the presence of cholinergic antagonists that could have partially blocked synaptic events and lead to an underestimate of total connectivity levels (Takemura, 2017).

The EM data revealed that the the number of synapses made by individual KCs was well-described by a Poisson distribution, where each synapse connects with a uniform, independent, and random probability to one of the KCs. Although the predicted distributions strongly depend on the number of connections between two cell types, almost all KC connections to other cells obeyed Poisson statistics. This was true of every KC in the α1 and α3 compartments, where each MBON has compartment-filling dendrites. The α2 compartment is somewhat unusual in that its MBONs innervate only subzones of the compartment. While light microscopy showed that MBON-α2sc primarily innervates the surface and core of the compartment, MBON-α2sp was found to project more to the surface and posterior. The connectome results bore out these observations from the light and electron microscopy, although EM reconstructions also showed that these borders were not sharp, and these MBONs receive less extensive and weaker connections outside these subzones. Nevertheless, within the primary area of innervation, it was again the case that every KC made synapses with all MBONs along its axon. Thus each of the 949 α/β KCs can deliver information to the MBONs in each of the three α lobe compartments (Takemura, 2017).

A strictly feed-forward view of the circuit may miss important processing, however, as earlier studies suggested, and the current results re-emphasize. Firstly, gap junctions between KCs have been reported. This opens up the possibility for lateral propagation of signals across KCs, either biochemical or electrical. For example, in mammalian systems, axo-axonal gap junction coupling can synchronize firing between neurons. Secondly, chemical synapses between KCs have been reported in the MB pedunculus in the locust. The reconstructions show that such KC>KC connections are also present in the lobes, where they are surprisingly prevalent. In fact, the most frequent outputs of the α/βs KCs are other α/βs KCs, assuming the morphologically defined KC>KC connections are functional synapses (Takemura, 2017).

A high percentage (55%) of these putative KC>KC synapses occur in rosette-like structures where multiple KCs also converge on a single dendritic process of an MBON . These are relatively unusual structures, not observed in EM reconstructions of the Drosophila visual system and, indeed, there is no direct evidence that they are functional synapses. At present it is only possible to speculate on their role. As points of heavy convergence, they might allow the effects of synapses from different KCs onto the same dendrite to act synergistically. Activity of a single KC may spread to its neighbors within the rosette, potentially generating a large compound synaptic release event onto the MBON in the middle. Such a signal amplification mechanism may be important to ensure that individual KCs can have a significant impact on MBON membrane potential by recruiting their rosette partners. How the specificity of learning could be maintained in this scenario is, however, unclear. Several basic questions will need to be answered before it is possible to begin to understand the functional significance of these rosettes. For example, can a single KC in the rosette indeed activate its neighbors? And how similar are the response properties of the different KCs that contribute to one rosette (Takemura, 2017)?

In conclusion, the connectivity of the KCs that carry olfactory and other sensory representations supports a model where parallel distributed memory processing occurs in each compartment. However, several circuit motifs that seem designed to spread and possibly amplify signals at the sites of KC output indicate that this circuit is likely more complicated than a simple feed-forward view of the system suggests (Takemura, 2017).

Dopamine-induced plasticity of the KC>MBON synapse is thought to be central to associative learning in this system. The reconstructions showed that dopaminergic neurons make well-defined synaptic contacts within the α lobe, with closely apposed post-synaptic membranes. This contrasts somewhat with dopaminergic innervation in the mammalian system, where there is typically not such close contact with a single clear post-synaptic partner, and volume transmission is the predominant model for dopamine release. It is not known whether the direct and indirect dopaminergic release sites have different functional consequences. Nevertheless, it seems likely that some type of volume transmission happens in the mushroom body. First, ~10 times more KC>MBON synapses than presynaptic sites of dopamine release were found in the α lobe, but previous work showed that learning-induced plasticity depresses MBON responses so strongly that most inputs are likely affected. Second, dopamine would need to diffuse only ~2 μm to reach every KC>MBON synapse within a compartment, but would also be sufficiently short range to prevent significant spill-over of dopamine to neighboring compartments, ensuring that the modularity of plasticity is maintained (Takemura, 2017).

Functional connectivity measurements showed that stimulating the DANs elicits large amplitude calcium signals from MBONs, similar to previous results. Intracellular recordings revealed that this was a surprisingly strong connection, sufficient to elicit spikes in the MBON. The response persisted when both spiking and nicotinic transmission was blocked, to limit the possibility that the DANs act through the KCs, which are cholinergic. Conversely, the response was strongly reduced by adding a dopamine receptor antagonist. Taken together, these results indicate that the response is likely a direct action of dopamine released by the DANs on the MBON, although a more complex mechanism or a role for the transmitter contained in the dense core vesicles observed in the DANs cannot be formally rule out. The depolarization exhibited markedly slow dynamics, peaking >2 s after stimulation offset, and then decaying over tens of seconds. Dopaminergic responses of similar amplitude and time course have been reported in both mammalian systems and in Aplysia, where it is mediated by cAMP-driven changes in a non-selective cation conductance (Takemura, 2017).

It is possible to induce memory formation in this circuit by pairing odor delivery with artificial activation of DANs. Targeting this optogenetic training procedure to DANs that innervate different compartments within the α lobe gives rise to memories with different valence, induction threshold and persistence. In the α1 compartment, a single pairing for 1 min induces an appetitive memory that lasts for 1 day. In contrast, optogenetic training focused on the α3 compartment requires multiple 1 min pairings, repeated at spaced intervals, and induces an aversive memory that lasts for 4 days. Although it seems likely that the different valences reflect the different projection sites of the MBONs for each of these compartments, where the differences in induction threshold and memory persistence might arise is less clear. There is no simple explanation for these differences from the EM-level circuit structure, as the basic wiring motifs were very similar in each compartment. Moreover, any explanation that invokes biochemical differences in KC>MBON synapses would require crisp spatial localization of the signaling pathway machinery that triggers plasticity, as exactly the same KCs participate in memory formation in different compartments. However, the observation that there are DAN>MBON synapses raises the possibility that biochemical differences in the MBONs might contribute to these differences in plasticity induction and maintenance. Indeed, RNAseq data from a set of four different MBONs showed expression of dopamine receptors. An alternative possibility, suggested by the findings in this study, is that the cotransmitter found in the dense core vesicles in the DANs is responsible for these differences. The size of these vesicles differs between DANs innervating the different compartments. Thus, these cells might release distinct co-transmitters, as has been observed in mammalian brain, which could trigger different signaling cascades in either the KCs or the MBONs to differentially modulate the induction and expression of plasticity across compartments (Takemura, 2017).

Models of MB function have generally considered the role for DANs to be confined to relaying signals about punishment or reward to the MB. However, in the mammalian brain, DANs can dynamically change their responses to both US and CS. In this study, it was found that the axonal terminals of the DANs receive many inputs from KCs within the lobes. In other words, both MBONs, DANs and even KCs receive extensive synaptic input from KCs in each compartment. If the current model that plasticity is pre-synaptic proves to be correct, this suggests that the responses of the DANs themselves would be subject to plasticity. If the synaptic depression observed at KC>MBON synapses also acts at KC>DAN connections, odor-evoked DAN responses would be diminished as a result of learning. This would serve as a negative feedback loop, reducing the strength of plasticity on successive training cycles with the same odor. Indeed, a gradually plateauing of the learning curve is a common feature of memory formation in different systems, including olfactory conditioning in Drosophila (Takemura, 2017).

One of the more surprising findings of this study was the observation that there are many direct DAN>MBON synaptic connections. Moreover, the functional connectivity measures indicate that these were relatively strong excitatory inputs. The excitatory sign of the DAN>MBON connection is also consistent with the behavioral effects of DAN activation that was observed. What role these DAN>MBON connections play in overall circuit function is an important question for future work. There are two general possibilities that are felt to be interesting to consider. Dopaminergic modulation has been proposed to play a general role in routing of information through the MB to different downstream neurons. Although changes in KC>MBON strength contribute to this process, the current results suggest that such state changes could also potentially be conveyed to the MBONs directly from the DANs. State-dependent changes in DAN activity have indeed been observed with calcium imaging. The slow synaptic dynamics observed in the DAN>MBON connection in MBON-α1 suggest the possibility that small changes in DAN firing might be capable of producing sustained changes in MBON membrane potential reflecting the current internal state of the animal (Takemura, 2017).

A second possibility, suggested from the framework of reinforcement learning established in vertebrates, is related to motivation and the comparison of expected versus actual reward. In Drosophila, prior work on odor-sugar conditioning in larvae provided evidence that flies form a comparison between the current state of reward and the reward expected from the conditioned cue. This work showed that animals behaviorally express memories only when the expected reward intensity is higher than the currently available reward. This is similar to the results presented in this study; just as the presence of reward diminished memory expression in the larvae, stimulating the DANs suppressed performance of animals trained by the optogenetic conditioning. The need to compare current and expected reward could potentially explain why there is an opponent relationship between the depression of KC>MBON synapses that drives associative learning and the excitatory effects of the DAN>MBON connection. If depression dominates, the association drives behavior, but this can be overridden by sufficient levels of DAN activity. In this respect, it is noteworthy that DANs appear to be able to act directly on the MBON, without participation of the KCs. Overall, this comparison could ensure that learned behavior is motivated not strictly by the expectation of reward, but rather the expected increase in reward, assessed at the moment of testing (Takemura, 2017).

The organization of the MB into a set of compartments arranged in series along the KC axons is well suited for simultaneously storing multiple independent memories of a given sensory stimulus. However, there must be some means by which these modules interact with one another to ensure coordinated, coherent expression of memory. Feedforward connections that link different compartments, first discovered by light microscopic anatomy, have recently been shown to be important for mediating such interactions. In particular, MBON-γ1pedc>α/β is an inhibitory neuron that connects aversive and appetitive learning compartments; it ensures that the circuit can readily toggle between different behavioral outputs (Takemura, 2017).

The EM reconstructions included both MBON-γ1pedc>α/β and MBON-β1>α, two feedforward neurons which project from their respective compartments to widely innervate other parts of the MB. Memories stored in the α lobe compartments are long-term and relatively inflexible, whereas the short-term memories formed in β1 and γ1pedc are readily updated by recent experiences. The feedforward connections are thought to enable the short-term memories in β1 and γ1pedc to temporarily mask expression of the stable memories stored in the α lobe. Indeed training an animal with either a multi-component aversive/appetitive food stimulus, or by simultaneous optogenetic activation of a composite set of DANs covering both appetitive and aversive compartments results in a compound memory that is initially aversive and later transitions to appetitive. The connectome results show that the primary synaptic targets of these feedforward neurons are the MBONs in the downstream compartment. By contrast, relatively few connections onto KCs were observed. Overall, this suggests that the feedforward connections can strongly influence the output from a compartment, but likely have little impact on the sensory information delivered to each compartment from the KCs. This is consistent with observations that MBON-γ1pedc>α/β strongly modulates activity of glutamatergic neurons at the tip of the horizontal lobe, but not their dendritic responses. Targeting these feedforward connections to the MBON may ensure that conflicting memories can form simultaneously in response to a complex sensory input, but with the behavioral manifestation of those memories capable of undergoing a crisp switch (Takemura, 2017).

This study has provided synapse level anatomical information on neuronal circuits involved in learning and memory in Drosophila. The comprehensive nature of this dataset should enable modeling studies not previously possible and suggests many experiments to explore the physiological and behavioral significance of the circuit motifs that were observed. That many of these motifs were not anticipated by over 30 years of extensive anatomical, experimental and theoretical studies on the role of the insect MB argues strongly for the value of electron microscopic connectomic studies (Takemura, 2017).

A dense (complete) reconstruction of neurons and synapses is resource intensive, so it is reasonable to ask if tracing a subset of cells or synapses could have yielded similar results with less effort. This is hard to answer in general, since there are many sparse tracing strategies, and each can be pursued to differing degrees of completeness. It is likely that most sparse tracing strategies would have discovered the new pathways reported in this, as the connections are numerous and connect well known cell types. Conversely, the conclusions that all cell types in this circuit had been identified would have been more difficult to make with confidence and a rare cell type, such as the SIFamide neuron, might have been missed. Perhaps, most importantly, statistical arguments, particularly those that require an accurate assessment of which cells are not connected, such as the absence of network structures such as rings or chains, would have been hard to make from sparse tracing. More generally, the model independent nature of dense tracing helps to discover any 'unknown unknowns', provides the strongest constraints on how neural circuits are constructed, and allows retrospective analysis of network properties not targeted during reconstruction (Takemura, 2017).

Dopaminergic neurons (DANs) signal punishment and reward during associative learning. In mammals, DANs show associative plasticity that correlates with the discrepancy between predicted and actual reinforcement (prediction error) during classical conditioning. Also in insects, such as Drosophila, DANs show associative plasticity that is, however, less understood. This study examined ssociative plasticity in DANs and their synaptic partners, the Kenyon cells (KCs) in the mushroom bodies (MBs), while training Drosophila to associate an odorant with a temporally separated electric shock (trace conditioning). In most MB compartments DANs strengthened their responses to the conditioned odorant relative to untrained animals. This response plasticity preserved the initial degree of similarity between the odorant- and the shock-induced spatial response patterns, which decreased in untrained animals. Contrary to DANs, KCs (α'/β'-type) decreased their responses to the conditioned odorant relative to untrained animals. No evidence was found for prediction error coding by DANs during conditioning. Rather, the data supports the hypothesis that DAN plasticity encodes conditioning-induced changes in the odorant's predictive power (Dylla, 2017).

Associative learning enables animals to anticipate negative or positive events. The neural mechanisms of associative learning are commonly studied in classical conditioning paradigms, in which animals are trained to associate a cue (conditioned stimulus; CS) with a punishment or reward (unconditioned stimulus; US). In the standard conditioning paradigm CS and US overlap in time, while in the trace conditioning paradigm there is a temporal gap between the CS and US. During both standard conditioning and trace conditioning, the US is mediated by dopaminergic neurons (DANs), in animals as diverse as monkeys and fruit flies (Dylla, 2017).

Genetic tools for monitoring and manipulating neuronal activity in the fruit fly Drosophila melanogaster promoted the understanding of the neural mechanisms of dopamine-mediated learning. Those mechanisms are well-described for standard 'odor-shock conditioning' in Drosophila, in which an olfactory CS is paired with a temporally overlapping electric shock US. During conditioning, an odor-shock association is formed in the mushroom body (MB) neuropil. The intrinsic neurons of the MB, the Kenyon cells (KCs), receive olfactory input in the MB-calyx and project to the vertical (α and α'), and the medial (β, β', and γ) MB-lobes. During odor-shock conditioning, the olfactory CS activates an odorant-specific KC population, and the electric shock US activates DANs that innervate the MB-lobes. In KCs, the CS-induced increase in intracellular calcium and the US-(dopamine)-induced second messengers synergistically activate an adenylyl cyclase, which alters the synaptic strength between KCs and MB output neurons (MBONs). This change in KC-to-MBON synapses is thought to encode the associative odor memory (Dylla, 2017).

The MB-lobes are divided into 15 compartments (α1-3, β1-2, α'1-3, β'1-2, and γ1-5), each of which is innervated by a distinct population of DANs and MBONs. These compartments constitute functional units, which are involved in different forms of associative learning. In compartments such as γ1, γ2, and β2, DANs mediate electric shock reinforcement. Besides mediating reinforcement during classical conditioning, Drosophila DANs are involved in long-term memory formation, forgetting, extinction learning and memory reconsolidation, and in integrating internal states with memory and sensory processing. A single DAN can even serve different functions, for example, PPL1-γ1pedc (also referred to as MB-MP1) signals reinforcement, and controls state-dependent memory retrieval (Dylla, 2017).

The functional complexity of Drosophila DANs is further increased by the fact that DANs show learning-induced associative plasticity: they increase their response to the CS during classical conditioning. Mammalian DANs also increase their CS-induced responses during classical conditioning. In addition, they decrease their response to the US, and when a predicted US does not occur, they decrease their activity below baseline level. This pattern of response plasticity in mammalian DANs is compatible with the hypothesis that animals only learn to associate a CS with a US, when the US occurs unpredictably. Thus, mammalian DANs appear to encode this prediction error. In Drosophila, however, DANs do not change their response to the US. Therefore, Drosophila DANs appear to encode the US prediction by the CS rather than encoding the US prediction error during classical conditioning. It is not clear, whether classical conditioning in insects is driven by US prediction error. There is evidence for prediction error-driven conditioning in crickets, but there is also a controversy about whether or not blocking (a failure to learn, when the US is already predicted by another CS) occurs (Dylla, 2017).

This study reassessed the hypothesis that Drosophila DANs do not encode the prediction error during classical conditioning. Different from Riemensperger (2005) who pooled DAN activity across the mushroom body lobes, this study differentiated between DAN types that innervate different compartments of the MB lobes. Moreover, instead of using standard conditioning, trace conditioning with a 5 s gap between the CS and the US was used, allowing for precise distinguishing between responses to either the CS or the US (Dylla, 2017).

This study investigated associative plasticity in the responses of DANs and their synaptic partners, the KCs, across the compartments of the Drosophila MB. Using calcium imaging, CS- and US-induced responses of a subpopulation of DANs (labeled by TH-GAL4) and of KCs (labeled by OK107-GAL4) were recorded during odor-shock trace conditioning. Note, that most compartments are innervated by multiple TH-GAL4-labeled DANs. Therefore, the average activity that was recorded in most of the compartments might mask possible differences in the response properties and plasticity between individual DANs and KCs. Only DAN responses in the compartments γ2 and α'1 reflect the responses of a single neuron (Dylla, 2017).

Across MB compartments, DANs and KCs differed in their response strength to odorants and electric shock, and they differed in CS-US pairing-induced plasticity. Compared to the unpaired control groups, KCs decreased their responses to the CS in all compartments of the β'-lobe and in the junction, while DANs increased their responses to the CS in all compartments of the γ- and β'-lobe, and in the junction. The occurrence of associative plasticity in DANs in the compartments γ3-5 and β'1 is surprising, given that these DANs are not known to be involved in odor-shock conditioning, after training there was neither an associative change in US-induced DAN responses nor a change of activity during US-omission after CS presentation. It is therefore concluded that Drosophila DANs do not encode the US-prediction error during classical conditioning (Dylla, 2017).

Previous studies suggested that DANs in the MB lobes respond strongly to electric shock and weakly to odorants. The compartment-resolved analysis of the calcium imaging data refines this picture: It is confirmed that DANs of all imaged compartments respond to both electric shock and odorants, and it was shown that their relative response strength to odorants and electric shock differs across compartments. For example, DANs innervating γ1 responded stronger to electric shock than to odorants, while DANs innervating β'2 responded equally strong to odorants and electric shock. The strongest DAN responses to electric shock were shown to be in the compartments γ1 and γ2. These compartments receive input from PPL1-γ1pedc and PPL1-γ2α'1 DANs that mediate electric shock reinforcement. In all compartments, except in α1/α'1, the DAN response strength correlated positively with the current strength encountered by individual flies. Thus, DANs are capable of encoding the strength of the electric shock US, and this property may account for the positive dependence between electric shock strength and learning performance in flies (Dylla, 2017).

Calcium responses in KCs differ between MB lobes, and they differ between the compartments of a given lobe, possibly due to compartment-specific modulation by DANs and MBONs. KCs in γ2 and γ3 responded strongest to odorants, confirming the results of Cohn (2015). KCs generally responded only weakly to electric shocks. Previously published strong KC responses to electric shock may be because electric shocks were applied to the flies' abdomen rather than to their legs, which might have resulted in a stronger stimulation (Dylla, 2017).

The associative strengthening of DAN responses to the olfactory CS (as compared to the unpaired control group), confirms the previous report by Riemensperger (2005). Associative plasticity occurred in those DANs that innervate the MBs (PPL1 and PAM cluster DANs; note that the used TH-GAL4 driver line covers only a small subpopulation of PAM neurons but not in DANs that innervate the central complex (PPL1 and PPM3 cluster DANs). This is in line with the established role of MB innervating-DANs in associative memory formation, while central complex-innervating DANs are involved in behaviors such as locomotion, wakefulness, arousal, and aggression, and are therefore not expected to show odor-shock conditioning-induced plasticity (Dylla, 2017).

In contrast to previous studies, this study did not find an associative increase in KC calcium responses to the CS in the MB-lobes after odor-shock conditioning. This may indicate either a difference between trace conditioning and standard conditioning, or a difference in other experimental parameters that may also account for inconsistencies in the published effects of odor-shock conditioning (Dylla, 2017).

The associative decrease in KC responses in the β'-lobe compartments is in line with previous studies that showed conditioning-induced depression of KC-to-MBON synapses (Cohn, 2015; Hige, 2015). Therefore, it propose that the associative decrease in KC responses to the CS reflects a presynaptic depression at KC-to-MBON synapses in β'-lobe compartments (Dylla, 2017).

What is the site of neuronal plasticity that underlies the relative increase in DANs' responses to the olfactory CS? Riemensperger (2005) proposed that DANs get odorant-driven excitatory input via a MBON feedback loop that is strengthened during odor-shock conditioning. However, the DAN population is composed of different neuron types that do not share a common input either from MBONs or from other neurons that could explain the global associative plasticity across MB compartments. Because KCs presumably provide the only common odor-driven input to all MB-innervating DANs, it is suggested that the site of associative plasticity is located in a KC-to-DAN synapse. Indeed, KC-to-DAN synapses have recently been reported in Drosophila (Cervantes-Sandoval, 2017). Associative increase in CS-induced DAN responses occurred despite unaltered or decreased KC responses in the same compartment. This suggests that the associative plasticity occurs post-synaptic in DANs and is not inherited from KCs (Dylla, 2017).

What is the neuronal substrate of CS-US coincidence detection in DANs and KCs? Drosophila trace conditioning depends on dopamine receptor-triggered signaling in KCs, as is the case for standard conditioning. However, the CS-US coincidence detection mechanism in trace conditioning is unknown. In standard conditioning the CS-induced increase in KCs' calcium concentration coincides with the US-(dopamine)-induced second messengers, which is thought to synergistically activate the rutabaga adenylyl cyclase, and ultimately alters the strength of KC-to-MBON synapses. This mechanism would not work for trace conditioning, because (1) at the time the US occurs, CS-induced increase in KCs' calcium concentration is back to baseline levels, and (2) trace conditioning does not involve the rutabaga adenylyl cyclase. It is therefore hypothesized that a non-rutabaga adenylyl cyclase or a protein kinase C could serve as a molecular coincidence detector for the CS trace and the US. For example, the CS-induced calcium and dopamine signaling could lead to a sustained activation of an adenylyl cyclase or protein kinase C in KCs, which then would increase synergistically and drive synaptic plasticity during the US-induced dopamine signaling (Dylla, 2017).

DAN responses to odorants and associative strengthening of DAN responses to the CS-odorant are not included in current models of associative learning in the MB. However, associative plasticity is a common feature of US-mediating neurons, which occurs in mammalian and Drosophila DANs, and in an octopaminergic neuron in honey bees (Dylla, 2017).

What could be the function of odorant-induced responses and odor-shock conditioning-induced plasticity in DANs? MB-innervating DANs strengthened their response to the CS (as compared to the unpaired group) during odor-shock conditioning, in line with Riemensperger (2005). However, other than in monkey DANs, this study did not observe associative plasticity in DANs' response to the US. The data therefore support the idea that Drosophila DANs encode predictive power of the CS, e.g., US-prediction, but not the US-prediction error during classical conditioning (Dylla, 2017).

This study found shock-induced responses and associative plasticity in DANs that are not involved in odor-shock conditioning, for example in DANs innervating β'1, γ3, γ4, and γ5. This suggests that those DANs serve a function in aversive odor learning which is not captured by the commonly applied conditioning paradigms. For example, the relative strengthening of CS-induced responses could mediate reinforcement during second-order conditioning, in which a previously reinforced CS1 can act as US in subsequent conditioning of a second CS2. As Drosophila is capable of second-order learning, this theory can be tested in behavioral experiments: if associative strengthening of DAN responses to the CS underlies CS1-induced reinforcement in second-order conditioning, then preventing associative plasticity in DANs, or blocking their output during CS2-CS1 pairing should abolish second-order conditioning (Dylla, 2017).

The occurrence of CS-induced responses and associative plasticity in most of the MB-innervating DANs suggests that the separation between the CS- and US-pathway and between different US-pathways is less strict than suggested in current models of associative learning in the MB. Associative plasticity in the spatial pattern of CS-induced DAN responses makes them a potential neuronal substrate for encoding the US identity in CS-US memories and the predictive power of a CS (Dylla, 2017).

These data revealed similar response properties and plasticity rules across Drosophila DANs in the γ- and β'-lobe. This contrasts with their anatomical and functional heterogeneity, which indicates yet undiscovered mechanisms and functions of DAN plasticity. Note, that this study could not test whether the flies learned in the imaging setup, as currently no behavioral readout exists for odor-shock conditioning during physiological experiments. Nevertheless, since a conditioning protocol and stimulus application comparable to an established behavioral paradigm was used, it is believed that the associative plasticity in neuronal responses that was found underlies behavioral associative plasticity. Therewith the data lay the foundations for causal studies on the function of associative plasticity in DANs (Dylla, 2017).

In Drosophila, negatively reinforcing dopaminergic neurons also provide the inhibitory control of satiety over appetitive memory expression. This study shows that aversive learning causes a persistent depression of the conditioned odor drive to two downstream feed-forward inhibitory GABAergic interneurons of the mushroom body, called MVP2, or mushroom body output neuron (MBON)-γ1pedc>α/β. However, MVP2 neuron output is only essential for expression of short-term aversive memory. Stimulating MVP2 neurons preferentially inhibits the odor-evoked activity of avoidance-directing MBONs and odor-driven avoidance behavior, whereas their inhibition enhances odor avoidance. In contrast, odor-evoked activity of MVP2 neurons is elevated in hungry flies, and their feed-forward inhibition is required for expression of appetitive memory at all times. Moreover, imposing MVP2 activity promotes inappropriate appetitive memory expression in food-satiated flies. Aversive learning and appetitive motivation therefore toggle alternate modes of a common feed-forward inhibitory MVP2 pathway to promote conditioned odor avoidance or approach (Perisse, 2016).

Prior work in Drosophila indicated that negative reinforcement and hunger-state-dependent motivational control of appetitive memory performance might be controlled by the same dopaminergic neurons (DANs). The presynaptic field of the MP1/PPL1-γ1pedc DANs occupies a defined region of the MB that also contains the MVP2/MBON-γ1pedc >αβ dendrites, suggesting that these DANs modulate the efficacy of this specific KC-MBON connection. The results of the current study demonstrate that the MVP2 MBONs also play a critical role in the expression of short-term aversive memory and the state-dependence of appetitive memory expression. Since these findings directly mirror the described roles for the MP1 DANs (Krashes, 2009), it is concluded that DAN modulation of the KC-MVP2 junction is critical for both negative reinforcement during olfactory learning and the motivational salience of appetitive odor cues (Perisse, 2016).

The GABA-ergic MVP2 neurons have postsynaptic and presynaptic processes in the MB, suggesting that they are interneurons of the MB and feed-forward inhibit other MBON compartments. Dendrites of MVP2 neurons (and the presynaptic terminals of the MP1 DANs) innervate the γ1 region and more densely innervate the αβs than the αβ core (αβc) region of the αβ ensemble (Krashes, 2009). MVP2 are therefore likely to be primarily driven by αβs KCs. Since αβs neurons contribute to conditioned approach and avoidance, whereas αβc are particularly important for conditioned approach (Perisse, 2013), there is an imbalance in the drive to approach and avoidance behaviors at this level of the MBON network (Perisse, 2016).

Artificial activation of MVP2 neurons in naive flies drives approach behavior, consistent with them preferentially inhibiting MBON compartments that direct avoidance -- as opposed to those that drive approach. Anatomical and functional connectivity and odor-directed behavioral data are consistent with such a model. MVP2 stimulation inhibits odor-evoked activity in M4/6 but not in V2αV2α' MBONs. MVP2 stimulation also promotes expression of approach memory in food-satiated flies, yet it inhibits naive odor avoidance behavior. It is concluded that MVP2 directly inhibit the M4/6 class of horizontal lobe MBONs through synapses made on the primary axonal segment as it exits the MB lobes. Inhibition exerted in this area might be expected to control the gain of the MBON responses following integration of KC inputs in the MBON dendrite in a manner similar to perisomatic inhibition in mammals. Consistent with this anatomy and idea, no obvious changes were found in the odor drive to the dendritic region of M4/6 neurons between hungry and satiated flies, but a hunger-dependent decrease was apparent when odor-evoked responses were measured in the efferent neurites. In contrast, MVP2 neurons do not functionally inhibit or densely innervate the neurites of V2αV2α' MBONs, nor does hunger reduce odor-evoked responses in V2αV2α' MBONs. It therefore seems likely that MVP2 neurons contact DANs or other neurons that occupy the α2 compartment of the MB lobe (Perisse, 2016).

The data also demonstrate that aversive learning reduces the relative conditioned odor drive to MVP2 neurons, which would presumably decrease feed-forward inhibition onto the relevant MBON compartments and thereby render them more responsive to odors. Output from the glutamatergic M4/6 neurons, which are postsynaptic to the KCs in the horizontal tip regions, is required for expression of aversive and appetitive memory. Furthermore, the relative odor-drive to M4/6 neurons was shown to be depressed by reward learning and potentiated by aversive learning. Since aversive learning reduces the conditioned odor drive of the MVP2 neuron, it is proposed that the observed increase in odor-drive to M4/6 after aversive learning results from reduced feed-forward inhibition from MVP2. This would mean that bi-directional output plasticity could emerge via a direct junctional plasticity following reward conditioning, but a network property of reduced MVP2 feed-forward inhibition after aversive conditioning. Such a layered feed-forward network architecture linking one site of DAN-driven KC-MBON plasticity to another KC-MBON connection would provide a means to achieve odor-specific bi-directional plasticity at a particular synaptic junction using dopamine-driven synaptic depression in two different places. It is proposed that this circuit design principle in which plasticity at one site of a neuron can, via feed-forward inhibition, indirectly alter the efficacy of output elsewhere in the same neuron, could be a general feature in the brain of the fly and other animals. It is possible that the KC-MVP2 junction also exhibits bi-directional plasticity, notably with inverted polarity relative to M4/6 plasticity traces (Perisse, 2016).

The layered network architecture places the aversive memory relevant MVP2 plasticity on top of the M4/6 plasticity that is relevant for appetitive memory (Owald, 2015). This organization could accommodate the co-existence of aversive and appetitive olfactory memories following conditioning reinforced by sugar laced with bitter taste. Immediately after such training flies avoid the conditioned odor because the aversive taste memory relieves feed-forward inhibition onto the sites that are depressed by appetitive sugar plasticity and therefore over-rides the expression of approach memory. However, as the aversive memory decays, feed-forward inhibition returns and appetitive memory is then expressed. A similar mechanism might account for the time-dependent switch from conditioned aversion to approach following odor conditioning reinforced by alcohol (Kau, 2011). It is notable that learning-induced plasticity of relative odor-drive to MVP2 persists for at least 3 hr after training whereas output from MVP2 is dispensable for the expression of aversive memory at that time. Since expression of different phases of aversive memory requires distinct combinations of MBON pathways (Bouzaiane, 2015), it is proposed that more persistent MVP2 plasticity might provide a permissive gate for both the formation of aversive memory in, and the expression from, other parts of the MBON network. This would be reminiscent of fear conditioning in the neural circuitry of the mouse amygdala, where dopamine suppresses feed-forward GABA-ergic inhibition from local interneurons to facilitate the induction of long-term potentiation (Perisse, 2016).

MVP2 neuron output is required for the expression of sugar-reinforced approach memory at all times. Moreover, odors evoked larger MVP2 responses in hungry than in food-satiated flies, and elevating MVP2 activity in satiated flies promoted inappropriate expression of appetitive memory. These results are consistent with the model that hunger generally increases feed-forward inhibition through MVP2 to support appetitive memory expression). This result is also the mirror-image of that with MP1 DANs whose activity increases when the flies are satiated and whose inhibition leads to the expression of appetitive memory in satiated flies. Taken with prior work, it is therefore proposed that hunger increases dNPF, which releases MP1 inhibition over the KC-MVP2 connection. This results in an increase of odor-evoked MVP2 feed-forward inhibition onto the MBON compartments such as M4/6 that contain the KC-MBON synapses that are directly modified by appetitive conditioning. The increase of MVP2 inhibition into these, and other, compartments allows more efficient expression of the appetitive memory-directed approach behavior by effectively raising the motivational salience of learned food-related odors. Appetitive conditioning may also increase odor-specific recruitment of MVP2 feed-forward inhibition (Perisse, 2016).

The current findings therefore suggest that the MVP2 neuron pathway functions in at least three modes that are presumably selected by the aversively reinforcing MP1 DANs. If the flies are aversively conditioned, phasic MP1 specifically depresses conditioned-odor drive to MVP2 neurons. In a food-satiated fly, tonic MP1 limits general odor-driven MVP2 activity. Lastly, in the hungry fly, lower MP1 activity generally enhances odor-drive to MVP2. In the OFF modes, low-level MVP2 feed-forward inhibition skews the MBON network toward behavioral avoidance, whereas in the ON mode the increased feed-forward inhibition from MVP2 skews the MBON network toward favoring conditioned approach. The MP1 DANs signal the aversive reinforcing properties of electric shock, heat, and bitter taste, suggesting they provide general aversive influence. The satiated state presumably uses a tonic version of this aversive signal to limit the fly approaching an appetitive odor cue (Perisse, 2016).

The parallels between the fly and mammalian dopaminergic systems appear striking. DANs in the basal ganglia of the mammalian brain also support reinforcement learning and the prediction of stimuli that potentially lead to rewarding outcomes. Furthermore, like the fly DANs, mammalian DANs can be anatomically divided into those that generate aversion and different types of reward. GABA-ergic neurons in the mouse ventral tegmental area, whose cell bodies are interspersed with the DANs, have been proposed to signal the value of expected reward and provide a source of subtraction to DANs that calculate a reward prediction error. Negatively reinforcing MP1 DANs in the fly modulate odor-drive to the MVP2 neurons to provide the motivational control over actions to gain reward. Therefore, MVP2 neurons may provide an inhibitory bridge between MBON domains that are controlled by aversive and rewarding DANs (Perisse, 2016).

Massive activation of dopamine neurons is critical for natural reward and drug abuse. In contrast, the significance of their spontaneous activity remains elusive. In Drosophila melanogaster, depolarization of the protocerebral anterior medial (PAM) cluster dopamine neurons en masse signals reward to the mushroom body (MB) and drives appetitive memory. Focusing on the functional heterogeneity of PAM cluster neurons, a single class of PAM neurons, PAM-γ3, mediates sugar reward by suppressing their own activity. PAM-γ3 is selectively required for appetitive olfactory learning, while activation of these neurons in turn induces aversive memory. Ongoing activity of PAM-γ3 gets suppressed upon sugar ingestion. Strikingly, transient inactivation of basal PAM-γ3 activity can substitute for reward and induces appetitive memory. Furthermore, the satiety-signaling neuropeptide Allatostatin A (AstA) was identified as a key mediator that conveys inhibitory input onto PAM-γ3. These results suggest the significance of basal dopamine release in reward signaling and reveal a circuit mechanism for negative regulation (Yamagata, 2016).

Sugar ingestion triggers multiple reward signals in the fly brain. This study has provided lines of evidence that part of the reward is signaled by inactivating dopamine neurons. The role of PAM-γ3 highlights the striking functional heterogeneity of PAM cluster dopamine neurons. The decrease and increase of dopamine can convey reward to the adjacent compartments of the same MB lobe-γ3 and γ4-. The reward signal by the transient decrease of dopamine is in stark contrast to the widely acknowledged role of dopamine. Midbrain dopamine neurons in mammals were shown to be suppressed upon the presentation of aversive stimuli or the omission of an expected reward, implying valence coding by the bidirectional activity. As depolarization of PAM-γ3 can signal aversive reinforcement, these neurons convey the opposite modulatory signals to the specific MB domain by the sign of their activity. Intriguingly, the presentation and cessation of electric shock act as punishment and reward, respectively. Such bidirectional activity of PAM-γ3 may represent the presentation and omission of reward. (Yamagata, 2016).

While thermoactivation of PAM-γ3 induced robust aversive memory, blocking their synaptic transmission did not affect shock learning, leaving a question regarding their role in endogenous aversive memory process. PAM-γ3 may only be involved in processing aversive reinforcement different from electric shock-like heat. However, two studies show that dopamine neurons mediating aversive reinforcement of high temperature and bitter N,N-Diethyl-3-methylbenzamide (DEET) are part of those for electric shock. Identification of such aversive stimuli that are signaled by PAM-γ3 activation is certainly interesting, as it is perceived as the opposite of sugar reward and thus provides the whole picture of the valence spectrum. Another scenario where sufficiency and necessity do not match is the compensation of the reinforcing effect by other dopamine cell types (e.g. MB-M3). The lack of PAM-γ3 requirements for electric shock memory may be explained by a similar mechanism. (Yamagata, 2016).

How can the suppression of PAM-γ3 modulate the downstream cell and drive appetitive memory? Optogenetic activation of the MB output neurons from the γ3 compartment induces approach behavior. This suggests that the suppression of the PAM-γ3 neurons upon reward leads to local potentiation of Kenyon cell output. This model is supported by recent studies showing the depression of MB output synapses during associative learning. A likely molecular mechanism is the de-repression of inhibitory D2-like dopamine receptors, DD2R. As D2R signaling is a widely conserved mechanism, it may be one of the most ancestral modes of neuromodulation. (Yamagata, 2016).

Furthermore, recent anatomical and physiological studies demonstrated that different MB-projecting dopamine neurons are connected to each other and act in coordination to respond to sugar or shock. Therefore, memories induced by activation or inhibition of PAM-γ3 may well involve the activity of other dopamine cell types (Yamagata, 2016).

The finding that appetitive reinforcement is encoded by both activation and suppression of dopamine neurons raises the question as to the complexity of reward processing circuits (see Reward signals by excitation and inhibition of dopamine neurons). It is, however, reasonable to implement a component like PAM-γ3 as a target of the satiety-signaling inhibitory neuropeptide AstA. Intriguingly, the visualization of AstA receptor distribution by DAR-1-GAL4 revealed expression in two types of MB-projecting dopamine neurons: PAM-γ3 and MB-MV1 (also named as PPL1- γ2α'1). Given the roles of MB-MV1 in aversive reinforcement and locomotion arrest, AstA/DAR-1 signaling may also inhibit a punishment pathway upon feeding. It is thus speculated that this complex dopamine reward circuit may be configured to make use of bidirectional appetitive signals in the brain (Yamagata, 2016).

Dopaminergic neurons provide reward learning signals in mammals and insects. Recent work in Drosophila has demonstrated that water-reinforcing dopaminergic neurons are different to those for nutritious sugars. This study tested whether the sweet taste and nutrient properties of sugar reinforcement further subdivide the fly reward system. They found that dopaminergic neurons expressing the OAMB octopamine receptor specifically conveyed the short-term reinforcing effects of sweet taste. These dopaminergic neurons projected to the β'2 and γ4 regions of the mushroom body lobes. In contrast, nutrient-dependent long-term memory required different dopaminergic neurons that project to the γ5b regions, and it could be artificially reinforced by those projecting to the β lobe and adjacent α1 region. Surprisingly, whereas artificial implantation and expression of short-term memory occurred in satiated flies, formation and expression of artificial long-term memory required flies to be hungry. These studies suggest that short-term and long-term sugar memories have different physiological constraints. They also demonstrate further functional heterogeneity within the rewarding dopaminergic neuron population (Huetteroth, 2015).

These results demonstrate that the sweet taste and nutrient properties of sugars are independently processed and reinforce memories of different duration. Sweet taste is transduced through octopaminergic neurons whose released octopamine, via the OAMB receptor, activates dopaminergic neurons that project to the β'2am and γ4 regions of the mushroom body. Octopaminergic reinforcement also modulates the state dependence of STM via the OCTβ2R receptor that is required in the dopaminergic MB-MP1 neurons (Huetteroth, 2015).

Nutrient-dependent LTM does not involve octopamine or sweet-taste-reinforcing dopaminergic neurons. Nutrient reinforcement instead requires dopaminergic neurons innervating γ5b of the mushroom body, whereas those going to β1, β2, and the adjacent α1 region are sufficient. More work will be required to understand this distributed process, which apparently has an immediate and delayed dynamic (Huetteroth, 2015).

Whereas formation and expression of sweet-taste-reinforced STM is insensitive to satiety state, artificial formation and expression of nutrient-relevant memory require flies to be hungry. Even direct stimulation of the relevant rewarding dopaminergic neurons cannot implant appetitive LTM in food-satiated flies. These experiments suggest that hunger establishes an internal state that permits the nutrient-reinforcing signals to be effective. It will be interesting to understand what the permissive state involves and where it is required. Others have previously described a role for CREB-regulated transcription co-activator 1 (CRTC) in enabling hunger-dependent LTM in the fly and promoting persistent memory in the mouse. It therefore seems plausible that such a mechanism might be required in the mushroom body neurons to permit nutrient-dependent reinforcement (Huetteroth, 2015).

The detection of environmental temperature and regulation of body temperature are integral determinants of behaviour for all animals. These functions become less efficient in aged animals, particularly during exposure to cold environments, yet the cellular and molecular mechanisms are not well understood. This study identifies an age-related change in the temperature preference of adult fruit flies that results from a shift in the relative contributions of two parallel mushroom body (MB) circuits-the β'- and β-systems. The β'-circuit primarily controls cold avoidance through dopamine signalling in young flies, whereas the β-circuit increasingly contributes to cold avoidance as adult flies age. Elevating dopamine levels in β'-afferent neurons of aged flies restores cold sensitivity, suggesting that the alteration of cold avoidance behaviour with ageing is functionally reversible. These results provide a framework for investigating how molecules and individual neural circuits modulate homeostatic alterations during the course of senescence (Shih, 2015).

Learned and adaptive behaviors rely on neural circuits that flexibly couple the same sensory input to alternative output pathways. This study shows that the Drosophila mushroom body functions like a switchboard in which neuromodulation reroutes the same odor signal to different behavioral circuits, depending on the state and experience of the fly (see Compartmentalized Architecture of the Mushroom Body). Using functional synaptic imaging and electrophysiology, it was shown that dopaminergic inputs to the mushroom body modulate synaptic transmission with exquisite spatial specificity, allowing individual neurons to differentially convey olfactory signals to each of their postsynaptic targets. Moreover, the dopaminergic neurons function as an interconnected network, encoding information about both an animal's external context and internal state to coordinate synaptic plasticity throughout the mushroom body. These data suggest a general circuit mechanism for behavioral flexibility in which neuromodulatory networks act with synaptic precision to transform a single sensory input into different patterns of output activity (Cohn, 2015).

This study took advantage of the mushroom body's orderly architecture to gain insight into the circuit mechanisms through which neuromodulation mediates flexible sensory processing. Compartmentalized dopaminergic signaling permits independent tuning of synaptic transmission between an individual KC and its repertoire of postsynaptic MBON targets. As a consequence, the same KC odor representation can evoke different patterns of output activity, depending on the state of the animal and the dopaminergic network. Recent data indicate that the ensemble of MBONs acts in concert to bias an animal's behavioral response to an odor such that altering the balance of their activity can modify the olfactory preferences of both naive and trained animals. In accord with such a model, this study revealed how a distributed neuromodulatory network is poised to orchestrate plasticity across all 15 compartments of the mushroom body and reweight the net output of the MBONs, allowing for adaptive behavioral responses based on the immediate needs or past experience of the animal (Cohn, 2015).

Distinct subsets of DANs are sufficient to drive learned olfactory associations, leading to the suggestion they may act autonomously to encode the rewarding or punishing contextual stimuli that assign meaning to an odor. The current data, however, suggest a more complex circuit architecture, in which rich functional interconnectivity between compartments contributes to coordinated and bidirectional patterns of activity across the DAN population. This raises the possibility that reinforcement experiences may be represented by combinatorial patterns of DAN excitation and inhibition in different compartments, endowing the dopaminergic population with a greater capacity to instruct behavior via the limited repertoire of mushroom body outputs. Intriguingly, midbrain dopaminergic neurons responsive to punishment and reward also project to distinct targets in the mammalian brain and display a similar functional opponency as a consequence of reciprocal network interactions. Thus, the concerted and partially antagonistic action of neuromodulatory pathways may represent a general and conserved circuit principle for generating adaptive behavioral responses (Cohn, 2015).

Distinct DAN network activity states are evoked by electric shock and sugar ingestion, reinforcers classically used in associative olfactory conditioning paradigms because of their strong inherent valence. However, similarly distributed patterns of DAN activity are correlated with the fly's motor activity, implying that an animal's behavioral state might serve as a reinforcement stimulus that itself drives synaptic plasticity to shape odor processing. Metabolic states, such as thirst and hunger, have been shown to gate appetitive reinforcement by water and sugar rewards, permitting state-dependent formation of olfactory associations only in motivated animals. The current data highlight an additional facet of how an animal's internal state can regulate dopamine release to adjust the salience of contextual cues. Together, these observations indicate that the distributed DAN network integrates information about external context and internal state with MBON feedback to represent the moment-by-moment experience of an animal and dynamically regulate the flow of olfactory signals through the mushroom body (Cohn, 2015).

The independent regulation of synapses along an axon is thought to permit a single neuron to convey specialized information to different downstream targets, providing additional flexibility and computational power to neural circuits. In the mushroom body, synapse-specific plasticity is achieved through spatially restricted patterns of dopaminergic modulation that divide a KC axon into functionally distinct segments. Thus, the ensemble of synapses within a compartment, as the site of convergence for sensory and contextual signals, represents the elementary functional unit that underlies experience-dependent mushroom body output (Cohn, 2015).

Within a compartment, multiple neuromodulatory mechanisms appear to shape synaptic signaling. Broad potentiation of KC-MBON synapses is seen after DAN activation, but odor-specific depression is seen if DANs were coincidently activated with KCs, consistent with the synaptic changes previously proposed to occur after learning. Taken together, these findings indicate that neuromodulation in the mushroom body instructs opposing forms of synaptic plasticity, analogous to the bidirectional tuning of synaptic strength by dopamine in mammalian brain centers. The molecular mechanisms through which dopamine can direct diverse synaptic changes within a compartment remain to be elucidated, but they may depend on signaling through different dopamine receptors or downstream signaling cascades that function as coincidence detectors. Indeed, while DopR1 in KCs is essential to the formation of learned olfactory associations, this receptor was found to play only a subtle role in the context-dependent patterning of Ca2+ along their axons. Conversely, DopR2 strongly influences the topography of presynaptic Ca2+ along KC axons, in accord with evidence that tonic release of dopamine during ongoing behavior acts through this receptor to interfere with the maintenance of specific learned olfactory associations. Thus, distinct molecular pathways may transform the same dopaminergic reinforcement signals into synaptic changes of opposite polarity to shape olfactory processing based on both the present context and prior experiences of an individual (Cohn, 2015).

The mushroom body has been most extensively studied as a site for associative learning in which the temporal pairing of an odor with a reinforcement experience selectively alters subsequent behavioral responses to that odor. The current data suggest that the convergence of DAN network activity and KC olfactory representations within the mushroom body lobes may drive associative plasticity in each compartment, allowing the odor tuning of the MBON repertoire to reflect the unique experiences of an individual. However, these observations also provide insight into the mushroom body's broader role in the context-dependent regulation of innate behaviors. The ongoing activity of the distributed DAN network, encoding information about an animal's current environmental context and behavioral state, is poised to continuously reconfigure the activity patterns of the MBON population to allow for adaptive responses based on the acute needs of the animal. This context-dependent synaptic modulation could potentially erode odor-specific learned associations within the mushroom body, permitting the immediate circumstances of an animal to dominate over previously learned olfactory associations that may no longer be predictive or relevant. The axons of MBONs ultimately converge with output pathways from the lateral horn, a Drosophila brain center thought to mediate stereotyped responses to odors, providing a potential substrate for learned and context-dependent output from the mushroom body to influence inherent olfactory preferences (Cohn, 2015).

Thus, the dual role of neuromodulation in the mushroom body-to select among alternative circuit states that regulate both innate and learned behaviors-is reminiscent of its function in other higher integrative brain centers. In the basal ganglia, for example, different temporal patterns of dopamine release are thought to select the relevant circuit configurations that control inherently motivated behaviors as well as reinforcement learning. The generation of flexible behavioral responses based on experience, whether past or present, may therefore rely on common integrative brain structures in which neuromodulatory networks act with exquisite spatial precision to shape sensory processing (Cohn, 2015).

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Animals discriminate stimuli, learn their predictive value and use this knowledge to modify their behavior. In Drosophila, the mushroom body (MB) plays a key role in these processes. Sensory stimuli are sparsely represented by approximately 2000 Kenyon cells, which converge onto 34 output neurons (MBONs) of 21 types (see Circuit diagrams of the mushroom body). The role of MBONs were studied in several associative learning tasks and in sleep regulation, revealing the extent to which information flow is segregated into distinct channels and suggesting possible roles for the multi-layered MBON network. Optogenetic activation of MBONs can, depending on cell type, induce repulsion or attraction in flies. The behavioral effects of MBON perturbation are combinatorial, suggesting that the MBON ensemble collectively provides an adaptive mechanism to assign valence-positive or negative survival value-to a sensory stimulus then store that information, and recall it when that same stimulus is encountered again. It is proposed that local, stimulus-specific dopaminergic modulation selectively alters the balance within the MBON network for those stimuli. These results suggest that valence encoded by the MBON ensemble biases memory-based action selection (Aso, 2014: PubMed).

To survive in a dynamic environment, an animal must discover and remember the outcomes associated with the stimuli it encounters. It then needs to choose adaptive behaviors, such as approaching cues that predict food and avoiding cues that predict danger. The neural computations involved in using such memory-based valuation of sensory cues to guide action selection require at least three processes: (1) sensory processing to represent the identity of environmental stimuli and distinguish among them; (2) an adaptive mechanism to assign valence-positive or negative survival value-to a sensory stimulus, store that information, and recall it when that same stimulus is encountered again; and (3) decision mechanisms that receive and integrate information about the valence of learned stimuli and then bias behavioral output. To understand such decision-making processes, one approach is to locate the sites of synaptic plasticity underlying memory formation, identify the postsynaptic neurons that transmit stored information to the downstream circuit and discover how their altered activities bias behavior (Aso, 2014).

The mushroom body (MB) is the main center of associative memory in insect brains. While the MB processes several modalities of sensory information and regulates locomotion and sleep, MB function has been most extensively studied in the context of olfactory memory-specifically, associating olfactory stimuli with environmental conditions in order to guide behavior. In Drosophila, olfactory information is delivered to the MB by projection neurons from each of ~50 antennal lobe glomeruli. Connections between the projection neurons and the ~2000 Kenyon cells (KCs), the neurons whose parallel axonal fibers form the MB lobes, are not stereotyped; that is, individual flies show distinct wiring patterns between projection neurons and KCs. Sparse activity of the KCs represents the identity of odors. The output of the MB is conveyed to the rest of the brain by a remarkably small number of neurons-34 cells of 21 cell types per brain hemisphere (Aso, 2014).

The information flow from the KCs to the MB output neurons (MBONs) has been proposed to transform the representation of odor identity to more abstract information, such as the valence of an odor based on prior experience (see discussion in Aso, 2014a). In contrast to KCs, MBONs have broadly tuned odor responses; any given odor results in a response in most MBONs, although the magnitude of the response varies among MBON cell types. Unlike the stereotyped response to odors of the olfactory projection neurons that deliver odor information to the MB, the odor tuning of the MBONs is modified by plasticity and varies significantly between individual flies, suggesting that MBONs change their response to odors based on experience (Aso, 2014).

For olfactory associative memory in Drosophila, multiple lines of evidence are consistent with a model in which dopamine-dependent plasticity in the presynaptic terminals of KCs alters the strength of synapses onto MBON dendrites. This is thought to provide a mechanism by which the response of MBONs to a specific odor could represent that odor's predictive value. D1-like dopamine receptors and components of the cAMP signaling pathway, such as the Ca2+/Calmodulin-responsive adenylate cyclase encoded by the rutabaga gene, are required specifically in the KCs for memory formatio and rutabaga was shown to be required for the establishment of the differences in MBON odor tuning between individuals. Reward and punishment recruit distinct sets of dopaminergic neurons (DANs) that project to specific regions in the MB lobes. Moreover, exogenous activation of these DANs can substitute for reinforcing stimuli to induce either appetitive or aversive memory, depending on DAN cell type. In sum, while the identity of the learned odor is likely encoded by the small subset of KCs activated by that odor, whether dopamine-mediated modulation assigns positive or negative valence to that odor would be determined by where in the MB lobes KC-MBON synapses are modulated and thus which MBON cell types alter their response to the learned odor (Aso, 2014ab).

Combining the above observations with the comprehensive anatomical characterization of MB inputs and outputs lays the groundwork for testing models of how the MB functions as a whole. It is suggested that each of the 15 MB compartments-regions along the MB lobes defined by the arborization patterns of MBONs and DANs (see Circuit diagrams of the mushroom body)-functions as an elemental valuation system that receives reward or punishment signals and translates the pattern of KC activity to a MBON output that serves to bias behavior by altering either attraction or aversion. This view implies that multiple independent valuation modules for positive or negative experiences coexist in the MB lobes, raising the question of how the outputs across all the modules are integrated to result in a coherent, adaptive biasing of behavior (Aso, 2014).

Although several MBON cell types have been shown to play a role in associative odor memory, the functions of most MBONs have not been studied. Based on anatomical analyses (Aso, 2014a), it is believed that just 34 MBONs of 21 types provide the sole output pathways from the MB lobes. To gain mechanistic insight into how the ensemble of MBONs biases behavior, it would be first important to know the nature of the information conveyed by individual MBONs and the extent to which their functions are specialized or segregated into different information channels. Then it would be necessary to discover how the activities of individual MBONs contribute to influence the behavior exerted by the complete population of MBONs. Thus, in order to understand how memory is translated into changes in behavior, experimental access would be needed to a comprehensive set of MBONs and investigate how the outputs from different MBONs bias behavior, singly and in combination (Aso, 2014).

In the accompanying paper (Aso, 2014a), the detailed anatomy is described of the DANs and MBONs (see Circuit diagrams of the mushroom body and the generation of intersectional split-GAL4 driver lines to facilitate their study. All but one of the 21 MBON cell types consists of only one or two cells per hemisphere. Dendrites of MBONs that use the same neurotransmitter-GABA, glutamate or acetylcholine-are spatially clustered in the MB lobes. Intriguingly, this spatial clustering resembles the innervation patterns of modulatory input by two clusters of dopaminergic neurons, PPL1 and PAM. MBONs have their axonal terminals in a small number of brain regions, but their projection patterns also suggest pathways for relaying signals between compartments of the MB lobes; three MBONs send direct projections to the MB lobes and several other MBONs appear to target the dendrites of specific DANs (Aso, 2014).

Split-GAL4 drivers give the capability to express genetically encoded effectors in identified MBONs to modify their function. This study examine the roles of specific MBONs in various learning and memory tasks as well as in the regulation of locomotion and sleep. Whether direct activation of specific MBONs are sufficient to elicit approach or avoidance was also studied. The results indicate that the ensemble of MBONs does not directly specify particular motor patterns. Instead, MBONs collectively bias behavior by conveying the valence of learned sensory stimuli, irrespective of the modality of the stimulus or the specific reward or punishment used during conditioning (Aso, 2014).

In the insect brain, a sparse and non-stereotyped ensemble of Kenyon cells represents environmental cues such as odors. The behavioral response to these cues can be neutral, repulsive or attractive, influenced by the prior experiences of that individual and how dopaminergic and other modulatory inputs have changed the weight of its KC-MBON synapses. MBONs are thought to encode the predictive value associated with a stimulus. A fly would then use that information to bias its selection of behavioral responses in an ever-changing environment. The accompanying paper (Aso, 2014), describes in detail the projection patterns of the MBON and DAN cell types that comprise the MB lobes in Drosophila. This report begins the process of determining the nature of the information conveyed by MBONs. Specific MBON cell types were correlated with roles in associative memory and sleep regulation (see A map of MBON functions). These anatomical and behavioral results lay the groundwork for understanding the circuit principles for a system of memory-based valuation and action selection (Aso, 2014b).

Optogenetic activation of MBONs in untrained flies can induce approach or avoidance. The ability of the MBONs to induce changes in behavior in the absence of odors suggests that MBONs can bias behavior directly. This observation is consistent with a recent study showing that flies are able to associate artificial activation of a random set of KCs-instead of an odor stimulus-with electric shock, and avoid reactivation of the same set of KCs in the absence of odors (Vasmer, 2014), a result that recapitulates a finding in the potentially analogous piriform cortex of rodents. This study found that the sign of the response to MBON activation was highly correlated with neurotransmitter type; all the MBONs whose activation resulted in avoidance were glutamatergic, whereas all the attractive MBONs were cholinergic or GABAergic (Aso, 2014).

By tracking flies as they encounter a border between darkness and CsChrimson-activating light, activation of an MBON was shown to bias walking direction. Although activation of glutamatergic MBONs repelled flies, the avoidance behaviors were not stereotyped; flies showed a variety of motor patterns when avoiding the red light. This observation implies that MBONs are unlikely to function as command neurons to drive a specific motor pattern, as has been observed, for example, in recently identified descending neurons that induce only stereotyped backward walking (Aso, 2014).

Rather, fly locomotion can be considered as a goal-directed system that uses changes in MBON activity as an internal guide for taxis. For example, walking in a direction that increases the relative activity of aversive-encoding MBONs, which would occur as a fly approaches an odorant it had previously learned to associate with punishment (or when a fly expressing CsChrimson in an avoidance-inducing MBON approaches the CsChrimson activating light), signals the locomotive system to turn around and walk the other direction. Detailed studies of locomotor circuitry will be required to determine the mechanisms of executing such taxic behaviors and should help elucidate how MBON inputs guide this system (Aso, 2014b).

In this view, the MBON population functions as neither a purely motor nor a purely sensory signal. From the motor perspective, as described above, MBONs bias locomotive outcomes rather than dictate a stereotyped low-level motor program. From the sensory perspective, this study has shown that the same MBONs can be required for experience-dependent behavioral plasticity irrespective of whether a conditioned stimulus is a color or an odor, and irrespective of the specific identity of the odor. Taken together with the fact that MBONs lie immediately downstream of the sites of memory formation, these observations support the proposal that MBONs convey that a stimulus has a particular value-but not the identity of the stimulus itself. This contrasts with sensory neurons whose activity can also induce approach or avoidance, but which do convey the stimulus per se. In mammals, neural representations of abstract variables such as 'value', 'risk' and 'confidence' are thought to participate in cognition leading to action selection. From the point of view of this framework, the MBON population representing the value of a learned stimulus and informing locomotion might be operationally viewed as a cognitive primitive (Aso, 2014).

Co-activating multiple MBON cell types revealed that the effects of activating different MBONs appear to be additive; that is, activating MBONs with the same sign of action increases the strength of the behavioral response, whereas activating MBONs of opposite sign reduces the behavioral response. Thus, groups of MBONs, rather than individual MBONs, likely act collectively to bias behavioral responses. Consistent with the idea of a distributed MBON population code, all 19 MBON cell types imaged so far show a calcium response to any given odor (Aso, 2014).

If it is the ensemble activity of a large number of MBONs that determines memory-guided behavior, how can local modulation of only one or a few MB compartments by dopamine lead to a strong behavioral response? Activation of a single DAN such as PPL1-γ1pedc that innervates a highly localized region of the MB can induce robust aversive memory, yet the odor associated with the punishment will activate MBONs from all compartments, including MBONs that can drive approach as well as those that drive avoidance. It is proposed that, in response to a novel odor stimulus, the activities of MBONs representing opposing valences may initially be 'balanced', so that they do not impose a significant bias. Behavior would then be governed simply by any innate preference a fly might have to that odor, using neuronal circuits not involving the MB. Now suppose an outcome associated with that stimulus is learned. Such learning involves compartment-specific, dopamine-dependent plasticity of the KC-MBON synapses activated by that stimulus. If that occurs, the subsequent ensemble response of the MBONs to that stimulus would no longer be in balance and an attraction to, or avoidance of, that stimulus would result. Consistent with this idea, eliminating MB function by disrupting KCs, which are nearly 10% of neurons in the central brain, had surprisingly minor effects on odor preference (Aso, 2014).

Recent studies of dopamine signaling have implicated distinct sites of memory formation within the MB lobes. Consistent with this large body of work, this study found that one type of PPL1 cluster DAN, PPL1-γ1pedc, played a central role in formation of aversive memory in both olfactory and visual learning paradigms. This DAN also mediates aversive reinforcement of bitter taste. For appetitive memory, PAM cluster DANs that innervate other regions of the MB lobes, in particular the compartments of glutamatergic MBONs, are sufficient to induce appetitive memory. These results strongly suggest that the synaptic plasticity underlying appetitive and aversive memory generally occurs in different compartments of the MB lobes (Aso, 2014).

The sign of preference observed in response to CsChrimson activation of particular MBONs was, in general, opposite to that of the memory induced by dopaminergic input to the corresponding MB compartments. For example, activation of MBON-γ1pedc>α/β and MBON-γ2α'1 attracted flies, whereas DAN input to these regions induced aversive memory. Conversely, activation of glutamatergic MBONs repelled flies, while DAN input to the corresponding regions is known to induce appetitive memory. These results are most easily explained if dopamine modulation led to synaptic depression of the outputs of the KCs representing the CS + stimulus. Consistent with this mechanism, the PE1 MBONs in honeybees as well as the V2 cluster MBONs in Drosophila reduce their response to a learned odor and depression of KC-MBON synapses has been shown for octopamine modulation in the locust MB. Moreover, long-term synaptic depression is known to occur in the granular cell synapses to Purkinje cells in the vertebrate cerebellum, a local neuronal circuit with many analogies to the MB. Other mechanisms are also possible and multiple mechanisms are likely to be used. For example, dopamine may modulate terminals of KCs to potentiate release of an inhibitory cotransmitter such as short neuropeptide F, which has been demonstrated to be functional in KCs and hyperpolarizes cells expressing the sNPF receptor. RNA profiling of MBONs should provide insights into the molecular composition of synapses between KCs and MBONs. It is also noteworthy that the effect of dopamine can be dependent on the activity status of Kenyon cells; activation of PPL1-γ1pedc together with odor presentation induces memory, while its activation without an odor has been reported to erase memory. In the vertebrate basal ganglia, dopamine dependent synaptic plasticity important for aversive and appetitive learning is known to result in both synaptic potentiation and synaptic depression (Aso, 2014).

This study sought the effects of selectively and specifically manipulating the activities of a comprehensive set of MBONs on several behaviors. As a consequence, some insights were gained into the extent to which the relative importance of particular MBONs differed between behaviors. Most obvious was the segregation between appetitive and aversive behaviors. For example, this study found that blocking MBON-γ1pedc>α/β impaired both short-term aversive odor and visual memory, suggesting a general role in aversive memory independent of modality. Conversely, a subset of glutamatergic MBONs was required in all three appetitive memory assays. It still remains to be demonstrated that the outputs of these MBONs are required transiently during memory retrieval. Nevertheless, CsChrimson activation experiments demonstrate that activation of these MBONs can directly and transiently induce attraction and avoidance behaviors (Aso, 2014).

In the cases described above, the DANs and MBONs mediating a particular behavior innervate the same regions of MB lobes. Cases were found where the DANs and MBONs required for a behavior do not innervate the same compartments of the MB lobes. For example, even though several cholinergic MBONs are required for appetitive memory, the compartments with cholinergic MBONs do not receive inputs from reward-mediating PAM cluster DANs, but instead from PPL1 cluster DANs that have been shown to be dispensable for odor-sugar memory. What accounts for this mismatch? Perhaps these cholinergic MBONs' primarily function is in memory consolidation rather than retrieval. But the fact that CsChrimson activation of the cholinergic MBON-γ2α'1 and V2 cluster MBONs resulted in attraction, strongly suggests that at least some of the cholinergic MBONs have a role in directly mediating the conditioned response. Indeed, previous studies found a requirement for cholinergic MBONs (the V2 cluster and MBON-α3) during memory retrieva. One attractive model is that requirement of cholinergic MBONs originates from the transfer of information between disparate regions of the MB lobes through the inter-compartmental MBONs connections within the lobes or by way of connections outside the MB, like those described in the next two sections (Aso, 2014).

The multilayered arrangement of MBONs provides a circuit mechanism that enables local modulation in one compartment to affect the response of MBONs in other compartments. Once local modulation breaks the balance between MBONs, these inter-compartmental connections could amplify the differential level of activity of MBONs for opposing effects. For example, the avoidance-mediating MBON-γ4>γ1γ2 targets the compartments of attraction-mediating MBON-γ2α'1 and MBON-γ1pedc>α/β (Aso, 2014).

This network topology might also provide a fly with the ability to modify its sensory associations in response to a changing environment. Consider the α lobe. Previous studies and the current results indicate that circuits in the α lobe play key roles in long-term aversive and appetitive memory. The α lobe is targeted by MBONs from other compartments and comprises the last layer in the layered output model of the MB. The GABAergic MBON-γ1pedc>α/β and the glutamatergic MBON-β1>α both project to α2 and α3, where their axonal termini lie in close apposition. DAN input to the compartments housing the dendrites of these feedforward MBONs induces aversive and appetitive memory, respectively. This circuit structure is well-suited to deal with conflicts between long-lasting memory traces and the need to adapt to survive in a dynamic environment where the meaning of a given sensory input may change. To test the proposed role of the layered arrangement of MBONs in resolving conflicts between old memories and new sensory inputs, behavioral paradigms will be needed that, unlike the simple associative learning tasks used in the current study, assess the neuronal requirements for memory extinction and reversal learning (Aso, 2014).

The neuronal circuits that are downstream of the MBONs and that might read the ensemble of MBON activity remain to be discovered. However, the anatomy of the MBONs suggests that, at least in some cases, summation and canceling effects may result from convergence of MBON terminals on common targets. For example, the terminals of the sleep-promoting cholinergic MBON-γ2α'1 overlap with terminals of wake-promoting glutamatergic MBONs (γ5β'2a, β'2mp and β'2mp bilateral) in a confined area in CRE and SMP. In addition, some MBONs appear to terminate on the dendrites of DANs innervating other compartments, forming feedback loops. Using these mechanisms, local modulation in a specific compartment could broadly impact the ensemble of MBON activity and how it is interpreted (Aso, 2014).

Testing these and other models for the roles of the MBON network, both within the MB lobes and in the surrounding neuropils, will be facilitated by an EM-level connectome to confirm the synaptic connections that were inferred based on light microscopy. Physiological assays will be needed to confirm the sign of synaptic connections and to measure plasticity. For example, the sign of action of glutamate in the targets of glutamatergic MBONs are not known, as this depends on the receptor expressed by the target cells. In this regard, it is noted that previous studies demonstrated a role for NMDA receptors in olfactory memory (Aso, 2014b).

Neurons that are thought to mediate innate response to odors (a subset of projection neurons from the antennal lobes and output neurons from the lateral horn) also project to these same convergence zones (see Convergence zone of MBON terminals as network nodes to integrate innate and learned valences). It is proposed that these convergence zones serve as network nodes where behavioral output is selected in the light of both the innate and learned valences of stimuli. What are the neurons downstream to these convergence zones? One obvious possibility is neurons that project to the fan-shaped body of the central complex whose dendrites are known to widely arborize in these same areas. It would make sense for the MB to provide input to the central complex, a brain region involved in coordinating motor patterns (Aso, 2014).

Using inactivation to uncover the roles of specific cell types is inherently limited by redundancy and resiliency within the underlying neural circuits. For example, consider the MBONs from the α/β lobes. The output of the α/β Kenyon cells is known to be required for retrieval of aversive memory. The current anatomical and behavioral results show that MBON-γ1pedc>α/β, a cell type that was found to be critical for aversive memory, has terminals largely confined inside the α/β lobes, well-positioned to regulate a total 6 types of MBONs from the α/β lobes. Yet no requirement was detected for any of these MBONs in short term aversive memory when tested individually. The ability to detect phenotypes also depends on the strength of the effector; for example, four glutamatergic MBON drivers showed aversive memory impairment in initial screening assays with a strong inhibitor of synaptic function, but these effects could not be confirmed using a weaker effector (Aso, 2014).

The failure to see effects when inactivating individual cell types is most easily explained by combinatorial roles and redundancy between MBONs. It is noted that this high level of resiliency is very reminiscent of observations made with genetic networks, where less than half of gene knockouts of evolutionarily conserved Drosophila genes result in a detectable phenotype. Whether or not a requirement is detected for a particular MBON in a particular learning paradigm is likely to depend on which DANs are recruited by the unconditioned stimulus used in that paradigm as well as the degree of redundancy in the MBON representation of valence. It will be informative to test systematically whether blocking combinations of MBONs, which did not show significant behavioral effect when blocked separately, results in significant memory impairment. It will also be important in future experiments to employ imaging and electrophysiological methods, in which the activities of individual neurons, and the consequences of plasticity, can be observed without being obscured by redundancy (Aso, 2014).

The MBs are implicated in functions beyond processing of associative memory. MBONs that influence approach to, or avoidance of, a learned stimulus may also have roles in innate preference behaviors for temperature and hunger-dependent CO2 avoidance. Moreover, the behavioral repertoire that MBONs govern are expected to go beyond simple approach and avoidance; the MB is known to play a role in experience-dependent regulation of proboscis extension as well as regulation of sleep and post-mating behaviors such as oviposition. Intriguingly, this study found that MBONs whose activation was repulsive promoted wakefulness, whereas MBONs whose activation was attractive promoted sleep; it would make sense for flies to be awake and attentive in an adverse environment. Other internal states, in addition to sleep, are likely to affect the decision to carry out a particular memory-guided behavior; for example, the state of satiety has been shown to regulate memory expression. The diverse influences of MBONs on behavior can be most easily explained if it is assumed that the activity of the ensemble of MBON conveys an abstract representation of both valence and internal state. In this view, the ensemble of MBONs may represent internal states along axes such as pleasant-unpleasant or aroused-not aroused. It is upon these axes that primitive forms of emotion are thought to have evolved (Aso, 2014).

Drosophila melanogaster can acquire a stable appetitive olfactory memory when the presentation of a sugar reward and an odor are paired. However, the neuronal mechanisms by which a single training induces long-term memory are poorly understood. This study shows that two distinct subsets of dopamine neurons in the fly brain signal reward for short-term (STM) and long-term memories (LTM). One subset induces memory that decays within several hours, whereas the other induces memory that gradually develops after training. They convey reward signals to spatially segregated synaptic domains of the mushroom body (MB), a potential site for convergence. Furthermore, a single type of dopamine neuron was identified that conveys the reward signal to restricted subdomains of the mushroom body lobes and induces long-term memory. Constant appetitive memory retention after a single training session thus comprises two memory components triggered by distinct dopamine neurons (Yamagata, 2014).

In Drosophila, sugar ingestion in a single training session induces stable appetitive odor memory. The results showed that observed memory represents the composite of STM and LTM that are induced by the distinct and complementary reward signals of dopamine. These separate reward signals very well corroborate parallel processing of STM and LTM in the MB. The stm-PAM neurons (a specific cluster of dopamine neurons) induce appetitive memory that decays within hours, whereas memory by ltm-PAM gradually develops after training. These dopamine neurons convey reward signals to spatially segregated synaptic domains of the MB, whereas the other PAM cluster neurons may also contribute to LTM reward. Furthermore, this study identified a single type of dopamine neurons (PAM-α1) encoding a reward signal for LTM. The PAM-α1 targets a spatially restricted subdomain in the α-lobe of the MB, suggesting local associative modulation in the α-lobe. These results indicate that sugar ingestion activates multiple reward signals of different qualities to form complementary memories, rather than a single reward system forming STM that later transforms into LTM (Yamagata, 2014).

In the defensive siphon and tail withdrawal reflex of Aplysia, different modes of 5-HT application to the sensory neurons in the pedal and pleural ganglion differentially induce short-term and long-term sensitization memories. As both ganglia are innervated by a single identified serotonergic neuron, it is likely that the tail shock activates the same cell to induce both forms of sensitization independently. This cellular configuration is in strong contrast to the reward system in Drosophila appetitive memory, despite parallel formation of STM and LTM in both systems. The sugar reward may be more intricately encoded in the fly, given the importance of long-lasting food-related memory in survival (Yamagata, 2014).

Representations of different reinforcing stimuli by the same transmitter seems to be variable. In Drosophila aversive memory, reinforcing signals of electric shock and heat punishment converge to the same dopamine neurons, whereas distinct dopamine neurons are recruited for different aversive stimuli in mammals. Appetitive memory of Drosophila is closer to the latter case. However, a critical difference is that stm- and ltm-PAM neurons seem to signal different properties of the same reward, again pointing to more complex representation of the sugar reward. It would be interesting to compare neuronal representations of different rewarding stimuli, such as ethanol and water. An important future question would be to understand physiological mechanisms by which the MB computes the distinct dopamine inputs to control approach behavior (Yamagata, 2014).

Memory induced by ltm-PAM develops gradually after training. This gradual increase may reflect the time to implement learning-dependent molecular changes. Many molecules that are specifically required for LTM have been identified, and some of these molecules are involved in learning-dependent gene transcription/translation. For instance, fasting-dependent LTM that is formed without training repetition requires CREB-regulated transcription coactivator (CRTC)-mediated cAMP response element binding protein (CREB) transcription in the MB. Such a transcription-dependent mechanism takes time and may thus underlie gradual formation of memory induced by the ltm-PAM (Yamagata, 2014).

The complementary memory dynamics as a consequence of the distinct reward signals suggest that the 'intent' of appetitive memory undergoes a transition from palatability to caloric content. Similar temporal transitions have been found in feeding choice, where flies initially choose sugars according to sweet taste, but later prioritize caloric contents. Similarly, ethanol exposure initially acts as an aversive reinforcer, but eventually turns into reward and induces LTM. The sequential regulation of appetitive behavior by the same stimulus may be conserved across relevant appetitive stimuli. As palatability is not always a faithful predictor of its nutritional value, it may be a general design of reward systems to balance short-term benefit and long-term fitness (Yamagata, 2014).

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Activity of dopaminergic neurons is necessary and sufficient to evoke learning-related plasticity in neuronal networks that modulate learning. During olfactory classical conditioning, large subsets of dopaminergic neurons are activated, releasing dopamine across broad sets of postsynaptic neurons. It is unclear how such diffuse dopamine release generates the highly localized patterns of plasticity required for memory formation. This study has mapped spatial patterns of dopaminergic modulation of intracellular signaling and plasticity in Drosophila mushroom body (MB) neurons, combining presynaptic thermogenetic stimulation of dopaminergic neurons with postsynaptic functional imaging in vivo. Stimulation of dopaminergic neurons generated increases in cyclic AMP (cAMP) across multiple spatial regions in the MB. However, odor presentation paired with stimulation of dopaminergic neurons evoked plasticity in Ca2+ responses in discrete spatial patterns. These patterns of plasticity correlated with behavioral requirements for each set of MB neurons in aversive and appetitive conditioning. Finally, broad elevation of cAMP differentially facilitated responses in the gamma lobe, suggesting that it is more sensitive to elevations of cAMP and that it is recruited first into dopamine-dependent memory traces. These data suggest that the spatial pattern of learning-related plasticity is dependent on the postsynaptic neurons' sensitivity to cAMP signaling. This may represent a mechanism through which single-cycle conditioning allocates short-term memory to a specific subset of eligible neurons (gamma neurons) (Boto, 2014).

Dopaminergic neurons are involved in modulating diverse behaviors, including learning, motor control, motivation, arousal, addiction and obesity, and salience-based decision making. In Drosophila, dopaminergic neurons innervate multiple brain regions, including the mushroom body (MB), where they modulate aversive learning, forgetting, state-dependent modulation of appetitive memory retrieval, expression of ethanol-induced reward memory, and temperature-preference behavior (Boto, 2014).

Dopaminergic circuits play a particularly critical role in memory acquisition. During olfactory classical conditioning, where an odor (conditioned stimulus [CS]) is paired with an aversive event (e.g., electric shock; the unconditioned stimulus [US]), dopaminergic neurons respond strongly to the aversive US (Mao, 2009). Dopamine functions in concert with activity-dependent Ca2+ influx to synergistically elevate cyclic AMP (cAMP) (Tomchik, 2009) and PKA (Gervasi, 2010), suggesting that dopamine is one component of a molecular coincidence detector underlying learning. Proper dopamine signaling is necessary for aversive and appetitive memory. Moreover, driving activity of a subset of TH-GAL4+ dopaminergic neurons that differentially innervates the vertical α/α' MB lobes (with less dense innervation of the horizontal β/β'/γ lobes, peduncle, and calyx), is sufficient to induce behavioral aversion to a paired odorant in larvae and adult flies. Conversely, stimulation of a different set of Ddc-GAL4+ dopaminergic neurons, the PAM cluster that innervates mainly the horizontal β/β'/γ lobes, is sufficient to induce behavioral attraction to a paired odorant. Thus, dopaminergic neurons comprise multiple circuits with distinct roles in memory acquisition (Boto, 2014).

Multiple subsets of MB neurons receive CS and US information and express molecules associated with the coincidence detection, making them theoretically eligible to generate dopamine/cAMP-dependent plasticity. Yet only some subsets are required to support memory at any given time following conditioning, leaving open the question of how spatial patterns of plasticity are generated during conditioning. This question has been approached, by using a technique to probe the postsynaptic effects of neuronal pathway activation. Odor presentation was paired with stimulation of presynaptic dopaminergic neurons via ectopic expression of the heat-sensitive channel TRPA1, while monitoring postsynaptic effects with genetically encoded optical reporters for Ca2+, cAMP, and PKA in vivo (Boto, 2014).

The present data demonstrate four major points about how dopaminergic circuits function in neuronal plasticity underlying olfactory classical conditioning. (1) Stimulation of small subsets of dopaminergic neurons evokes consistent, compartmentalized elevations of cAMP across the MB lobes. (2) Broad stimulation of dopaminergic neurons generates broad postsynaptic elevation of cAMP, but Ca2+ response plasticity occurs in discrete spatial regions. (3) Stimulation of TH-GAL4+ neurons and Ddc/R58E02-GAL4+ neurons, which mediate opposing behavioral responses to conditioned stimuli, generates an overlapping pattern of Ca2+ response plasticity in the γ lobe, with additional regions recruited by Ddc/R58E02-GAL4+ stimulation. Finally, (4) the spatial pattern of plasticity coincides with differential sensitivity to cAMP in the γ lobe. Collectively, these data suggest that different subsets of neurons exhibit heterogeneous sensitivity to activation of second messenger signaling cascades, which might shape their responses to neuromodulatory network activity and modulate their propensity for recruitment into memory traces (Boto, 2014).

The data suggest that dopaminergic neurons mediate Ca2+ response plasticity largely in the γ lobe and suggest a potential mechanism for localization of short-term, learning-related plasticity. These data coincide with multiple previous studies that have demonstrated a critical role of γ neurons in short-term memory. Rescue of Rutabaga (Rut) in the γ lobe of rut mutants is sufficient to restore performance in short-term memory, whereas rescue in α/β lobes supports long-term memory. Rescue of the D1-like DopR receptor in the γ lobe is sufficient to rescue both short- and long-term memory in a mutant background, suggesting that the γ neurons mediate the dopaminergic input during conditioning. In addition, stimulating MP1 dopaminergic neurons innervating the heel of the γ lobe is sufficient as an aversive reinforcer. Finally, learning induces plasticity in synaptic vesicle release from MB γ lobes, which depends in part on G(o) signaling (Zhang, 2013). The data support a critical role for the γ lobe in short-term memory. Furthermore, the observation of differential sensitivity of the γ lobe to cAMP might provide an elegant explanation for why it is specifically recruited into short-term memory traces (Boto, 2014).

Direct elevation of cAMP was sufficient to generate localized, concentration-dependent Ca2+ response plasticity in the MB γ lobe in these experiments. Because applying forskolin in the bath is expected to elevate cAMP across the brain, the spatial specificity of the effect is remarkable. This was not an acute effect, because the forskolin was washed out before imaging the first postconditioning odor response. At the concentrations that were tested, only the γ lobe was facilitated. Therefore, it is concluded that the γ lobe is most sensitive to elevation of cAMP, which has the effect of differentially recruiting γ neurons into the representation of short-term memory via dopamine-mediated neuronal plasticity. It is possible that additional signaling cascades are involved in generating learning-related plasticity in α/β and α'/β' neurons, given that no Ca2+ response plasticity was observed in those neurons following forskolin application (Boto, 2014).

The dominant model for cellular mechanisms of olfactory associative learning is that integration of information about the conditioned and unconditioned stimuli are integrated by Rut, which functions as a molecular coincidence detector. This would suggest that MB neurons, which receive CS and US information, would exhibit at least somewhat uniform Ca2+ response plasticity. From this molecular and cellular perspective, the finding that the α/β and α'/β' neurons did not exhibit Ca2+ response plasticity when an odor was paired with stimulation of dopaminergic neurons is surprising. These neurons are theoretically eligible to encode memory, because they receive information about the CS and US. However, the finding that γ neurons differentially exhibit dopamine-dependent plasticity following single-cycle conditioning is consistent with the data from the behavioral experiments. In summary, the present results suggest that differential cAMP sensitivity provides a potential mechanism allowing specific subsets of eligible neurons in an array (γ neurons) to differentially encode CS-US coincidence relative to other subsets (α/β neurons) that also receive CS/US information (Boto, 2014).

Locomotion involves rhythmic limb movement patterns that originate in circuits outside the brain. Purposeful locomotion requires descending commands from the brain, but it is not understood how these commands are structured. Focusing on the control of steering in walking Drosophila. First, different limb "gestures" associated with different steering maneuvers are described. Next, this study identified a set of descending neurons whose activity predicts steering. Focusing on two descending cell types downstream from distinct brain networks, this study shows that they evoke specific limb gestures: one lengthens strides on the outside of a turn, while the other attenuates strides on the inside of a turn. Notably, a single descending neuron can have opposite effects during different locomotor rhythm phases, and networks positioned to implement this phase-specific gating were identified. Together, these results show how purposeful locomotion emerges from brain cells that drive specific, coordinated modulations of low-level patterns (Yang, 2023).

Insect asynchronous flight is one of the most prevalent forms of animal locomotion used by more than 600,000 species. Despite profound insights into the motor patterns, biomechanics and aerodynamics underlying asynchronous flight, the architecture and function of the central-pattern-generating (CPG) neural network remain unclear. On the basis of an experiment-theory approach including electrophysiology, optophysiology, Drosophila genetics and mathematical modelling, this study identified a miniaturized circuit solution with unexpected properties. The CPG network consists of motoneurons interconnected by electrical synapses that, in contrast to doctrine, produce network activity splayed out in time instead of synchronized across neurons. Experimental and mathematical evidence support a generic mechanism for network desynchronization that relies on weak electrical synapses and specific excitability dynamics of the coupled neurons. In small networks, electrical synapses can synchronize or desynchronize network activity, depending on the neuron-intrinsic dynamics and ion channel composition. In the asynchronous flight CPG, this mechanism translates unpatterned premotor input into stereotyped neuronal firing with fixed sequences of cell activation that ensure stable wingbeat power and, as we show, is conserved across multiple species. These findings prove a wider functional versatility of electrical synapses in the dynamic control of neural circuits and highlight the relevance of detecting electrical synapses in connectomics (Hurkey, 2023).

Relative motion breaks a camouflaged target from a same-textured background, thus eliciting discrimination of a motion-defined object. Ring (R) neurons are critical components in the Drosophila central complex, which has been implicated in multiple visually guided behaviors. Using two-photon calcium imaging with female flies, this study demonstrated that a specific population of R neurons that innervate the superior domain of bulb neuropil, termed superior R neurons, encoded a motion-defined bar with high spatial frequency contents. Upstream superior tuberculo-bulbar (TuBu) neurons transmitted visual signals by releasing acetylcholine within synapses connected with superior R neurons. Blocking TuBu or R neurons impaired tracking performance of the bar, which reveals their importance in motion-defined feature encoding. Additionally, the presentation of a low spatial frequency luminance-defined bar evoked consistent excitation in R neurons of the superior bulb, whereas either excited or inhibited responses were evoked in the inferior bulb. The distinct properties of the responses to the two bar stimuli indicate there is a functional division between the bulb subdomains. Moreover, physiological and behavioral tests with restricted lines suggest that R4d neurons play a vital role in tracking motion-defined bars. It is concluded that the central complex receives the motion-defined features via a visual pathway from superior TuBu to R neurons and might encode different visual features via distinct response patterns at the population level, thereby driving visually guided behaviors (Duan, 2023).

The recent discovery of the head-direction (HD) system in fruit flies has provided unprecedented insights into the neural mechanisms of spatial orientation. Despite the progress, the neural substance of global inhibition, an essential component of the HD circuits, remains controversial. Some studies suggested that the ring neurons provide global inhibition, while others suggested the Δ7 neurons. The present study provides evaluations from the theoretical perspective by performing systematic analyses on the computational models based on the ring-neuron (R models) and Δ7-neurons (Delta models) hypotheses with modifications according to the latest connectomic data. Four tests were performed: robustness, persistency, speed, and dynamical characteristics. It was discovered that the two models led to a comparable performance in general, but each excelled in different tests. The R Models were more robust, while the Delta models were better in the persistency test. A hybrid model was tested that combines both inhibitory mechanisms. While the performances of the R and Delta models in each test are highly parameter-dependent, the Hybrid model performed well in all tests with the same set of parameters. These results suggest the possibility of combined inhibitory mechanisms in the HD circuits of fruit flies (Chang, 2023).

How different sensory stimuli are collected, processed, and further transformed into a coordinated motor response is a fundamental question in neuroscience. In particular, the internal and external conditions that drive animals to switch to backward walking and the mechanisms by which the nervous system supports such behavior are still unknown. In fruit flies, moonwalker descending neurons (MDNs) are considered command-type neurons for backward locomotion as they receive visual and mechanosensory inputs and transmit motor-related signals to downstream neurons to elicit backward locomotion. Whether other modalities converge onto MDNs, which central brain neurons activate MDNs, and whether other retreat-driving pathways exist is currently unknown. This study shows that olfactory stimulation can elicit MDN-mediated backward locomotion. Moreover, the moonwalker subesophageal zone neurons (MooSEZs), a pair of bilateral neurons, which can trigger straight and rotational backward locomotion. MooSEZs act via postsynaptic MDNs and via other descending neurons. Although they respond to olfactory input, they are not required for odor-induced backward walking. Thus, this work reveals an important modality input to MDNs, a novel set of neurons presynaptic to MDNs driving backward locomotion and an MDN-independent backward locomotion pathway (Israel, 2022).

This study showed that olfactory stimulus can trigger backward locomotion, which is mediated by MDNs. The MooSEZs, which trigger backward locomotion were unequivocally identified in the fly brain. MooSEZs synapse onto MDNs and act both upstream and in parallel to them. Although located in the SEZ, MooSEZs do not seem to respond to gustatory stimuli. Rather, MooSEZs respond to olfactory input. It was further shown that MooSEZs are not necessary for olfactory-driven backward locomotion. However, it was demonstrated that MooSEZs contribute to strong rotational backward locomotion, presumably via DNa01 and DNa02, which are downstream of MooSEZs (Israel, 2022).

The anatomy of the MooSEZs suggests mild polarity, with dendritic regions mostly in the ventral SEZ, and presynaptic projections predominantly in the inferior protocerebrum. The protocerebral innervation is found in the inferior posterior slope (IPS) and the crepine, but also reaching into wedge, vest, and the lower lateral accessory lobe (LAL), posterior to the AL. In the SEZ, the MooSEZs stay in the gnathal ganglia, sparing the prow. The SEZ predominantly receives mechanosensory and gustatory sensory input and SEZ output controls movements involved in feeding behavior. Although the main dendritic region of MooSEZs is in the ventral SEZ, they do not seem to respond to bitter taste. Rather, MooSEZs respond to olfactory input (Israel, 2022).

Interestingly, a significant portion of MooSEZ axonal projections terminate within the LAL, a major premotor region. The LAL receives inputs from the central complex (CX), the navigation hub of the insect brain, as well as from a variety of sensory processing regions, and projects output signals mainly through descending tracts to the VNC. The LAL was shown to be involved in a wide range of orientation-related behaviors across different insect species such as pheromone orientation in moths, flight in locusts, turning in cockroaches, and phonotaxis in crickets. Given the exceptional functional similarities of the LAL across insect species, it will be interesting to find whether MooSEZs are part of a canonical locomotor circuit and thus conserved across insect species (Israel, 2022).

MDNs were demonstrated to receive sensory information from the fly's visual system and mechanosensory input from the VNC. Whether other sensory inputs converge onto MDNs was not resolved yet. The current results add olfaction to the multi-modality input of MDNs. In addition, this study identified the SEZ as participating in MDN input. A recent study in larvae reported the existence of two SEZ neurons, AMB neurons, which mediate backward crawling in response to aversive blue light via activation of MDNs. The current results combined with the results from larvae, suggest that SEZ neurons may also act as modality integrators. The participation of the SEZ as an input source to MDNs also raises the option that SEZ gustatory or tactile input can activate MDNs (Israel, 2022).

These results indicate that MooSEZs add an additional MDN-independent component to the backward walking motor response. Indeed, some experimental evidence from previously published work in both adult flies and larvae are in line with this conclusion. For instance, in adult flies, optogenetic activation of TLAs while blocking MDNs resulted in residual backward walking. In addition, backward movement was correlated with a large membrane voltage difference in DNa01 and DNa02, two non-MDN descending neurons, which are downstream of MooSEZs. In larvae, it was recently demonstrated that MDNs were not necessary for dead end evoked backward crawling. Taken together, it seems that rather than a single command circuit originating in MDNs, additional neural circuits are recruited to allow for an adaptive backward locomotion response (Israel, 2022).

This study shows that unilateral activation of MooSEZs elicits ipsilateral backward turning. This result is analogous to recent findings demonstrating that unilateral activation of specific motor-related neurons can induce walking with a significant ipsilateral turning component. In addition, it was previously demonstrated that unilateral activation of MDNs also led to a steering bias.20 Thus, as MDNs are postsynaptic to MooSEZs, it is possible that the rotational response elicited by MooSEZ unilateral activation might be mediated to some extent by them. However, the data show that blocking MDNs had no effect on MooSEZ-induced backward turning suggesting that the dominant pathway driving the observed rotational backward walking is MDN independent. This conclusion is further supported by the anatomical data demonstrating that both DNa01 and DNa02, which participate in steering maneuvers and descend to the VNC,83,73 are downstream targets of MooSEZs. Flies usually encounter in their natural habitat complex odor plumes, which are constantly changing as a function of the flies' spatial locations relative to the positions of the varied odor sources around them, and the wind structure in their surroundings. Thus, flies are frequently exposed to asymmetrical olfactory cues in their natural environment. The ability of MooSEZs to recruit downstream motor circuits to perform a directional retreat response upon asymmetrical activation may enable flies to effectively guide their behavior under natural conditions (Israel, 2022).

This study demonstrates that an olfactory cue can trigger backward locomotion via MDNs. It was also shown that olfactory input activates MooSEZs that can trigger backward locomotion by themselves independent of MDNs. However, silencing MooSEZs had no effect on odor-evoked backward locomotion. These observations suggest that MooSEZs do not function as command-type neurons for odor-evoked backward locomotion, but rather seem to be part of a distributed neural circuit architecture controlling backward locomotion. Thus, odors may activate multiple parallel neural pathways driving backward locomotion. Indeed, functional redundant signaling also exists in other neuronal circuits in the fly brain. For example, optogenetic activation of LC16 VPNs, which were shown to respond to a visual looming stimulus,46 elicited backward locomotion via MDNs. However, silencing LC16 neurons had no effect on backward locomotion in response to a visual looming stimulus.46 Thus, although optogenetic activation of LC16 neurons drives backward locomotion, silencing these neurons has no effect on the behavioral output to the visual stimulus that activates them. Another example are neurons expressing SIFamide (SIFa). These neurons have elevated activity in starved flies and are involved in hunger-mediated behavior. Acute activation of SIFa neurons was sufficient to increase the response of fed flies to a food-related odor and to enhance their food consumption. However, silencing SIFa neurons did not decrease food intake in starved flies. Taken together, contrary to optogenetic activation of MooSEZs, which generates a robust and constant activation that further activates MDN-independent motor circuits, it seems that ecological odor stimulation elicits a physiologically adequate response in MooSEZs, which is presumably used to support odor-evoked backward locomotion but cannot sustain odor-evoked backward locomotion by itself (Israel, 2022).

Visual motion provides rich geometrical cues about the three-dimensional configuration of the world. However, how brains decode the spatial information carried by motion signals remains poorly understood. This paper studied a collision-avoidance behavior in Drosophila as a simple model of motion-based spatial vision. With simulations and psychophysics, it was demonstrated that walking Drosophila exhibit a pattern of slowing to avoid collisions by exploiting the geometry of positional changes of objects on near-collision courses. This behavior requires the visual neuron LPLC1, whose tuning mirrors the behavior and whose activity drives slowing. LPLC1 pools inputs from object and motion detectors, and spatially biased inhibition tunes it to the geometry of collisions. Connectomic analyses identified circuitry downstream of LPLC1 that faithfully inherits its response properties. Overall, these results reveal how a small neural circuit solves a specific spatial vision task by combining distinct visual features to exploit universal geometrical constraints of the visual world (Tanaka, 2022).

This study explored a collision avoidance behavior in walking Drosophila and its underlying circuit mechanisms as a simple model of motion-based spatial vision. Using high-throughput psychophysics experiments, it was demonstrated that back-to-front motion in the frontolateral visual field—a geometrical cue for near collision—causes slowing in walking flies. Using genetic silencing and activation experiments, the visual projection neuron LPLC1 was shown to be necessary for this putative collision avoidance behavior and its activity is sufficient to cause slowing in walking flies. Physiological response properties of LPLC mirrored the visual tuning of the slowing behavior, most notably in its spatial bias in direction selectivity, which was also consistent with the geometry of near collisions. Using connectomic analyses, optogenetics, and neurochemical imaging and manipulation, it was shown that object-selective T2 and T3 inputs are pooled with direction-selective T4/T5 inputs, likely establishing the object- and direction-selectivity of LPLC1, while spatially biased glutamatergic inhibition creates its position-dependent tuning. Lastly, a downstream neuron of LPLC1 called PLP219 was found to be sufficient to cause slowing, and to inherit the response property of LPLC1 faithfully (Tanaka, 2022).

As objects move relative to an observer, the apparent size and position of the object systematically change as dictated by geometry. How animals detect change in object size and use it to avoid predation has been well studied in various vertebrate species ranging from primates, rodents, birds, and fish , as well as in insects. In contrast, less is known about how and when animals use positional changes or directional motion to detect and avoid collision with moving objects. In general, positional changes of moving objects are more salient than their changes in apparent size: One can show that the maximum apparent expansion rate of an object with radius R moving at a given speed is always less than its maximum apparent translational velocity when the object is more than R away from the observer. Moreover, the ratio between the maximal translation rate and the maximal expansion rate can become arbitrarily large as the object is further and further from the observer. Intuitively, these results correspond to the fact that one can easily tell whether someone 100 meters away is running to the right or left, while it is difficult to tell if that same person is running towards or away from you, based solely on visual motion. This saliency of translation rates is likely one reason that aerial predators employ interception strategies that minimize their apparent positional shifts on their prey's retinae. Less sophisticated pursuit strategies, often used in non-predatory chasing among conspecifics, generate positional changes that can be used by pursuees to detect pursuers. Note that even predators that employ sophisticated strategies will suffer from positional changes after sudden turns of the prey until they settle into a new interception course (Tanaka, 2022).

Positional changes are therefore a useful cue to simply detect objects such as conspecifics and predators, but back-to-front motion in particular can be predictive of future collisions. This is because approaching objects appear to be moving back-to-front only when they will cross the path of the observer in front, which would then pose collision risks if the object slows or stops. This conjecture was by running a simple simulation with randomized trajectories. Based on this geometrical argument, the direction selective slowing behavior of the flies examined in this study is interpreted as a collision avoidance behavior. This is in contrast to other object motion-triggered freezing behaviors in both flies and mice, which are not selective for stimulus direction and thus are unlikely to be a specific response to predicted collision (Tanaka, 2022).

This study found retinotopic biases in the direction selectivity of both behaviors and neural processing. First, the direction selectivity of the collision avoidance slowing to the back-to-front direction was more pronounced in the frontolateral visual field. In addition, direction selectivity of LPLC1 neurons also strongly correlated with the azimuthal location of their receptive field (Tanaka, 2022).

Since the frontolateral visual field is where back-to-front motion is most predictive of immediate collision, the spatial bias in the LPLC1 circuitry can be seen as an adaptation to this geometry (Tanaka, 2022).

Retinotopic biases in visual processing have been found in diverse species. For example, in vertebrate retinae, circuit features such as opsin expression, dendritic morphology, and synaptic strengths can all vary systematically across visual space, depending on species. It is also well established that features such as receptive field sizes and orientation selectivity exhibit retinotopic biases in primate visual cortices. Although these biases have been variously speculated to be adaptations to unique sensory ecology of different species, few were connected to strong geometrical explanations or to behavior (Tanaka, 2022).

Importantly, the geometrical justification provided in this study for the spatial bias in direction selectivity for collision detection is not specific to flies. Thus, it is likely that similar biases exist in other sighted species, arrived at through convergent evolution (Tanaka, 2022).

Indeed, rodent superior colliculus—a center of visual threat detection—has been reported to exhibit a similar retinotopic bias where back-to-front and upward motion is overrepresented in the upper lateral visual field, likely mirroring the geometry of approaching overhead predators (Tanaka, 2022).

Although this study focused on LPLC1's involvement in collision avoidant slowing behavior in walking flies, this does not preclude the possibility that LPLC1 is involved in different behavioral programs in other sensory and behavioral contexts. Supporting this idea, multiple downstream neurons of LPLC1 were found whose activation did not result in slowing and also had divergent visual response properties. Indeed, a previous study reported that strong optogenetic activation of LPLC1 can lead to behavioral phenotypes other than slowing, such as jumping (Tanaka, 2022).

Descending neurons DNp03 and DNp06, which receive inputs from other loom-sensitive, jump-inducing VPNs (LC4, LC6), make good candidates for the neural basis of such jumping phenotypes (Tanaka, 2022).

An interesting question is how the activation of LPLC1 neurons by different stimuli (e. g., small objects moving back-to-front vs. looming objects) results in different behavioral responses. For example, one can imagine that the activation of LPLC1 without activation of other loom sensitive cells (e.g., LC4, LC6) is decoded as the presence of a conspecific in a collision course to initiate slowing, whereas simultaneous activation of LPLC1 alongside other loom detectors strongly implies predators and thus triggers rapid escape. How such population-level decoding and behavioral decision-making is implemented through the network of interglomerular local neurons is of particular interest for future studies (Tanaka, 2022).

In flies, the lobula complex consists of the lobula and lobula plate, which are the highest order brain neuropils that remain specialized for visual processing. Among these two neuropils, lobula plate has been historically under intensive study as the neural basis of visual motion detection and stabilization reflexes, while the functions of the lobula neuropil have remained less clear. The recent series of studies on lobula output neurons have started to show that these neurons detect ethologically relevant objects, like mates and predators, to drive specific behavioral programs. Visual projection neurons innervating both lobula and lobula plate, including LPLC1, are uniquely situated to integrate these object and motion signals. This study showed that LPLC1 likely pools inputs from motion- and object-detecting interneurons (T4/T5 and T2/T3 neurons) to construct a more complex visual feature. While there are other visual projection neuron types spanning lobula plate and lobula whose physiology have been studied, lobula inputs to those neurons remain to be explored (Tanaka, 2022).

Interestingly, a similar computational motif of convergence between motion- and object-detecting pathways seems to be present in the early visual systems of vertebrates as well. Vertebrate retinae are equipped with retinal ganglion cells selective for motion directions as well as small objects. The axon terminals of motion- and object-selective ganglion cells innervate shallowest layers of optic tectum in zebrafish as well as of superior colliculus in mice. While the internal circuitry of the optic tectum / superior colliculus is still not well understood, physiological studies on the neural bases of prey capture in larval zebrafish have identified tectal neurons that show direction selective responses to small objects similar to LPLC1. Similarly, narrow field neurons in mouse superior colliculus, which are also necessary for prey capture behavior, exhibit direction selectivity as well as tight tuning to small object sizes. These results suggest that integration of motion- and object-detector outputs similar to LPLC1 indeed takes place in the optic tectum / superior colliculus. Parallels between vertebrates and invertebrates in the early layers of visual processing and motion detection have been noted. The findings reported in this study extend the computational analogies between insect and vertebrate visual systems to the motif of initial segregation and subsequent convergence of motion and object detecting pathways to drive specialized object-detection behaviors (Tanaka, 2022).

Flexible mapping between activity in sensory systems and movement parameters is a hallmark of motor control. This flexibility depends on the continuous comparison of short-term postural dynamics and the longer-term goals of an animal, thereby necessitating neural mechanisms that can operate across multiple timescales. To understand how such body-brain interactions emerge across timescales to control movement, whole-cell patch recordings were performed from visual neurons involved in course control in Drosophila. The activity of leg mechanosensory cells, propagating via specific ascending neurons, is critical for stride-by-stride steering adjustments driven by the visual circuit, and, at longer timescales, it provides information about the moving body's state to flexibly recruit the visual circuit for course control. Thus, these findings demonstrate the presence of an elegant stride-based mechanism operating at multiple timescales for context-dependent course control. It is proposed that this mechanism functions as a general basis for the adaptive control of locomotion (Fujiwara, 2022).

Neural circuits must both execute the behavioral repertoire of individuals and account for behavioral variation across species. Understanding how this variation emerges over evolutionary time requires large-scale phylogenetic comparisons of behavioral repertoires. This study describes the evolution of walking in fruit flies by capturing high-resolution, unconstrained movement from 13 species and 15 strains of drosophilids. Walking can be captured in a universal behavior space, the structure of which is evolutionarily conserved. However, the occurrence of and transitions between specific movements have evolved rapidly, resulting in repeated convergent evolution in the temporal structure of locomotion. Moreover, a meta-analysis demonstrates that many behaviors evolve more rapidly than other traits. Thus, the architecture and physiology of locomotor circuits can execute precise individual movements in one species and simultaneously support rapid evolutionary changes in the temporal ordering of these modular elements across clades (York, 2022).

Neurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. Different motion detection circuits have different velocity sensitivity, but it remains untested how the response dynamics of individual cell types drive this tuning. This study sped up or slowed down specific neuron types in Drosophila's motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neuron types upstream of motion detectors increased their sensitivity to fast or slow visual motion, exposing distinct roles for excitatory and inhibitory dynamics in tuning directional signals, including a role for the amacrine cell CT1. A circuit model constrained by functional data and anatomy qualitatively reproduced the observed tuning changes. Overall, these results reveal how excitatory and inhibitory dynamics together tune a canonical circuit computation (Gonzalez-Suarez, 2022).

To navigate towards a food source, animals frequently combine odor cues about source identity with wind direction cues about source location. Where and how these two cues are integrated to support navigation is unclear. This study describes a pathway to the Drosophila fan-shaped body that encodes attractive odor and promotes upwind navigation. This study showed that neurons throughout this pathway encode odor, but not wind direction. Using connectomics, fan-shaped body local neurons called hΔC that receive input from this odor pathway and a previously described wind pathway. hΔC neurons exhibit odor-gated, wind direction-tuned activity, that sparse activation of h∆C neurons promotes navigation in a reproducible direction, and that hΔC activity is required for persistent upwind orientation during odor. Based on connectome data, a computational model was developed showing how hΔC activity can promote navigation towards a goal such as an upwind odor source. The results suggest that odor and wind cues are processed by separate pathways and integrated within the fan-shaped body to support goal-directed navigation (Matheson, 2022).

Many behavioural tasks require the manipulation of mathematical vectors, but, outside of computational models, it is not known how brains perform vector operations. This study shows how the Drosophila central complex, a region implicated in goal-directed navigation, performs vector arithmetic. First, a neural signal in the fan-shaped body is described that explicitly tracks the allocentric travelling angle of a fly, that is, the travelling angle in reference to external cues. Past work has identified neurons in Drosophila and mammals that track the heading angle of an animal referenced to external cues (for example, head direction cells), but this new signal illuminates how the sense of space is properly updated when travelling and heading angles differ (for example, when walking sideways). A neuronal circuit was characterized that performs an egocentric-to-allocentric (that is, body-centred to world-centred) coordinate transformation and vector addition to compute the allocentric travelling direction. This circuit operates by mapping two-dimensional vectors onto sinusoidal patterns of activity across distinct neuronal populations, with the amplitude of the sinusoid representing the length of the vector and its phase representing the angle of the vector. The principles of this circuit may generalize to other brains and to domains beyond navigation where vector operations or reference-frame transformations are required (Lyu 2022).

Whether mammalian brains have neurons that are tuned to the allocentric travelling direction of an animal as in Drosophila is still unknown. Although a defined population of neurons tuned to travelling direction has yet to be highlighted in mammals, such cells could have been missed because their activity would loosely resemble that of the head-direction cells outside a task in which the animal is required to sidestep or walk backwards (Lyu 2022).

Neurons are often modelled as summing their synaptic inputs, but the heading inputs that PFN cells receive from the EPG system appear to be multiplied by the self-motion (for example, optic flow) input, resulting in an amplitude or gain modulation. Multiplicative or gain-modulated responses appear in classic computational models for how neurons in area 7a of the primate parietal cortex might implement a coordinate transformation, alongside similar proposals in mammalian navigation. The Drosophila circuit described in this study strongly resembles aspects of the classic models of the parietal cortex. Units that multiply their inputs are also at the core of the 'attention' mechanism used, for example, in machine-based language processing. The experimental evidence for input multiplication in a biological network may indicate that real neural circuits have greater potential for computation than is generally appreciated (Lyu 2022).

This study describes a travelling direction signal and how it is built; related results and conclusions appear in a parallel study. The mechanisms described for calculating the travelling direction are robust to left-right rotations of the head and to the possibility of the allocentric projection vectors being non-orthogonal. It is possible that the travelling signal of hΔB cells is compared with a goal-travelling direction to drive turns that keep a fly along a desired trajectory. Augmented with an appropriate speed signal (or if the fly generally travels forward relative to its body), the hΔB signal could also be integrated over time to form a spatial-vector memory via path integration. There are hundreds more PFN cells beyond the 40 PFNd and 20 PFNv cells examined in this study, and thus the central complex could readily convert other angular variables from egocentric to allocentric coordinates via the algorithm described in this study. Because many sensory, motor and cognitive processes can be formalized in the language of linear algebra and vector spaces, defining a neuronal circuit for vector computation may open the door to better understanding of several previously enigmatic circuits and neuronal activity patterns across multiple nervous systems (Lyu 2022).

Ephaptic transmission of light information from photoreceptor cells in the retina mediates the increase in firing rate in the OSNs during odor stimulations. This study has not revealed whether the ephaptic transmission directly changes the firing rate of the OSNs. Amputation of the antennal nerve abolished the firing rate increases during sustained light, suggesting that once the light information might be received by neurons in the brain, the information would be relayed by the neurons through the antennal nerve to the antenna, resulting in the firing rate increases in the OSNs (Ikeda, 2022).

While ephaptic coupling has been reported earlier, such as between neighboring neurons within the same sensillum, or between Purkinje cells, which is at a distance of <100 μm , this study shows that ephaptic transmission reaches >200 μm in vivo, equivalent to the depth of the entire fly brain, beyond the distance between neighboring neurons. Light stimulations cause ~10 mV field potential deflections in a retina. If endogenous fields are neglected in the brain, light stimulations may induce ~33.3 mV/mm electric field between the retina and center of the brain (0 mV), since the distance between them is ~300 μm. This electric field is strong enough to modulate neural activities, as even weaker electric fields (<0.5 mV/mm) changed the firing patterns of neurons in vitro (Ikeda, 2022).

In rodents, the firing rate of cerebellar Purkinje cells either decreased or increased when a current was injected into the extracellular field around their axons, causing field potential changes of 0.2 mV. In insects, odor-evoked field potential oscillations whose amplitude is comparable with that caused by the current injection in the rodents, are induced by synchronous firing of olfactory neurons in the antennal lobe which are mediated by GABAergic neurons forming reciprocal synapses with excitatory projection neurons. Changes in the extracellular field potential are commonly observed in many nervous systems. While such extracellular field potential activities have been considered as a side effect of synchronized spiking of neurons, this study suggests that such field potential changes evoked by a sensory stimulus can control the excitability of distant neurons, in addition to adjacent neurons. As ephaptic transmission is more effective at a short distance, the ephaptic transmission from the retinae may contribute significantly to firing rate changes in downstream neurons of the photoreceptor cells in the optic lobe (Ikeda, 2022).

This study also revealed that odor responses of OSNs were clearly modulated when light conditions changed transiently. This mechanism may help flies switch attention to newly presented sensory cues or maintain attention toward those remaining after the change. Turning the light on, for example, reduces the firing rates of the OSNs, which may enable the flies to pay more attention to visual information, whereas turning the light off increases the firing rates of the OSNs, which may help them attend to olfactory sensory cues (Ikeda, 2022).

Recent connectome analyses have revealed the entire synaptic network in the CNS in Drosophila and provides insight into how neural information is subject to synaptic relays to determine the behavioral output. This study has show that ephaptic relays also contribute to modulating the firing rate of distant neurons and modify the sensory responses that can change momentarily in a context-dependent manner. To build an integrated model of the fly brain, we should also consider ephaptic relay of neural information (Scheffer and Meinertzhagen, 2021). The compound eye-antenna model would be a suitable model to determine the role of ephaptic transmission in neural processing (Ikeda, 2022).

Memory consolidation is augmented by repeated learning following rest intervals, which is known as the spacing effect. Although the spacing effect has been associated with cumulative cellular responses in the neurons engaged in memory, this study reports the neural circuit-based mechanism for generating the spacing effect in the memory-related mushroom body (MB) parallel circuits in Drosophila. To investigate the neurons activated during the training, expression was monitored of phosphorylation of mitogen-activated protein kinase (MAPK), ERK [phosphorylation of extracellular signal-related kinase (pERK)]. In an olfactory spaced training paradigm, pERK expression in one of the parallel circuits, consisting of gammam neurons, was progressively inhibited via dopamine. This inhibition resulted in reduced pERK expression in a postsynaptic GABAergic neuron that, in turn, led to an increase in pERK expression in a dopaminergic neuron specifically in the later session during spaced training, suggesting that disinhibition of the dopaminergic neuron occurs during spaced training. The dopaminergic neuron was significant for gene expression in the different MB parallel circuits consisting of alpha/betas neurons for memory consolidation. These results suggest that the spacing effect-generating neurons and the neurons engaged in memory reside in the distinct MB parallel circuits and that the spacing effect can be a consequence of evolved neural circuit architecture (Awata, 2019).

Spaced learning, which consists of repeated learning with appropriate rest intervals, facilitates memory consolidation to a greater extent than repeated learning without rest. This augmentation of memory, known as the spacing effect, has been demonstrated in the animal kingdom. The central issue of this type of memory consolidation is how the neural circuit recognizes the temporally distributed same learning experience as spaced learning without recognizing each learning session as a novel experience and induce memory consolidation. Numerous studies have aimed to elucidate the mechanism by which the neurons recognize spaced learning through the cumulative cellular responses, such as the oscillatory activation of PKA and mitogen-activated protein kinase (MAPK). However, animals encounter various sensory stimuli in the natural environment, and it remains unclear how repeated experiences among intermingled stimuli are specifically subjected to memory consolidation. A recent study has identified the neural correlates of novelty and familiarity in the olfactory system of Drosophila, raising another possibility that the spacing effect may be produced by distinguishing the initial novel training experience from subsequent training experiences at the neural circuit level (Awata, 2019).

The spacing effect in Drosophila has been demonstrated using an aversive training paradigm in which an odor [the conditioned stimulus (CS)] is associated with electric shocks (the unconditioned stimulus). When flies are repeatedly subjected to aversive training with rest intervals, LTM formation occurs, depending on de novo gene expression. In contrast, single aversive training or repeated aversive training without rest intervals (massed training) does not induce LTM formation. Olfactory memory in flies is mediated by parallel circuits in the MB, each of which circuit consists of different types of neurons, including ~ 500 α/β surface (α/βs) neurons, 600 γmain (γm) neurons, and others (see The making of the Drosophila mushroom body and The neuronal architecture of the mushroom body provides a logic for associative learning). Given that retrieval of aversive LTM requires α/βs neurons, the spacing effect may target α/βs neurons for LTM formation. Importantly, MB axons are compartmentalized, and each compartment projects to a different single MB output neuron (MBON). Each MBON exhibits projections to different brain areas, some of which are known to innervate dopamine neurons (DANs) and form feedback loops with MB neurons. This layered structure linking the MB parallel circuits may be important for producing the spacing effect (Awata, 2019).

The present study explored the neural mechanisms underlying the spacing effect by focusing on the MB parallel circuits. The findings suggested that the reduced activity of the MB parallel circuit consisting of γm neurons is important for LTM formation, which affects the activity of the downstream MBON-DAN network. The results suggest that the spacing effect does not only solely depend on the cumulative cellular responses, but also relies on the neural circuit-based computation via the MB parallel circuits (Awata, 2019).

This study adopted an olfactory spaced training paradigm in Drosophila to investigate the neural circuit underlying the spacing effect. Advantage of immunohistochemistry by monitoring phosphorylation of MAPK (ERK), which allowed mapping the neurons activated in the normal training paradigm. Although an increase or decrease in pERK expression may result from either the change in the neural activation or of the ERK-signaling pathway, the optogenetic manipulation in this study suggested that the neural activity change in the MB-MBON-DAN network is significant in LTM formation. While previous studies have demonstrated that γm neurons are actively involved in memory formation, the present study suggests that a decrease in γm activation is also required for LTM formation. As a result, a single GABAergic neuron (MBON-γ1pedc) postsynaptic to γm neurons became inactivated, which, in turn, led to activation of a dopamine neuron (PPL1-α'2α2). The findings further revealed that the PPL1-α'2α2 neuron innervates another MB parallel circuit consisting of α/βs neurons to induce gene expression required for LTM. This study suggests the model in which the multistep linear circuit in the MB would be significant to index spaced learning of the environment. This neural circuit may act in concert with the cumulative cellular responses, such as the previously proposed oscillatory kinase activity during spaced learning. Dopamine-dependent synaptic suppression between MB neurons and MBON as previously demonstrated may also affect the MBON-DAN network (Awata, 2019).

PPL1-α'2α2 activation in the latter sessions of spaced training was required for gene expression in LTM formation. PPL1-α'2α2 activation was observed via calcium imaging during single training. However, increases in PPL1-α'2α2 activation during spaced training via MBON-γ1pedc inactivation may be necessary to provide sufficient signaling for inducing gene expression. Backward spaced training significantly increased pERK expression in the PPL1-α'2α2 neuron, although Arc2 mRNA was not induced, suggesting that association of an odor and electric shocks is also required for Arc2 expression. Consistently, although dTRPA1-dependent activation induced pERK in all α/βs neurons, artificial activation of the PPL1-α'2α2 neuron, and α/βs neurons induced Arc2 protein expression in only a few α/βs neurons, which would be the result of bypassing the requirement of the association due to the artificial activation. Thus, the multiple mechanisms for gene expression should be converged during spaced training, which include activation of the PPL1-α'2α2 neuron (spacing effect information), α/βs neurons (odor information), and other dopamine neurons (electric shock information). A previous study demonstrated that the cfos-expressing neurons show pERK expression upon memory retrieval. In contrast, this study never found pERK expression in the Arc2-expressing neurons upon retraining, memory retrieval, or reverse training. Accordingly, it was found that the pERK-expressing α/βs neurons were slightly reduced following spaced training, compared to single training. There are 2 possibilities. First, the neural activity of the Arc2-expressing neurons could be suppressed by spaced training. Given that synaptic depression between MBs and MBONs has been proposed as the neural correlates of memory, the decreased activity of the Arc2-expressing neurons may play an important role in LTM. Second, the Arc2-expressing neurons could undergo down-regulation in the ERK signaling, although the neurons are activated during memory retrieval. These should be examined in the future study to understand the physiological role of gene expression involved in LTM (Awata, 2019).

Previous studies have suggested that olfactory information relies on sparse coding in the parallel circuits of the MB, although the plasticity of these sparse codings has yet to be explored. In the present study, it was demonstrated that spaced learning preferentially targets sparse coding in the MB parallel circuit consisting of γm neurons via dopamine signaling, leading to memory consolidation in another MB parallel circuit consisting of α/βs neurons. Thus, the neurons responsible for generating the spacing effect and the neurons engaged in memory reside in the different MB parallel circuits. This neural circuit-based computation is accomplished by the MBON-DAN network linking these parallel circuits. This may be generalized to other types of sensory input in Drosophila and may provide insight into the neural representations within parallel neural circuits in other animals (Awata, 2019).

One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. This study offers an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). This study combined structural and activity data and, using a holistic normative framework based on similarity-matching, biologically plausible mechanistic models of the circuit were formulated. In particular, a linear circuit model is considered, for which an exact theoretical solution and a nonnegative circuit model were derived, which was examined through simulations. The latter largely predicts the ORN LN synaptic weights found in the connectome and demonstrates that they reflect correlations in ORN activity patterns. Furthermore, this model accounts for the relationship between ORN LN and LN-LN synaptic counts and the emergence of different LN types. Functionally, it is proposed that LNs encode soft cluster memberships of ORN activity, and partially whiten and normalize the stimulus representations in ORNs through inhibitory feedback. Such a synaptic organization could, in principle, autonomously arise through Hebbian plasticity and would allow the circuit to adapt to different environments in an unsupervised manner. This study thus uncovered a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient. Finally, this study provides a unified framework for relating structure, activity, function, and learning in neural circuits and supports the conjecture that similarity-matching shapes the transformation of neural representations (Chapochnikov, 2023).

Combining the Drosophila larva olfactory circuit connectome, ORN activity data, and a normative model, this study has advanced understanding of sensory computation and adaptation, quantitatively link ORN activity statistics, functional data, and connectome, and makes testable predictions. A canonical circuit model capable of autonomously adapting to different environments is revealed, while maintaining the critical computations of partial whitening (decorrelation) and feature extraction. Such a circuit architecture may arise in other brain areas and may be applicable in machine learning and signal processing. Using ORN activity patterns as input, this normative framework accounts for the biological circuit structural organization and identifies in the connectome signatures of circuit function and adaptation to ORN activity. Such an approach offers a general framework to understand circuit computation and could provide valuable insights into more neural circuits, whose structural and activity data become available (Chapochnikov, 2023).

This paper compares the structural predictions of the normative approach to the connectome. The Neural Network Collection (NNC) model, when adapted to the ORN activity dataset, accounts for key structural characteristics, for example, the ORNs -> LN connection weight vectors. This study asked two questions: 1) Why does the strong resemblance between model and data arise, when the available odor dataset most probably imperfectly matches the true larva odor environment? 2) Why isn't the resemblance even greater, and could the imperfect fit suggest that the model inadequately explains the biological circuit (Chapochnikov, 2023)?

For 1), a possibility is that generic correlations between ORNs arise in large enough ORN activity datasets, causing robust features in the model connectivity. These correlations could result from the intrinsic chemical properties of ORN receptors. Odor statistics would also influence the connection weights, but to a lesser degree. Thus, a more naturalist activity dataset could further improve model predictions (Chapochnikov, 2023).

For 2), first, due to intrinsic noise and variability, no model could be 100% accurate in predicting connectivity. In fact, variability in synaptic count and innervation arises for Drosophilas raised in similar environments, indicating potential 'imprecision' of development and/or learning. Variability was also observed in the left vs. right side connectivity. Second, incomplete ORN activity statistics may decrease prediction accuracy. Third, synaptic count might not exactly reflect synaptic strength. Finally, this model being a simplification of reality misses additional factors shaping circuit connectivity (Chapochnikov, 2023).

This analysis indicates that the matches between model and data likely do not result from chance only, suggesting that the similarity-matching principle influences circuit organization. However, the unsupervised approach assumes that no odor is 'special' for the animal, and thus LNs in the circuit model cluster odors solely based on their representations in the ORN activity space. This contrasts with the biological ORN-LN circuit, where LNs such as Keystone and Picky 0 have specific downstream connections likely related to survival needs and different hardwired animal behaviors, requiring them to detect particular odors. Consequently, the connectivity of such LNs might contribute to the imperfect one-to-one correspondence between the model and the connectome (Chapochnikov, 2023).

The circuit model can learn the optimal connection weights autonomously via Hebbian learning, offering the capacity to adapt to different environments. Studies in adult Drosophila reveal that glomeruli sizes (and thus ORN-LN or ORN-PN synaptic weights) or activity depend on the environment in which the Drosophila grew up. It is, however, unknown if activity-dependent plasticity also occurs in the larval ORN-LN circuit and whether the observed synaptic counts are a result of such plasticity. If present, it is unclear whether the short 6-h life of the larva from which the connectome was reconstructed allows substantial learning to occur and whether changes in synaptic weights would translate to different synaptic counts (Chapochnikov, 2023).

Resolving connectomes of larvae raised in different odor environments and at different times of their life, probing synaptic plasticity, and recording ORN responses to the full odor ensemble present in its environment would help clarify the influence of noise, plasticity, and genetics in circuit shaping (Chapochnikov, 2023).

LNs form a significant part of the neural populations in the brain, perform diverse computational functions, and exhibit extremely varied morphologies and excitabilities. This study proposes a dual role for LNs in this olfactory circuit: altering the odor representation in ORNs and extracting ORN activity features, available for downstream use. In the olfactory system of Drosophila and zebrafish, LNs perform multiple computations, such as gain control, normalization of odor representations, and pattern and channel decorrelation, which is consistent with the current results. Also, in Drosophila the LN population expands the temporal bandwidth of synaptic transmission and temporally tunes PN responses, which was not addressed in this study (Chapochnikov, 2023).

In topographically organized circuits, such as in the visual periphery or in the auditory cortex, distinct LN types uniformly tile the topographic space, and each LN type extracts a specific feature of the input, e.g., in the retina. In nontopographically organized networks, however, the organization and role of LNs remains a matter of research and controversy. This study examined a subcircuit with four LN types, and most types contain several similarly connected LNs. What is the function of multiple similar LNs in the ORN-LN circuit, as also observed in the NNC? First, LNs might differentiate further as the larva grows. Second, several LNs might help expand the dynamic range of a single LN. What are the features extracted by LNs in the Drosophila larva? The NNC model and the distinct connectivity patterns of LN types in the connectome, suggest that different LN types are activated in response to different sets of odors. The extracted features might relate to clusters in ORN activity and to prewired, animal-relevant odors. Since several ORNs --> LN connection weight vectors {𝐰𝑘} in the NNC model resemble those in the biological circuit, the odor clusters identified by the model likely correspond to the set of odors that activate LNs in the biological circuit. The feedforward synaptic count vector from ORNs to the Broad Trio 𝐰BT, which aligns with the first PCA direction of ORN activity and with an ORNs --> LN connection weight vector {𝐰k}in the NNC model could potentially encode the mean ORN activity and thus be related to the global odor concentration. Other LNs might encode features of odors, such as aromatic vs. long-chain alcohols, or specific information influencing larva behavior, but more experiments are required to definitely resolve the features. While the current conclusions differ from a study that found that LN activation is invariant to odor identity), that study imaged several LNs simultaneously and might thus have missed the selectivity of individual LNs (Chapochnikov, 2023).

The connectome reveals LN-LN connections, which are propose play a key role in clustering and shaping the odor representation, and are co-organized with thed ORN-LN connections. A role of LN-LN connections and their relationship to ORN-LN connections is relatively unexplored (Chapochnikov, 2023).

In summary, this study emphasizes the importance of the different ORN-LN and LN-LN connection strengths and argues that LNs are minutely selective and organized to extract features and render the representation of odors more efficient. It is proposed that the circuit's effect on the neural representation of odors in ORNs corresponds to partial ZCA-whitening and divisive normalization. Such computations, which reduce correlations originating from the sensory system and the environment, have appeared in efficient coding and redundancy reduction theories. Partial whitening is in fact a solution to mutual information maximization in the presence of input noise. In this circuit too, complete whitening might also not be desirable due to potential noise amplification. Thus, keeping low-variance signal directions of the input unchanged and dampening larger ones is consistent with mutual information maximization. These conclusions are in line with reports of pattern decorrelation and/or whitening in the olfactory system in zebrafish and mice (Chapochnikov, 2023 and references therein).

The computation in this model also resembles divisive normalization, an ubiquitous computation in the brain, proposed for the analogous circuit in the adult Drosophila. Divisive normalization captures two effects of neuronal and circuit computation: 1) neural response saturation with increasing input up to a maximum spiking rate σ, arising from the neuron's biophysical properties; 2) dampening of the response of a given neuron when other neurons also receive input, often due to lateral inhibition. Aspect (1) is absent in the curren model but could be implemented with a saturating nonlinearity. Depending on the biological value of the maximum output, this model might not accurately capture responses for high-magnitude inputs. However, signatures of (2) are evident in the saturation of the activity pattern magnitudes in ORN axons for increasing ORN soma activity pattern magnitudes. Activity patterns of large magnitude correspond to activity at higher odor concentrations and with a high number of active ORNs. Because such input directions are more statistically significant in the dataset, these stimuli are more strongly dampened by LNs (which encode such directions) than those with few ORNs active. Thus, the model presents a possible linear implementation of a crucial aspect of divisive normalization, which in itself is a nonlinear operation (Chapochnikov, 2023).

Although the basic form of divisive normalization performs channel decorrelation, and not activity pattern decorrelation, models in this paper perform both channel and pattern decorrelation. Nevertheless, a modified version of divisive normalization, which includes different coefficients for the driving inputs in the denominator, performs pattern decorrelation too, as the circuit model. The proposed neural implementations of divisive normalization usually require multiplication by the feedback, which might not be as biologically realistic as circuit implementation described in this study. Several neural architectures similar to the current have been proposed to learn to decorrelate channels, perform normalization, or learn sparse representations in an unsupervised manne. However, these studies either lack a normative/optimization approach or have a different circuit architecture or synaptic learning rules. Using a normative approach has the advantage of directly investigating the underlying principles of neural functioning and also potentially providing a mathematically tractable understanding of the circuit structure and function (Chapochnikov, 2023).

This study complements machine learning approaches to understand neural circuit organization. These approaches use supervised learning and backpropagation to train an artificial neural network to perform tasks such as odor or visual classification. In the olfactory system, circuit configurations arising from this optimization, which could mimic the evolutionary process, display many connectivity features found in biology. Unlike these approaches, a general principle is proposed governing the transformation of neural representations, similarity-matching, and also a mechanism to learn autonomously during the animal's lifetime (Chapochnikov, 2023).

One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. This study offers an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). This study combined structural and activity data and, using a holistic normative framework based on similarity-matching, biologically plausible mechanistic models of the circuit were formulated. In particular, a linear circuit model is considered, for which an exact theoretical solution and a nonnegative circuit model were derived, which was examined through simulations. The latter largely predicts the ORN LN synaptic weights found in the connectome and demonstrates that they reflect correlations in ORN activity patterns. Furthermore, this model accounts for the relationship between ORN LN and LN-LN synaptic counts and the emergence of different LN types. Functionally, it is proposed that LNs encode soft cluster memberships of ORN activity, and partially whiten and normalize the stimulus representations in ORNs through inhibitory feedback. Such a synaptic organization could, in principle, autonomously arise through Hebbian plasticity and would allow the circuit to adapt to different environments in an unsupervised manner. This study thus uncovered a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient. Finally, this study provides a unified framework for relating structure, activity, function, and learning in neural circuits and supports the conjecture that similarity-matching shapes the transformation of neural representations (Chapochnikov, 2023).

Complex behaviors depend on the precise developmental specification of neuronal circuits, but the relationship between genetic programs for neural development, circuit structure, and behavioral output is often unclear. The central complex (CX) is a conserved sensory-motor integration center in insects that governs many higher order behaviors and largely derives from a small number of Type II neural stem cells. This study shows that Imp, a conserved IGF-II mRNA-binding protein expressed in Type II neural stem cells, specifies components of CX olfactory navigation circuitry. This study shows: (1) that multiple components of olfactory navigation circuitry arise from Type II neural stem cells and manipulating Imp expression in Type II neural stem cells alters the number and morphology of many of these circuit elements, with the most potent effects on neurons targeting the ventral layers of the fan-shaped body. (2) Imp regulates the specification of Tachykinin expressing ventral fan-shaped body input neurons. (3) Imp in Type II neural stem cells alters the morphology of the CX neuropil structures. (4) Loss of Imp in Type II neural stem cells abolishes upwind orientation to attractive odor while leaving locomotion and odor-evoked regulation of movement intact. Taken together, this work establishes that a single temporally expressed gene can regulate the expression of a complex behavior through the developmental specification of multiple circuit components and provides a first step towards a developmental dissection of the CX and its roles in behavior (Hamid, 2023).

Neural circuit assembly features simultaneous targeting of numerous neuronal processes from constituent neuron types, yet the dynamics is poorly understood. This study used the Drosophila olfactory circuit to investigate dynamic cellular processes by which olfactory receptor neurons (ORNs) target axons precisely to specific glomeruli in the ipsi- and contralateral antennal lobes. Time-lapse imaging of individual axons from 30 ORN types revealed a rich diversity in extension speed, innervation timing, and ipsilateral branch locations and identified that ipsilateral targeting occurs via stabilization of transient interstitial branches. Fast imaging using adaptive optics-corrected lattice light-sheet microscopy showed that upon approaching target, many ORN types exhibiting 'exploring branches' consisted of parallel microtubule-based terminal branches emanating from an F-actin-rich hub. Antennal nerve ablations uncovered essential roles for bilateral axons in contralateral target selection and for ORN axons to facilitate dendritic refinement of postsynaptic partner neurons. Altogether, these observations provide cellular bases for wiring specificity establishment (Li, 2021).

Prior to this study, it was unclear what cellular mechanism is used for ipsilateral target selection. The data support the following model: ORN axons send out transient interstitial branches at multiple locations along the main axon; the branch that reaches the target region becomes stabilized, and further interstitial branches are suppressed. Stabilization of appropriately positioned branches and elimination of ectopic branches are also used for topographic retinotopic targeting, suggesting that the mechanism of transient interstitial branching followed by stabilization applies to the formation of both continuous and discrete neural maps (Li, 2021).

The exploring branches discovered using AO-LLSM imaging suggest a means by which a growing ORN axon may increase the chance of identifying its target. These exploring branches consist of long, microtubule-based parallel branches that extend and retract rapidly and independently, allowing them to sample a relatively large region for possible targets. The transient occurrence of exploring branches when ORN axons approach their target region suggests that they are induced by local cues near target regions to facilitate target selection. In the ipsilateral antennal lobe, exploring branches were found in ORN types that form ipsilateral branches shortly after the main axon passes by, consistent with them serving as the precursor to the eventual ipsilateral branch. Exploring branches are also found in axon terminals in the contralateral antennal lobe in ORN types, suggesting a general role in facilitating contralateral target identification (Li, 2021).

For ORN types that have a long delay in extending the ipsilateral branch, exploring branches were not observed, suggesting a distinct mechanism for consolidating the ipsilateral branch. Nevertheless, dynamic interstitial branches occur over a prolong period of time until the formation of the ipsilateral branch, suggesting that these ORN types also use stabilization of transient interstitial branches as a means to consolidate the ipsilateral branch (Li, 2021).

In summary, after the initial trajectory choice such that ORN axons navigate in the half of the antennal lobe where their eventual targets are, it is proposed that the next critical step in ORN axon development is the stabilization of transient interstitial branches by target-derived cues, aided at least in part by the exploring branches. Together, these cellular mechanisms begin to explain how each ORN chooses one of 50 glomerular targets precisely (Li, 2021).

A surprising finding is that the cytoskeletal organization of ORN terminals differs substantially from that of classic growth cones, comprising F-actin-based filopodia and lamellipodia at the periphery and a microtubule-enriched central hub. The terminal branches of ORN axons, in particular the exploring branches, are filled with microtubules, whereas F-actin is concentrated at the central hub. Similar cytoskeletal organization were also found in photoreceptor axon terminals. These differences are unlikely due to species difference, as the classic growth cone cytoskeletal organization is found in neurons (mostly dissociated in culture) from Aplysia, Drosophila, and mammals. It cannot be ruled out that F-actin is present in low amount at the terminal of each exploring or post-innervation branch but is beyond the detection limit of utrophin-based F-actin labeling; if so, each terminal branch would have its own growth cone at its tip, resembling classic growth cones. Even if that is the case, ORN axon terminals still differ from classic growth cones by having multiple microtubule-based parallel branches emanating from an F-actin rich central hub. Indeed, EB1-GFP puncta can be found at the tip of the branch, suggesting that microtubules can fill the entire branch. Microtubule polymerization has been shown to mediate membrane extension directly in lipid vesicle (Li, 2021).

It is suspected that the deviation of cytoskeletal organization in ORN axon terminals from the classic growth cone is likely due to the more complex environments axon terminals need to explore in the brain compared with the primary culture. Indeed, a recent study showed that neurons cultured in three-dimensional environments have microtubules extending to the edge of growth cones unconstrained by F-actin. The current findings have important implications for mechanisms that convert cell-surface recognition of extracellular cues into cytoskeletal-based structural changes in axon terminals during axon targeting. Specifically, it is suggested that signaling to microtubule is particularly important at initial stages of target selection (Li, 2021).

Bilaterally symmetric organization of the nervous system is a cardinal feature of all bilaterians. Unilateral antennal nerve severing indicates the requirement of bilateral axons in target selection. The simplest cellular mechanism is direct interactions between ipsilateral and contralateral ORN axons. These interactions may facilitate midline crossing by creating a critical mass of midline-penetrating axons, disruption of which may cause some axons to leave the antennal lobe instead. Later, bilateral axon-axon interactions between the same ORN type may facilitate target selection of contralateral ORNs. The data does not rule out the possibility that bilateral interactions may be indirect; for example, ipsilateral ORNs may change the properties of their partners PNs, which in turn regulate target selection of contralateral ORNs. Indeed, upon unilateral antennal nerve severing, targeting defects was mostly found in ORN types that sequentially innervate ipsilateral and contralateral glomeruli. The ease of severing antennal nerve in explant cultures provides a means to further investigate cellular and molecular mechanisms of bilateral interactions (Li, 2021).

In conclusion, time-lapse imaging has greatly enriched understanding of the cellular events that enable the step-wise assembly of the fly olfactory circuit, and highlight the precise genetic control of multiple steps during ORN axon targeting. These include the choice of a trajectory along which an ORN axon navigates the ipsilateral antennal lobe, the timing and location of stabilizing its ipsilateral branch, and the interactions with contralateral ORN axons to cross the midline and innervate its contralateral target. Finally, ORN axons also help refine dendrites of their partner PNs, which pattern the antennal lobe first. The stage is set to combine live imaging and the cellular insights it has brought with genetic manipulations of key wiring molecules identified by genetic, transcriptomic, and proteomic approaches to reach a deeper level of mechanistic understanding of the circuit assembly process (Li, 2021).

While the targeting precision in explant culture mimics closely in vivo, it takes ORN axons longer to reach the same developmental stage in culture than in vivo. Thus, measurements involving time in explants may be protractions of equivalent events in vivo. The small number of single axons from specific ORN types, due to limited drivers that label specific ORN types strongly in early development, did not allow assessment of the variation of targeting behavior among ORNs of the same type. While this study sampled axon targeting of a large fraction of antennal ORN types, axons from 6 maxillary palp ORN types were not sampled as the explant did not include maxillary palp. It is unclear whether maxillary palp ORN axons follow similar rules as antennal ORN axons. However, as maxillary palp ORN axons reach the antennal lobe substantially later than antennal ORN axons, the lack of maxillary palp ORN axons in explants should not affect the early stages of antennal ORN axon targeting (Li, 2021).

Understanding neuronal representations of odor-evoked activities and their progressive transformation from the sensory level to higher brain centers features one of the major aims in olfactory neuroscience. This study investigated how odor information is transformed and represented in higher-order neurons of the lateral horn, one of the higher olfactory centers implicated in determining innate behavior, using Drosophila melanogaster. This study focused on a subset of third-order glutamatergic lateral horn neurons (LHNs) and characterized their odor coding properties in relation to their presynaptic partner neurons, the projection neurons (PNs), by two-photon functional imaging. Odors evoke reproducible, stereotypic, and odor-specific response patterns in LHNs. Notably, odor-evoked responses in these neurons are valence-specific in a way that their response amplitude is positively correlated with innate odor preferences. It is postulated that this valence-specific activity is the result of integrating inputs from multiple olfactory channels through second-order neurons. GRASP and micro-lesioning experiments provide evidence that glutamatergic LHNs obtain their major excitatory input from uniglomerular PNs, while they receive an odor-specific inhibition through inhibitory multiglomerular PNs. In summary, this study indicates that odor representations in glutamatergic LHNs encode hedonic valence and odor identity and primarily retain the odor coding properties of second-order neurons (Das Chakraborty, 2022).

Insects are the most successful taxon among the whole animal kingdom in terms of their distribution and ability to survive in a multitude of environmental conditions. Largely they rely on their olfactory sense to carry out their fundamental goal-directed behaviors, such as food navigation, mating, ovipositing, or escape from predators. The powerful ability to detect odor cues, to evaluate the information efficiently with a relatively small number of neurons and to transform the neuronal signal into an appropriate behavioral output, makes the insect olfactory system a premier model system for olfactory research. Numerous studies have investigated the neuronal representation of odors at successive neuronal layers from the periphery to higher brain levels using Drosophila melanogaster as a model organism. Although much progress has been made in understanding odor coding at the antennal lobe (AL) level, the coding strategies and processing mechanisms of higher brain centers still remain largely elusive. In this regard, the lateral horn (LH) has recently gained attention as a crucial signal processing center integrating both innate and learned behavioral information. Several studies during recent years have advanced understanding of the anatomical and functional properties of higher-order lateral horn neurons (LHNs) regarding odor processing. The generation of several LH cell-type-specific lines, characterization of polarity and neurotransmitter identity of LHNs, as well as the establishment of detailed EM connectomic datasets have led to a significant progress in the field to study the function of specific LHN classes. The LH is comprised of three categories of neurons, which include LH input neurons (LHINs, which are mainly olfactory projection neurons [PNs] along with mechanosensory, thermosensory, and gustatory neurons), LH local neurons (LHLNs), and LH output neurons (LHONs). In terms of PN-LHN connectivity, the olfactory PNs deriving from individual glomeruli of the AL form stereotyped and conserved connections with certain LHNs. Although all kinds of connections are possible, PNs having similar odor-tuning patterns are prone to target similar LHN types. Certain pairs of narrowly tuned glomeruli encoding ecologically relevant odors and eliciting specific kinds of behavior, such as courtship, aggregation, or food seeking, converge onto the same LHN types and have been shown to be overrepresented in the LH in terms of synaptic densities. Furthermore, a high amount of divergence has also been described to occur at the level of PN to LHN connectivity. Altogether, these complex connectivity patterns, in addition to direct pooling of feedforward inputs from PNs innervating different glomeruli, result in broader tuning patterns of LHNs compared to their presynaptic PNs. In addition to the observed broadly tuned LHNs, narrowly tuned LHNs also exist, which receive input from a single type of PN and which are assumed to be further modulated by odorant-selective inhibition through inhibitory neurons. Although several studies agree that odors are compartmentalized in the LH based on either their chemical identity, behavioral significance, or hedonic valence, it still remains controversial how the odor information is transformed from the PN to the LHN level and which odor features are coded by subtypes of LHNs (Das Chakraborty, 2022).

This study aimed to elucidate the odor coding and processing strategies of LHNs by investigating how a neuronal subset of particular neurotransmitter identity encodes different odor features in the LH and how this representation is correlated to their presynaptic partner neurons in the AL, the uniglomerular PNs (uPNs) and multiglomerular PNs (mPNs). Using photoactivatable GFP, diverse clusters of LHNs were first identified based on their different neurotransmitter identities, and the detailed analysis further focused exclusively on glutamatergic LHNs. Using in vivo two-photon functional imaging, several aspects of odor-evoked activity were characterized in these neurons, such as odor-specific response patterns, reproducibility of repeated stimulations, as well as stereotypy across different individuals. It was possible to successfully demonstrate that attractive and aversive odors are clearly segregated and that the response amplitudes of glutamatergic LHNs are positively correlated with the innate behavioral preference to an odor. How the excitatory input from uPNs and odor-specific inhibition from mPNs contribute to the fine-tuning of odor-specific response patterns of LHNs was further dissected. Altogether, this study demonstrates a significant role of glutamatergic LHNs regarding olfactory processing and extends knowledge about the transformation processes of neuronal information taking place from the periphery to higher brain levels, such as the LH (Das Chakraborty, 2022).

This study functionally characterizes a subset of glutamatergic higher-order neurons in the LH regarding odor coding and processing. It was demonstrated that glutamatergic LHNs respond in a reproducible, stereotypic, and odor-specific manner and these response properties emerge at the level of presynaptic uPNs. Notably, the differential activity levels of glutamatergic LHNs to attractive and aversive odors are positively correlated to the olfactory behavioral preference, indicating that these neurons are mainly tuned to attractive odors. The response features do not arise from the OSN level, but rather derive from local processing within the LH by integrating inputs from multiple olfactory channels through uPNs, which also show a valence-specific odor representation. Furthermore, laser transection experiments demonstrate that these higher-order neurons receive their major excitatory input from uPNs and an odor-specific inhibitory input from mPNs. Lastly, the data show that the observed mPN-mediated inhibition seems to be required for generating an odor-specific response map in the LH (Das Chakraborty, 2022).

A growing body of evidence suggests the existence of an anatomical and functional stereotypy in early processing centers of the insect olfactory pathway. This stereotypy becomes obvious first at the sensory neuron (OSN) level, where OSNs expressing a certain OR target and converge on a stereotypic glomerulus, resulting in a conserved spatial map in the AL between different individuals. This anatomical stereotypy was shown to be retained at the postsynaptic PN level (Das Chakraborty, 2022).

Several studies support the notion that an anatomical stereotypy might also be present at the level of the LH, particularly shown for the PN to LHN connectivity. Along this line, functional studies have demonstrated that LHNs respond in a reproducible and stereotyped manner to odors and this stereotypy is a general feature of the LH. However, how an ensemble of LHNs integrates inputs from several olfactory channels, that is, the presynaptic excitatory and inhibitory PNs, and whether each odor induces a specific and stereotyped response pattern in the LH was not clearly addressed before. In this study, it was demonstrated that each odor is represented by an odor-specific activity pattern in the LH, while the examined glutamatergic LHNs display broader tuning patterns than their presynaptic partner neurons. Although it has been assumed previously that odor specificity may not be encoded in higher-order brain centers, the findings are in accordance with a a recent study, that demonstrated the existence of 33 different LH cell types exhibiting stereotypic odor response properties with increased tuning breadth than PNs. The observed odor-evoked response features of LHNs that are odor-specific but with a broader tuning breadth could be due to several reasons. First, they receive input from similarly as well as differently tuned uPNs; therefore, the topographic map of uPN axonal terminals is not clearly retained at the level of LHNs. Second, LHNs integrate inputs from multiple odor channels, for example, one LHN receives on average excitatory input from ~5.2-6.2 glomeruli. Third, both uPNs and mPNs provide input to glutamatergic LHNs and those LHNs in turn feedback onto those second-order neurons and provide feedforward information to other LHNs. This study observed that uPNs are more efficient in encoding odor identity than the glutamatergic LHNs in the LH, whereas LHNs reveal an improved categorization of odors either based on behavioral significance, 'odor scene' or chemical group. However, the distinct odor-specific response map by glutamatergic LHNs observed in this study suggests that the dimensionality of odor features might not get reduced but still retains information about the odor specificity at the third-order processing stage (Das Chakraborty, 2022).

The functional imaging recordings revealed that odor valence is encoded by glutamatergic LHNs, leading to different activation strengths and patterns for attractive and aversive odors in the LH. Although the valence code is already present at the AL level in uPNs, it could be assumed that the same valence code might be translated to the next level of higher-order neurons. However, evidence of high convergence and divergence of neurons from different sensory modalities in the LH argues against a simple translation of the valence code or odor identity to the LHN level. This observation is well in line with previous studies that have revealed that odor-evoked responses in higher brain centers are generally categorized according to certain odor features as already mentioned above. For example, using functional imaging or patch-clamp recordings of second-order olfactory neurons revealed the existence of distinct attractive and aversive odor response domains in the LH formed by uniglomerular and multiglomular projection neurons (uPNs and mPNs). Such a categorization according to hedonic valence is also visible in this study when the odor-evoked responses of glutamatergic LHNs were plotted in a PCA, taking into account the spatial response patterns as well as the intensity of activity. Although no prominent spatial domain of attractive or aversive odors was evident in the recordings, attractive odors evoked a generally stronger activity when compared to aversive odors in this subset of LHNs. A similar trend is noted in second-order uPNs. However, their response strength was neither correlated with the olfactory preference determined in behavior nor the odor response properties of LHNs. The observed significant correlation between the amount of odor-evoked activity in glutamatergic LHNs to the behavioral valence of an odor leads to a postulate that the activity strengths of higher-order olfactory neurons to odor stimulation might determine the behavioral response - an assumption that needs to be tested in future studies (Das Chakraborty, 2022).

Although this study has documented how the glutamatergic LHNs determine the innate behavioral valence, odor valence in the LH can also be achieved through learning and LHNs have also been shown to play a critical role for learned behavior. A specific class of LHNs (so-called PD2a1/b1) has been reported to mediate innate approach response in addition to learned avoidance response in an odor concentration-dependent manner. Therefore, depending upon the context, the same LHNs can mediate innate as well as learned behavior with opposing valence. Extensive interconnections between the two higher brain centers, the MB and the LH through MBONs and LHONs, signify how the MB modulates the innate olfactory pathways and the valence code in the LH (Das Chakraborty, 2022).

This study suggests that glutamatergic LHNs use different strategies to extract different features of odor information, (1) conserving the identity of an olfactory stimulus by forming an odor-specific activity map and (2) encoding the valence of an odor by integrating information from multiple olfactory channels. It is an ongoing debate regarding how neurons in the LH evaluate an odor stimulus. Gradual detection, encoding, and categorization of an odor at different olfactory processing levels can result in a simple binary choice or complex behavioral responses of an animal. The behavioral preferences are simply reflected in either to approach (positive) or to leave (negative), to copulate (positive) or to reject (negative), and to oviposit (positive) or to find another suitable oviposition site (negative) depending upon the behavioral assays. Hence, based on the context or ecological relevance, an odor can be evaluated either as 'pleasant' or 'unpleasant,' which is well reflected by the response properties of glutamatergic LHNs regarding their valence specificity and their correlation between response strength and behavioral odor preference (Das Chakraborty, 2022).

Notably, such a correlation between response intensity and behavioral preference has also been observed in previous studies, where the amplitude of food odor-evoked activity in neuropeptide F (dNPF) neurons was found to strongly correlate with food odor attractiveness. Another study that combined functional imaging with tracking of innate behavioral responses revealed that the behavioral output could be accurately predicted by a model summing up the normalized glomerular responses, in which each glomerulus contributes a small but specific part to the resulting odor preference\. At the level of the LH, LHNs then integrate the olfactory information from the glomerular responses conveyed via uPNs and mPNs. In a previous study, it was demonstrated that mPNs respond differently to attractive and aversive odors, and mediate behavioral attraction. In this context, this study complements the previous finding by showing that also uPNs display distinct valence-specific responses in the LH to attractive and aversive odors. Information from these two PN pathways becomes integrated and processed in the LH, resulting in valence-specific activities in glutamatergic LHNs, which may in turn determine the relative behavioral preference (Das Chakraborty, 2022).

mPNs inhibit the glutamatergic LHNs in an odor-selective manner, leading to an odor-specific response pattern. According to observations of this study, glutamatergic LHNs receive a stronger inhibition from mPNs in response to the odors vinegar and acetophenone than to benzaldehyde, whereas other odors, such as 2,3 butanedione, linalool, and ethyl acetate, seem not to induce an inhibition. In the absence of this inhibition, it is noted that in addition to an increased response amplitude and altered odor representation, the activity patterns of different odors became more strongly correlated and hence more similar. It is therefore conclude that the mPN-mediated selective inhibition on this glutamatergic subset of LHNs is necessary to maintain odor specificity. Along this line, a previous study has reported that mPNs provide an odor-selective input to vlpr neurons, another class of third-order LHNs. This odor-specific modulation depends on the nature of the odor and results from the stereotyped connectivity of mPNs in the AL as well as in the LH. Although this study provides evidence that uPNs are not presynaptically inhibited by mPNs, another study established that mPNs indeed inhibit uPNs in the LH, facilitating odor discrimination. In addition to uPNs and vlpr neurons, this study identifies another class of recipient neurons (glutamatergic LHNs) that receives mPN-mediated odorant-selective inhibition (Das Chakraborty, 2022).

The glutamatergic LHN population in this study comprises glutamatergic LHONs as well as LHLNs since a broad line was used that labels an ensemble of all glutamatergic LHNs. A previous study employed specific split-Gal4 lines to selectively label LHINs, LHLNs, and LHONs with different neurotransmitter identities and analyzed their connectivity using EM connectomics. By employing artificial activation of specific subsets of LHNs via CsChrimson, it was demonstrated that one class of glutamatergic LHLNs (so-called PV4a1:5) forms excitatory synapses with AV1a1 LHONs that mediate aversive behavioral responses. In the current study, since a generic Gal4-line was used to label all glutamatergic LHNs, this study could neither activate nor silence specific neuronal subsets to observe their relevance with regard to odor-guided behavior. However, when the odor response strength of LHNs was correlated to the olfactory preference, these neurons were observed to be mainly tuned to attractive odors, suggesting that they are involved in mediating approach behavior. Although it was not possible to clearly differentiate the functional properties between different populations of LHONs and LHLNs, this study provides the first understanding of how odors are integrated, transformed, and finally represented in the LH by an ensemble of glutamatergic LHNs (Das Chakraborty, 2022).

Intriguingly, the neurotransmitter identity of this class of LHNs opens up another interesting aspect: Knowing that glutamate can act as an excitatory or inhibitory neurotransmitter, as well as a coincident detector, depending upon the receptors present in the postsynaptic neurons, further experiments are needed to reveal the consequences of glutamatergic LHN input onto their postsynaptic partner neurons. Certainly, the presence of an impressive amount of vesicular glutamate in the LH points towards a significant role of glutamatergic LHNs with regard to odor coding and processing at this higher brain center (Das Chakraborty, 2022).

To identify and memorize discrete but similar environmental inputs, the brain needs to distinguish between subtle differences of activity patterns in defined neuronal populations. The Kenyon cells (KCs) of the Drosophila adult mushroom body (MB) respond sparsely to complex olfactory input, a property that is thought to support stimuli discrimination in the MB. To understand how this property emerges, the role was explored of the inhibitory anterior paired lateral (APL) neuron in the input circuit of the MB, the calyx. Within the calyx, presynaptic boutons of projection neurons (PNs) form large synaptic microglomeruli (MGs) with dendrites of postsynaptic KCs. Combining electron microscopy (EM) data analysis and in vivo calcium imaging, it was shown that APL, via inhibitory and reciprocal synapses targeting both PN boutons and KC dendrites, normalizes odour-evoked representations in MGs of the calyx. APL response scales with the PN input strength and is regionalized around PN input distribution. These data indicate that the formation of a sparse code by the KCs requires APL-driven normalization of their MG postsynaptic responses. This work provides experimental insights on how inhibition shapes sensory information representation in a higher brain centre, thereby supporting stimuli discrimination and allowing for efficient associative memory formation (Prisco, 2021).

While the importance of inhibition in reducing the overlap among stimuli representation has been postulated many decades ago and supported by more recent experimental evidence, the complete mechanism by which inhibition supports stimuli discrimination is not fully understood yet. This study shows that the inhibitory APL neuron, by participating in the structure of MGs of the Drosophila MB calyx, provides inhibition scaled to the PNs excitatory inputs to the calyx. As a result, the average strength and the distribution of postsynaptic responses in KC dendritic claws become more similar across different odour representations. It is suggested that this normalization of postsynaptic responses operated by APL is at the core of pattern separation in the MB (Prisco, 2021).

Pattern separation is obtained in the MB through the formation of a sparse response in the KC layer. The decoding of a sparse code, in general, increases the storage capacity of associative networks, thereby supporting learning and classification tasks. In fact, sparse neuronal representations are described in several organisms including mammals, songbirds, and insects. APL was reported to play a key role in maintaining KCs responses sparse, but the underlying mechanism was far from understood. KCs receive inputs from six to eight PNs on average and, due to KCs high firing threshold, require more than half of those inputs to be coactive to spike. The current data suggest that the APL neuron, by confining KC claws responses within a certain range of activation, ensures that KCs requirement of multiple coactive claws is respected even in the presence of highly variable input strengths. In other words, APL inhibition makes KC input integration dependent on the combinatorial pattern of inputs rather than on the strength of individual inputs. In support of this, blocking APL leads to an increased correspondence between input strength and KC response. Of notice, odour discrimination is achieved at multiple levels of the Drosophila olfactory pathway by different types of inhibitory neurons. Indeed, input gain control normalization has been described for GABAergic interneurons in the AL as well as for inhibitory iPNs at the lateral horn. Additionally, APL and its homolog GGN in the locust showed increased depolarization in response to increasing odour concentration. This, combined with the current findings, suggests that the normalization performed by APL might be acting not only across stimuli identities, but also among concentrations of the same stimulus (Prisco, 2021).

Structural and functional data point towards the involvement of APL in a feedforward loop from PN boutons to KC claws, as well as a closed feedback loop with PN boutons. An advantage of using recurrent circuits to provide inhibition is that such a system can deal with a wide range of input strength, as inhibition and excitation strengths are proportional. Indeed, EM analysis revealed both pre- and postsynaptic connection between APL and PN boutons, linearly proportional to each other, and the differences in the APL calcium influx in response to odours correlated to the variability measured in PNs. So far, APL has been mainly described as a feedback neuron for KCs. However, feedforward inhibitory neurons from the input population onto the next layer have been described in other neuronal networks performing pattern separation. For example, granule cells receive both feedforward and feedback inhibition from Golgi cells at the cerebellar cortex, which are driven by excitatory inputs from the mossy fibers and granule cells' axons, respectively. Moreover, it has recently been demonstrated that Golgi cells recruit scales with the mossy fibers input density, similarly to what was observed in the functional imaging experiments carried out in this study. Additionally, adaptive regulation of KCs sparseness by feedforward inhibition has already been theorized in realistic computational models of insect's MBs. Regarding KCs-to-APL connections, a positive linearity was found among pre- and postsynapses between these two cells, confirming the presence of a local feedback loop within KC dendrites and the APL at the calyx. Furthermore, it has been reported that α/β KCs receive more inhibitory synapses along their dendritic trees compared to γ and α'/β', where the majority of synapses received from the APL is localized on KC claws instead. As the ability of inhibitory synapses to shunt current from excitatory synapses depends on the spatial arrangement of the two inputs, it is speculated that the difference in APL synapses localization could contribute to some of the electrophysiological differences recorded among distinct KCs type. For example, α/β_c KCs were found to have a higher input resistances and longer membrane time constants compared to α'/β' KCs, resulting in a sigmoidal current-spike frequency function rather than a linear one. Additionally, a difference in synapses distribution can also indicate that two inhibitory mechanisms coexist at the MB calyx, similarly to what has been shown in the cerebellum where Golgi cells are responsible for both tonic inhibition, controlling granule cells spike number, gain control, and phasic inhibition, limiting the duration of granule cells responses (Prisco, 2021).

Finally, volumetric calcium imaging showed that the APL inhibition is local within the MB calyx. In particular, a difference was found in the APL calcium transients when flies were stimulated with odours that activate PN subsets with segregated bouton distribution in the calyx. These data suggest that APL inhibition onto MGs can be imagined as a gradient that peaks at the MGs active during a given stimulus and attenuates with distance. Non-spiking interneurons in insects are typically large and characterized by complex neurite branching, an ideal structure to support local microcircuits. As a matter of fact, similar examples of localized APL response as described in the current study have been reported in the Drosophila MB as well as in the APL's homolog GGN in the locust. An advantage of having local microcircuits is that it allows a single neuron to mimic the activity of several inhibitory interneurons, as described in amacrine cells of both mammals. Additionally, a parallel local-global inhibition is suggested to expand the dynamic range of inputs able to activate KCs (Prisco, 2021).

An important open question is whether the APL inhibition onto MGs of the MB calyx is more of a presynaptic phenomenon, therefore acting on PN boutons output, or a postsynaptic one on KCs claws. Functional data reveal a clear impact of APL on the postsynaptic response in MGs, while the PN boutons display a broad range of activity levels. Accordingly, silencing of the GABAA receptor Rdl in KCs increased calcium responses in the MB, including the calyx, and reduced sparseness of odour representations . However, due to the presence of presynapses from APL to PN boutons, a presynaptic component of APL inhibition is certainly possible (Prisco, 2021).

One possible caveat to the hypothesis is given by the fact that reducing GABA synthesis in APL by RNAi has been found to improve olfactory learning. However, this could be explained by a low efficiency of RNAi in this case. Indeed, incomplete silencing via RNAi increases KC output without affecting sparseness, whereas blocking APL output via shibire^ts leads to large, overlapping odour representations and impaired olfactory discrimination (Prisco, 2021).

Taken together, this study provides novel insights on how feedforward inhibition via APL shapes the postsynaptic response to olfactory inputs in the MB calyx and contributes to maintaining odour-evoked KC activity sparse. In the future, it will be interesting to investigate the impact of APL on memory consolidation, which has been associated with structural plasticity in the calyx and with changes in the KC response (Prisco, 2021).

Understanding neuronal representations of odor-evoked activities and their progressive transformation from the sensory level to higher brain centers features one of the major aims in olfactory neuroscience. This study investigated how odor information is transformed and represented in higher-order neurons of the lateral horn, one of the higher olfactory centers implicated in determining innate behavior, using Drosophila melanogaster. This study focused on a subset of third-order glutamatergic lateral horn neurons (LHNs) and characterized their odor coding properties in relation to their presynaptic partner neurons, the projection neurons (PNs), by two-photon functional imaging. Odors evoke reproducible, stereotypic, and odor-specific response patterns in LHNs. Notably, odor-evoked responses in these neurons are valence-specific in a way that their response amplitude is positively correlated with innate odor preferences. It is postulated that this valence-specific activity is the result of integrating inputs from multiple olfactory channels through second-order neurons. GRASP and micro-lesioning experiments provide evidence that glutamatergic LHNs obtain their major excitatory input from uniglomerular PNs, while they receive an odor-specific inhibition through inhibitory multiglomerular PNs. In summary, this study indicates that odor representations in glutamatergic LHNs encode hedonic valence and odor identity and primarily retain the odor coding properties of second-order neurons (Das Chakraborty, 2022).

Sweet and bitter compounds excite different sensory cells and drive opposing behaviors. However, it remains unclear how sweet and bitter tastes are represented by the neural circuits linking sensation to behavior. To investigate this question in Drosophila, this study devised trans-Tango(activity), a strategy for calcium imaging of second-order gustatory projection neurons based on trans-Tango, a genetic transsynaptic tracing technique. Spatial overlap was found between the projection neuron populations activated by sweet and bitter tastants. The spatial representation of bitter tastants in the projection neurons was consistent, while that of sweet tastants was heterogeneous. Furthermore, it was discovered that bitter tastants evoke responses in the gustatory receptor neurons and projection neurons upon both stimulus onset and offset and that bitter offset and sweet onset excite overlapping second-order projections. These findings demonstrate an unexpected complexity in the representation of sweet and bitter tastants by second-order neurons of the gustatory circuit (Snell, 2022).

Connectomes generated from electron microscopy images of neural tissue unveil the complex morphology of every neuron and the locations of every synapse interconnecting them. These wiring diagrams may also enable inference of synaptic and neuronal biophysics, such as the functional weights of synaptic connections, but this requires integration with physiological data to properly parameterize. Working with a stereotyped olfactory network in the Drosophila brain, direct comparisons were made of the anatomy and physiology of diverse neurons and synapses with subcellular and subthreshold resolution. Synapse density and location jointly predict the amplitude of the somatic postsynaptic potential evoked by a single presynaptic spike. Biophysical models fit to data predict that electrical compartmentalization allows axon and dendrite arbors to balance independent and interacting computations. These findings begin to fill the gap between connectivity maps and activity maps, which should enable new hypotheses about how network structure constrains network function (Liu, 2021).

The results show that much of the diversity in mean physiological connection weights between second-order projection neurons (PNs) and third-order lateral horn neurons (LHNs) can be explained by anatomical properties measurable in EM images. The number of synapses and LHN surface area successfully predict somatic unitary excitatory postsynaptic potential (uEPSP) amplitudes for dendritic connections, but severely underpredicts amplitudes for axonal connections. However, a more complex model incorporating neural morphology and synapse locations accurately predicted the physiology of both connection types. This highlights the insufficiency of neural point models (which ignore morphology) for predicting synaptic and neural function (Liu, 2021).

It is perhaps surprising that such a strong correspondence occurs with purely passive models, given the plethora of voltage-gated ion channels expressed in neurons. However, the quiescent network state (ex vivo preparation) and the minimal stimulation (single spike resolution in single neurons) are both favorable conditions for remaining in a passive regime. A slight tendency for underprediction of some of the smaller uEPSP amplitudes is noted, which might indicate some active boosting at the low end, or some sublinear integration at the high end of connection weights. Interestingly, a similar phenomenon has been observed in triplet recordings of PNs and LHNs. Nevertheless, the passive models should serve as a foundation in which to investigate the role of active properties (Liu, 2021).

It was found striking that the relationship between dendritic synapse density and uEPSP amplitude was largely linear, and that connections in the antennal lobe and mushroom body were consistent with this relationship. This suggests a conservation of fundamental biophysics across neurons and synapses. Notably, the capacitance of neural membrane and the resistance of intracellular medium are fairly consistent in different neurons and species. Although membrane resistance is more variable due to differences in ion channel expression and activation, the quiescent network state likely quenches many of these differences. The baseline synaptic conductance is likely fairly uniform across synapses in the Drosophila brain, but does vary in a use-dependent manner. It is thus anticipated that these predictions of synaptic function from PN-LHN connections will apply to other connections in the fly brain, but that some features, such as synaptic plasticity, will remain idiosyncratic (Liu, 2021).

The results also highlight the importance of comparing structure and function with single neuron and single spike resolution. While this study found that connectomic data accurately predicted uEPSPs evoked by single spikes in single neurons, a recent study (using the same data sources) reported lower predictive power for compound EPSP amplitudes evoked by multiple spikes in multiple neurons. Future efforts to link connectivity and physiology may thus face challenges if sufficient physiological resolution is not obtained, but the current results suggest that biophysical models could fill the gap in certain instances. For example, spatial and temporal integration across neural populations can be explicitly modeled to match commonly used experimental measures, such as voltage imaging from neuropil containing multiple cells or cell types (Liu, 2021).

The morphologies of neurons are famously diverse. Yet while morphology affects synaptic efficacy in some neurons, it may play a minimal role in others. For LHNs, this study found evidence of both phenomena: fine morphology within arbors has limited impact on synaptic efficacy, while the inter-arbor cable has a major impact. More specifically, the results show that individual arbors passively integrate synaptic input democratically. This occurs because large local variations in mEPSP amplitude are mostly offset by compensatory variations in voltage attenuation. The electrotonic structure of central Drosophila neurons may thus be similar to the dendrites of cerebellar Purkinje cells, which orchestrate a similar dendritic democracy with passive mechanisms in a heavily branched arbor. Interestingly, this configuration has been attributed to the lack of a central trunk neurite, so the branching characteristics of fly neuron arbors may be a mechanism to achieve uniform synaptic efficacy without special spatial patterning of ion channel expression or synaptic conductances. In addition, because most PN-LHN connections target a single arbor with multiple spatially distributed synapses, much of the residual variability due to synapse location will average out for larger connections. Single arbors may therefore be fundamental 'units' of computation in Drosophila neurons, which can spatially intermingle even within the same brain region. Inter-arbor cables strike a balance between interaction and independence between arbors. Interaction enables neurons to compare inputs arriving on different arbors. This is especially relevant, because axon and dendrite arbors receive their own complements of synaptic inputs. LHNs are thus reminiscent of coincidence-detector neurons in the auditory brainstem, where input from each ear impinges on different dendritic arbors, allowing the connecting cable to compare timing. Inter-arbor cables in local LHNs are longer than necessary to connect the arbors, enabling discrimination of temporal sequences on behaviorally relevant scales of ~10 msec. In contrast, independence between arbors can enable some functions to remain arbor-specific. For instance, arbor-specific structural plasticity or active conductances (e.g., voltage-gated potassium channels) could implement different transformations within each arbor prior to comparison via the inter-arbor cable. Such a configuration could enable more complex computations such as multiplication (Liu, 2021).

The abstraction of intricate morphologies into arbors and cables should prove useful for studying other neurons. Even within the fly brain, a wide range of configurations exist, including neurons with one arbor and no cable, neurons with one arbor and one cable (Kenyon cells without axonal branching), three arbors with interposed cables (optic lobe neurons, and 2-dimensional arrays of dozens of arbors (amacrine neurons). An intriguing possibility is that the arbor and cable configuration largely determines the passive biophysics of these neurons, which could provide a simple organizing framework for predicting the function of neurons with diverse neuron morphologies(Liu, 2021).

The pairing of network connectivity maps with knowledge of neuronal and synaptic physiology provides a foundation to formulate hypotheses about activity maps, because assumptions about the function of each component can be calibrated. This study takes important steps in this direction by showing how synapse densities predict uEPSP amplitudes and how morphology predicts subcellular computation. Moreover, this study demonstrated that a simplified compartmental model (the barbell model) can balance biological accuracy with computational tractability. Incorporating these results into simulations of large neural networks should allow the formulation of more precise mechanistic hypotheses about the function of previously unexplored brain circuits(Liu, 2021).

An important goal for the future will be to incorporate additional sources of knowledge to constrain other properties, such as synaptic plasticity, active conductances, and neuromodulation. For example, short-term plasticity can correlate with the number of presynaptic vesicles or the location of a synapse along the dendrite. A systematic comparison of synaptic ultrastructure with synaptic plasticity may therefore reveal other structural patterns that predict function. Active properties of neurons are less likely to be predictable from ultrastructure but could be predicted from proteomics and transcriptomics. Relating physiological measurements to these data across cell types could be used to calibrate estimates of active biophysical properties. Neuromodulation can reconfigure entire networks, yielding different functions in different conditions. In the fly, dopamine alters physiological synaptic strengths, but it is not clear if such changes are visible in EM images. High resolution mapping of cellular and synaptic biochemical processes would thus be a valuable companion to a connectome. (Liu, 2021).

Another goal will be to incorporate ongoing refinement to wiring diagrams into model calibrations. This is important, because some regions of the hemibrain connectome still have incompletely traced connections. In addition, this connectome also lacks information on electrical synapses, glia, and some subcellular structures, which have important functional roles. As this information becomes available, the calibration of cellular and synaptic predictors can be continually adjusted and improved. (Liu, 2021).

While connectivity maps of increasingly large brain volumes bring new opportunities for understanding network organization, predicting function from structure remains famously difficult. While this study have focused on the function of individual components (i.e., neurons and synapses), a central challenge will be identifying how the operation of entire networks depends on those components. Although physiological properties of synapses and neurons are most easily characterized in quiescent network states (e.g., ex vivo), many network operations occur in highly active states (e.g., in vivo). The combination of connectivity maps with validated models of synaptic and neuronal function should help to bridge this gap, by generating testable predictions of how the anatomy and physiology of neurons and synapses constrain activity maps during behavior (Liu, 2021).

Evaluating odor blends in sensory processing is a crucial step for signal recognition and execution of behavioral decisions. Using behavioral assays and 2-photon imaging, this study has characterized the neural and behavioral correlates of mixture perception in the olfactory system of Drosophila. Mixtures of odors with opposing valences elicit strong inhibition in certain attractant-responsive input channels. This inhibition correlates with reduced behavioral attraction. Defined subsets of GABAergic interneurons provide the neuronal substrate of this computation at pre- and postsynaptic loci via GABAB- and GABAA receptors, respectively. Intriguingly, manipulation of single input channels by silencing and optogenetic activation unveils a glomerulus-specific crosstalk between the attractant- and repellent-responsive circuits. This inhibitory interaction biases the behavioral output. Such a form of selective lateral inhibition represents a crucial neuronal mechanism in the processing of conflicting sensory information (Mohamed, 2019).

This study analyzed the integration of binary odor mixtures of opposing hedonic valences and demonstrate how glomerular-specific inhibition and crosstalk results in an appropriate behavioral output. Glomeruli that strongly respond to the attractive odor are inhibited by the repellent odor in the mixture, which is mediated by defined subsets of GABAergic local interneurons (LNs; see Circuit model for glomerulus-specific crosstalk in the fly AL). Heterogeneity in responses to mixtures has been shown in previous studies where excitation of some glomeruli by one of the mixture components can inhibit the glomeruli activated by the other component. Similar to invertebrates, evidence for non-linearity of mixture interactions has been reported in individual mitral/tufted cells (PNs analogs) in the olfactory bulb of vertebrates. As an alternative scenario it is also conceivable that instead of inhibiting the attractant-coding pathway to shift the behavior towards aversion, the response of the repellent-responsive glomeruli could be boosted via lateral excitation. Lateral excitation has been described to drive synergistic interaction between the binary mixture of cis-vaccenyl acetate and vinegar. Although odors representing sex and food are mutually reinforcing, a binary mixture of odors with opposing valences means a conflicting input. It is therefore postulated that, in contrast to reinforcing input, conflicting sensory input is processed via lateral inhibition in the fly AL. An assumption that would be intriguing to be tested in the future (Mohamed, 2019).

No inhibition of the attractant-responsive glomeruli when stimulating with MIX(+) (a binary mixture of ethyl acetate and benzaldehyde). This lack of inhibition is probably due to the strong ORN input leading to high presynaptic firing rates in the attractant-responsive glomeruli. Consequently, lateral inhibition deriving from the aversive circuit has only a low impact and does not decrease the excitation of the attractant-responsive glomeruli (Mohamed, 2019).

Obviously not all glomeruli that are activated by an attractive odor are inhibited by a repellent in a mixture and might not contribute to the attractiveness of an odor. This observation makes sense in the light of accumulating evidence suggesting that the innate behavioral output is correlated either to the summed weights of specific activated glomeruli or to the activity of single processing channels. The latter argument is supported by the finding that only very few, special glomeruli seem to be valence-specific and induce clear attraction or aversion behavior upon artificial activation (Mohamed, 2019).

It is important to mention that the subset of repellent-responsive glomeruli does also respond to non-aversive and even partly attractive odors, such as E2-hexenal and ethyl benzoate. However, an attractive odorant may indeed activate some aversive input channels beside their main activation of the attractive circuitry (or the other way around). What actually matters is the behavioral output that is consequently elicited when a specific glomerulus becomes activated. For example, ORNs that respond to CO2 are also activated by ethyl benzoate and E2-hexenal. However, the CO2 circuit has been clearly demonstrated to mediate behavioral aversion. Following this argument, artificial activation of glomeruli DL1 and/or DL5 leads to aversive behavior, while silencing DM1 and DM4 abolished attraction to the attractant. These experiments provide evidence that activation of the repellent- and attractant-responsive glomeruli causes a valence-specific behavior, and can therefore be defined as attractive or aversive input channels, respectively (Mohamed, 2019).

Interestingly, one exception was observed in the data set: although the repellent odor geosmin reduced the attraction to balsamic vinegar in the mixture, no mixture inhibition was observed. The detection of geosmin is one of the rare cases, where an odor is detected by only one receptor type and consequently activates only one glomerulus. Similar specialized pathways have been described for the detection of sex pheromones and CO2. Glomeruli processing these ecologically labeled lines differ from broadly tuned glomeruli with regard to their neuronal composition. Hence, it is conceivable that the narrowly tuned geosmin-responsive glomerulus does not exhibit strong interglomerular interactions and has therefore a different impact on the attractant-responsive glomeruli. Mixture interactions between geosmin and attractive odors might be implemented in higher processing centers which contain circuit elements mediating interactions between odors (Mohamed, 2019).

Lateral inhibition, which is believed to enhance contrast and to facilitate discrimination of similar stimuli, is an important motif throughout the nervous system. In mice, dense center-surround inhibition refines mitral cell representation of a glomerular map56, while other evidence showed that lateral inhibition can be rather selective and biased between different mitral cells. In accordance with the olfactory bulb, the AL exhibits broad, selective or even both forms of lateral inhibition, whereby certain glomeruli can show different sensitivities towards an inhibitory input. Lateral inhibition in the Drosophila AL is largely mediated through GABA. Most of the GABAergic inhibition in the Drosophila AL has been shown to take place predominantly on the presynaptic site mediated through GABAA and GABAB receptors. In addition, PNs also receive GABAergic inhibition via GABAA and/or GABAB receptors from LNs. Notably, this study found that two out of four attractant-responsive glomeruli are inhibited on the pre- and postsynaptic levels (via GABAB- or GABAA-receptors), while the other two glomeruli are inhibited only presynaptically through GABAB-type receptors. Previous results have so far shown that GABAA-type receptors contribute weakly to lateral inhibition and shape the early phase of odor responses. However, the data demonstrate that GABAA-type receptors largely mediate mixture-induced inhibition during the full period of the odor presentation which is reminiscent to tonic inhibition in the mammalian system (Mohamed, 2019).

This study shows that mixture-induced lateral inhibition of the attractant-responsive glomeruli was abolished when GABA synthesis was silenced in mostly patchy LNs. Hence the data suggest, in consistency with previous studies, that LNs with more selective innervations mediate glomerulus-specific interactions and rather contribute to mixture processing, while pan-glomerular LNs (e.g., GH298-Gal4 and H24-Gal4), that globally release GABA, might be involved in gain control (Mohamed, 2019).

Interestingly, the repellent-responsive glomeruli DL1 and DL5 did not show any mixture interaction, but mediate the lateral inhibition of the attractant-responsive glomeruli. Two possible scenarios would provide the neuronal substrate for this mechanism dependent on either the donor (i.e. LNs) or the receiver (i.e. glomerulus) side. First, since glomeruli vary dramatically in their GABA sensitivity and consequently their sensitivity to LN activation14, lateral inhibition is heterogeneous across different glomeruli. Second, lateral inhibition is biased among different glomeruli due to a glomerulus-specific synaptic distribution of pre- and postsynapses of GABAergic LNs, i.e. the GABA release is not uniform. This assumption is supported by data revealing that GABAergic LNs possess a higher density of postsynapses in DL1 and DL5 than in the attractant-responsive glomeruli. In line with the current findings, EM based data from the larvae AL describe GABAergic, oligoglomerular 'choosy' LNs with a clear polarity contributing to postsynaptic inhibition for most glomeruli, while they receive inputs from only a small glomerular subset. Hence, there is strong evidence that some glomeruli can drive lateral inhibition in other glomeruli. Both scenarios could either occur separately or reinforce each other. Moreover, it might be ecological relevant not to inhibit the input of the aversive pathways since these are associated with life-threatening situations that should be coded reliably and rather override an attractive input (Mohamed, 2019).

In contrast to expectations, sole photoactivation of DL1 or DL5 or stimulation with the repellent alone did not induce inhibition in the attractant-responsive glomeruli. This might be due to the low spontaneous activity of ORNs innervating the attractant-responsive glomeruli, which correlates with spontaneous fluctuations in the membrane potential of the postsynaptic PNs. Consequently, inhibitory responses (i.e. hyperpolarizations) are difficult to capture with calcium imaging (Mohamed, 2019).

In other sensory systems, lateral inhibitory connections of neuronal subsets involved in sensory processing have been elucidated in great detail, such as in the retina of mice or the rat visual cortex. Also for the Drosophila AL, previous studies suggested that glomerular subgroups are connected via inhibitory LNs. However, these studies could neither pinpoint the precise connections nor their significance for behavioral perception. The data provide evidence for a specific inhibitory crosstalk between identified glomeruli and substantiate the existence of selective lateral inhibition in the fly AL. The postulated network circuits offer insights into the principle of sensory integration. It will be intriguing to see whether neuron-specific crosstalk represents a general phenomenon to integrate multiple and rather conflicting input channels in other sensory modalities (Mohamed, 2019).

Local interneurons (LNs) in the Drosophila olfactory system exhibit neuronal diversity and variability, yet it is still unknown how these features impact information encoding capacity and reliability in a complex LN network. This study used two strategies to construct a diverse excitatory-inhibitory neural network beginning with a ring network structure and then introduced distinct types of inhibitory interneurons and circuit variability to the simulated network. The continuity of activity within the node ensemble (oscillation pattern) was used as a readout to describe the temporal dynamics of network activity. Inhibitory interneurons were found to enhance the encoding capacity by protecting the network from extremely short activation periods when the network wiring complexity is very high. In addition, distinct types of interneurons have differential effects on encoding capacity and reliability. Circuit variability may enhance the encoding reliability, with or without compromising encoding capacity. Therefore, this study has described how circuit variability of interneurons may interact with excitatory-inhibitory diversity to enhance the encoding capacity and distinguishability of neural networks. This work has evaluate the effects of different types and degrees of connection diversity on a ring model, which may simulate interneuron networks in the Drosophila olfactory system or other biological systems (Tsai, 2018).

Drosophila olfactory local interneurons (LNs) in the antennal lobe are highly diverse and variable. How and when distinct types of LNs emerge, differentiate, and integrate into the olfactory circuit is unknown. Through systematic developmental analyses, this study found that LNs are recruited to the adult olfactory circuit in three groups. Group 1 LNs are residual larval LNs. Group 2 are adult-specific LNs that emerge before cognate sensory and projection neurons establish synaptic specificity, and Group 3 LNs emerge after synaptic specificity is established. Group 1 larval LNs are selectively reintegrated into the adult circuit through pruning and re-extension of processes to distinct regions of the antennal lobe, while others die during metamorphosis. Precise temporal control of this pruning and cell death shapes the global organization of the adult antennal lobe. These findings provide a road map to understand how LNs develop and contribute to constructing the olfactory circuit (Liou, 2018.

Local inhibitory neurons control the timing of neural activity in many circuits. To understand how inhibition controls timing, it is important to understand the dynamics of activity in populations of local inhibitory interneurons, as well as the mechanisms that underlie these dynamics. This study describes the in vivo response dynamics of a large population of inhibitory local neurons (LNs) in the Drosophila melanogaster antennal lobe, the analog of the vertebrate olfactory bulb, and dissects the network and intrinsic mechanisms that give rise to these dynamics. Some LNs respond to odor onsets ("ON" cells) and others to offsets ("OFF" cells), whereas still others respond at both times. Moreover, different LNs signal odor concentration fluctuations on different timescales. Some respond rapidly, and can track rapid concentration fluctuations. Others respond slowly, and are best at tracking slow fluctuations. A continuous spectrum of preferred stimulation timescales was found among LNs, as well as a continuum of ON-OFF behavior. Using in vivo whole-cell recordings, it was shown that the timing of an LN's response (ON vs OFF) can be predicted from the interplay of excitatory and inhibitory synaptic currents that it receives. Meanwhile, the preferred timescale of an LN is related to its intrinsic properties. These results illustrate how a population of inhibitory interneurons can collectively encode bidirectional changes in stimulus intensity on multiple timescales, and how this can arise via an interaction between synaptic and intrinsic mechanisms (Nagel, 2016).

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In nature, animals form memories associating reward or punishment with stimuli from different sensory modalities, such as smells and colors. It is unclear, however, how distinct sensory memories are processed in the brain. This study established appetitive and aversive visual learning assays for Drosophila that are comparable to the widely used olfactory learning assays. These assays share critical features, such as reinforcing stimuli (sugar reward and electric shock punishment), and allow direct comparison of the cellular requirements for visual and olfactory memories. It was found that the same subsets of dopamine neurons drive formation of both sensory memories. Furthermore, distinct yet partially overlapping subsets of mushroom body intrinsic neurons are required for visual and olfactory memories. Thus, these results suggest that distinct sensory memories are processed in a common brain center. Such centralization of related brain functions is an economical design that avoids the repetition of similar circuit motifs (Vogt, 2014).

Devising a transparent electric shock grid module made it possible to apply the same visual stimulation in aversive and appetitive conditioning assays. Also an integrated platform was developed for fully automated high-throughput data acquisition using customized software to control the presentation of electric shock and visual stimuli while making video recordings of behavior. In these assays, memory performance is based on altered visual preference in walking flies, a task likely to be less demanding than the constant flight required for flight simulator learning. These advantages facilitate behavioral examination of many genotypes (Vogt, 2014).

Circuits underlying olfactory and visual memory can be optimally compared when the sugar reward and electric shock punishment are matched between the two modalities. Visual and olfactory memories share the same subsets of dopamine neurons that convey reinforcing signals. This shared requirement of the transmitter system between visual and olfactory learning has been described in crickets. However, the pharmacological manipulation used in these studies does not allow further circuit dissection (Vogt, 2014).

For electric shock reinforcement, identified neurons in the PPL1 cluster, such as MB-MP1, MB-MV1 and MB-V1, drive aversive memories in both visual and olfactory learning, while the MB-M3 neurons in the PAM cluster seem to be involved specifically in aversive olfactory memory. Thus, overlapping sets of dopamine neurons appear to represent electric shock punishment in both visual and olfactory learning with olfactory aversive memory probably recruiting a larger set. Previous studies have shown that the MB-M3 neurons induce aversive olfactory memory that increases stability of other memory components. Olfactory memories last longer than visual memories potentially due to the recruitment of additional dopamine neurons (Vogt, 2014).

In appetitive conditioning, PAM cluster neurons play crucial roles in both olfactory and visual memories. Which cell types in these clusters are involved and whether there is a cellular distinction between olfactory and visual memory requires further analysis at the single cell level. Most importantly, all these neurons convey dopamine signals to restricted subdomains of the MB. The blockade of octopamine neurons did not impair appetitive visual memories with sucrose. The involvement of octopamine neurons may be more substantial when non-nutritious sweet taste rewards are used, as has been shown in olfactory learning (Vogt, 2014).

In addition to these shared reinforcement circuits in the MB, the necessity of MB output for visual memory acquisition and retrieval is also consistent with olfactory conditioning. Taken together, these results suggest that the MBs harbor associative plasticity for visual memories and support the conclusion that similar coincidence detection mechanisms are used to form memories within the MBs. Centralization of similar brain functions spares the cost of maintaining similar circuit motifs in different brain areas and may be an evolutionary conserved design of information processing. Such converging inputs of different stimuli into a multisensory area have even been described in humans (Vogt, 2014).

'Flight simulator' visual learning was shown to require the central complex but not the MBs. Although this appears to contradict the current study, it is noted that there are important differences between the behavioral paradigms employed. In the flight simulator, a single tethered flying Drosophila is trained to associate a specific visual cue with a laser beam punishment, to later on avoid flying towards this cue in the test. Although this study controlled for visual context consistency and the 'operant component' of the flight simulator training, any other difference could account for the differential requirement of brain structures. Given that flies during flight show octopamine-mediated modulation of neurons in the optic lobe, similar state-dependent mechanisms might underlie different requirement of higher brain centers. Thus, it is critical to design comparable memory paradigms (Vogt, 2014).

This study together with previous results in associative taste learning highlights the fact that the role of the MB in associative learning is not restricted to one sensory modality or reinforcer. This study found that olfactory and visual memories recruit overlapping, yet partly distinct, sets of Kenyon cells (see Circuit model of olfactory and visual short-term memories). In contrast to the well-described olfactory projection neurons, visual inputs to the MB remain unidentified. No anatomical evidence has been reported in Drosophila for direct connections between optic lobes and MBs although such connections are found in other insects. Also afferents originating in the protocerebrum were found to provide multi-modal input to the MB lobes of cockroaches. Thus, Drosophila MBs may receive indirect visual input from optic lobes, and the identification of such a visual pathway would significantly contribute to understanding of the MB circuits (Vogt, 2014).

Given the general requirement of the γ lobe neurons, visual and olfactory cues may be both represented in the γ neurons. Consistently, the dopamine neurons that convey appetitive and aversive memories heavily project to the γ lobe. In olfactory conditioning, the γ lobe was shown to contribute mainly to short-term memory. This converging evidence from olfactory and visual memories suggests a general role for the γ lobe in short-lasting memories across different sensory modalities. Previous studies found that the MB is also involved in sensorimotor gating of visual stimuli or visual selective attention. Therefore, the MB circuits for visual associative memories might be required for sensorimotor gating and attention (Vogt, 2014).

Interestingly, the contribution of the α'/β' lobes is selective for olfactory memories. This Kenyon cell class is more specialized to odor representation, as the cells have the broadest odor tuning and the lowest response threshold among the three Kenyon cell types (Vogt, 2014).

The role of α/β neurons in visual memories is also limited. The α/β neurons might play more modulatory roles in specific visual memory tasks, such as context generalization, facilitation of operant learning and occasion setting. This modulatory role of the α/β neurons is corroborated in olfactory learning, where they are preferentially required for long-lasting memories (Vogt, 2014).

Differentiated but overlapping sensory representations by KCs may be conserved among insect species. In honeybees, different sensory modalities are represented in spatially segregated areas of the calyx, whereas the basal ring region receives visual and olfactory inputs. The MB might thus have evolved to represent the sensory space of those modalities that are subject to associative modulation (Vogt, 2014).

The mushroom body in the fruitfly Drosophila melanogaster is an associative brain centre that translates odour representations into learned behavioural responses. Kenyon cells, the intrinsic neurons of the mushroom body, integrate input from olfactory glomeruli to encode odours as sparse distributed patterns of neural activity. This study has developed anatomic tracing techniques to identify the glomerular origin of the inputs that converge onto 200 individual Kenyon cells. Each Kenyon cell integrates input from a different and apparently random combination of glomeruli. The glomerular inputs to individual Kenyon cells show no discernible organization with respect to their odour tuning, anatomic features or developmental origins. Moreover, different classes of Kenyon cells do not seem to preferentially integrate inputs from specific combinations of glomeruli. This organization of glomerular connections to the mushroom body could allow the fly to contextualize novel sensory experiences, a feature consistent with the role of this brain centre in mediating learned olfactory associations and behaviours (Caron, 2013).

Olfactory perception in the fly is initiated by the binding of an odorant to an ensemble of olfactory sensory neurons (OSNs) in the antennae, resulting in the activation of a unique and topographically fixed combination of glomeruli in the antennal lobe (AL). The discrimination of odours therefore requires the integration of information from multiple glomeruli in higher olfactory centres. AL projection neurons (PNs) extend dendrites into a single glomerulus and project axons that bifurcate to innervate two distinct brain regions, the lateral horn and the MB. The invariant circuitry of the lateral horn is thought to mediate innate behaviours, whereas the MB translates olfactory sensory information into learned behavioural responses. PN axons that innervate the MB terminate in large boutons. A given KC extends a small number of dendritic “claws”, with each claw receiving information from only one PN bouton. A single bouton connects to multiple KC claws to form a discrete anatomic structure, the microglomerulus. Each KC projects an axon to one of the three different classes of MB lobes, α/β, α’/β’, or γ, where it synapses upon a relatively small number of extrinsic output neurons (Caron, 2013).

Electrophysiological and optical imaging studies show that odorants activate sparse subpopulations of KCs distributed across the MB without spatial preference. Individual KCs could be connected to preferential combinations of glomeruli that are co-ordinately activated by behaviourally relevant odours. Alternatively, KCs may not receive structured input; rather the glomerular inputs may be random, a feature that maximizes the diversity of odour representations in the MB. This study has exploited the specialized structure of the PN-KC synapse to characterize the glomerular origin of the PNs that converge onto individual KCs (Caron, 2013).

Photoactivatable green fluorescent protein (PA-GFP) was expressed in all neurons of the fly and a single KC was photolabelled. It was observed that individual photolabelled KCs elaborate between 2 and 11 dendritic claws restricted to the main olfactory calyx. The axonal projections of a labelled KC can be traced into either the α/β, α’β’, or γ lobes of the MB. Texas red dextran was then electroporated into the centre of a single KC claw, filling the PN bouton innervating that claw. Retrograde transfer of the dye labels a single PN and its associated AL glomerulus, providing further evidence that an individual KC claw receives input from only a single glomerulus (Caron, 2013).

It was verified that this tracing method identifies functional connections between PNs and KCs. Functional imaging was performed on flies that express the calcium indicator GCaMP3 in most KCs to identify the claws activated by the stimulation of a single glomerulus. Electroporation of dye into an activated microglomerulus labels a single PN that innervates the stimulated glomerulus. Thus, the electroporation of dye into a KC claw allows faithful identification of the PN to which it is functionally connected (Caron, 2013).

The strategy of photolabelling a single KC and sequential electroporation of dye into each of its claws was used to define glomerular inputs to an individual KC. In initial experiments, PA-GFP was expressed in all neurons and 100 randomly chosen KCs were photolabelled in 100 different flies. Among the 100 photolabelled KCs, 84 α/β KCs, 14 α’/β’ KCs, but only 2 γ KCs were identified. Each MB contains about 1000 α/β KCs, 370 α’β’ KCs, and 670 γ KCs. γKCs are underrepresented in this initial data set. This is likely to result from the spatial segregation of their cell bodies, which renders γ KCs less accessible to photolabelling. Most α/βKCs, but not the α/β and α’β’ KCs, express Fruitless (Fru). An additional 100 γ KCs were targeted for photoactivation in flies expressing PA-GFP under the control of the Fru promoter (Caron, 2013).

Texas red dextran was sequentially electroporated into different claws of a photolabelled KC. It is technically difficult to fill all the claws of a KC and on average 3 glomerular inputs were identified per KC. In fewer than 5% of the samples, the number of labelled PNs differed from the number of claws filled, reflecting either unsuccessful or imprecise electroporation. Samples with more labelled PNs than expected were discarded. The low frequency of unsuccessful claw fills indicates that claws extending from a given KC were filled with equal efficiency independent of size. Thus the size of a claw was not a selection criterion in these experiments (Caron, 2013).

A total of 683 inputs that synapse on 200 KCs were identified. 654 of these inputs connect to PNs innervating 49 of the 51 AL glomeruli. PNs innervating the DA3 and VL1 are absent from the data set but boutons were identified from these PNs in the MB calyx. 29 of the claws receive input from brain regions other than the AL. Interestingly, 11 of these claws are innervated by PNs that derive from pseudoglomeruli in the proximal antennal protocerebrum, a thermosensing centre in the fly brain that receives input from distinct heat- and cold-sensing neurons in the antennae. The remaining 18 PNs innervated different uncharacterized regions of the brain (Caron, 2013).

It was observe that the distribution of the glomerular inputs to KCs is not uniform. Inputs from the DA1 and DC3 glomeruli are most frequent, with each accounting for 5.1% of the total connections. The non-uniform distribution reflects the fact that the size and number of calycal boutons formed by PNs varies across glomeruli. For instance, the PNs associated with the DA1 and DC3 glomeruli form more numerous boutons in comparison with the PNs of less frequently represented glomeruli. It was also observed that there is a small but significant difference between the inputs to the α/β and γ KCs (p < 0.001). All subsequent statistical analyses were therefore performed separately on both the α/β and γ data sets, but failed to reveal any significant difference between the two data sets. Therefore, only the results obtained from the full data set are shown (Caron, 2013).

Statistical analyses of the 665 connections allowed searching for structure among the connections between glomeruli and KCs. First, it wae determined whether the KCs receiving input from a given glomerulus have a higher probability of receiving additional input from that same glomerulus. Of the 200 KCs in the data set, only 11 receive two inputs from the same glomerulus, and none receive three or more such inputs. It was determined whether the frequency of convergent input from a single glomerulus is significantly above or below chance expectations by randomly shuffling the connections in the data set between the different KCs, while maintaining the number of connections each of them receives. This shuffling maintains the frequency of glomerular connections observed in the experimental data, but eliminates any potential, non-random patterns of inputs onto individual KCs. This shuffling is used in all subsequent statistical analyses. The frequency of multiple connections from the same glomerulus in the observed and shuffled data sets is not significantly different. Thus, no KCs were observed that receive preferential inputs from a single glomerulus. Rather, individual KCs integrate information from multiple different glomeruli (Caron, 2013).

It was next determined whether KCs are connected to any preferential pair, trio, or quartet of glomeruli. Of the 1378 (53*52/2) different pairs of glomeruli that could converge onto an individual KC, 508 distinct pairs appear in the data set. 310 of these pairs connect to only one of the 200 KCs analysed, whereas certain pairs of glomeruli connect to multiple KCs. There are combinations of glomerular trios that connect with two different KCs, and one case in which two KCs receive inputs from the same quartet of glomeruli. However, the observed frequency with which the different pairs, trios and quartets converge onto different KCs is consistent with expectations from the shuffled data set. Thus, the identity of a glomerulus connected to a KC provides no predictive information as to the identity of the remaining glomerular inputs onto that neuron (Caron, 2013).

Glomeruli can be grouped based on biological properties shared by their associated OSNs (sensilla type, odour specificity) and PNs (developmental origin and topography of their axonal projections). KCs might receive preferential input from one or another of these glomerular categories. For example, the OSNs innervating the AL are derived from three sensillar types (basiconic, coeloconic, and trichoid sensilla) that project to three classes of glomeruli tuned to different odour categories. If individual KCs were tuned to a particular class of odours, they might preferentially integrate inputs from one type of sensillum. Statistical analyses, however, reveal that KCs that receive an input from one sensillar type are no more or less likely to receive additional inputs from this or any other type of sensillum than is predicted by chance. Sensillar type, however, provides only a coarse correlate of odour tuning. Therefore, glomeruli were first grouped based on the similarity of their odour response profiles and again no structure was observed in the inputs to a KC that correlated with odour tuning (Caron, 2013).

Glomeruli were classified on the basis of the properties of their PNs. PN axons from different glomeruli project to broad but stereotyped domains in the lateral horn and calyx of the MB. Input to an individual KC could be shaped by the topography of PN projections. Analysis of the distribution of inputs to a given KC, however, fails to reveal any preferential PN connectivity that reflects the organization of their projection in either the MB calyx or lateral horn. KCs do not preferentially integrate information from glomeruli innervated by PNs sharing a developmental origin. In addition, KCs do not select their input based on topographical constraints as suggested by a previous study. Finally, three glomeruli are innervated by Fru-expressing OSNs and PNs. Preferential pairing of inputs from Fru⁺ PNs onto individual KCs were not observed. Moreover, although most γ KCs express Fru, there is no preferential input from Fru⁺ glomeruli to γ KCs (Caron, 2013).

Next, an unbiased search was performed for structure by examining correlations within the connectivity matrix between the 53 glomeruli (51 AL glomeruli and 2 pseudoglomeruli) and the 200 KCs. Correlations were extracted by performing a principal component analysis of this matrix. This analysis failed to reveal structure in the input to KCs other than that inherent in the non-uniform distribution of glomerular inputs (Caron, 2013).

These data are consistent with a model in which each KC receives input from a combination of glomeruli randomly chosen from the non-uniform distribution of glomerular projections to the MB. Classification of either glomeruli or KCs on the basis of several shared developmental, anatomic, and functional features fails to reveal structured input onto individual KCs. Members of a given PN class do not preferentially converge onto an individual KC nor do members of a KC class receive specific and distinguishing PN inputs. A given KC can integrate information from glomeruli activated by food odours, pheromones, CO₂ and even temperature. Recent data suggests that the extrinsic output neurons of the MB that are responsible for the different forms of learned behaviour are anatomically segregated and synapse with KC axons within a specific MB lobe. Interestingly, similar glomerular inputs are observed for the KCs that innervate the different lobes of the MB. This random input to individual KCs provides a mechanism to contextualize a rich diversity of novel KC responses (Caron, 2013).

It is important to note that the tracing procedure that was developed only allows characterization of the inputs to a single KC per fly. It is therefore possible that the inputs to every KC are determined but this developmental program results in a distribution of glomerular inputs that appears random. However, it is difficult to conceive of a development mechanism that could dictate the identity of inputs to each of the seven claws of the 2000 KCs. Moreover, the logic of employing complex and unlikely identity codes to achieve an uncorrelated distribution of inputs is elusive. Indeed, a previous study examined the electrophysiological response of KCs to different odours in a line of flies that labels only 23 α/β neurons but failed to identify replicate KCs with shared odour response profiles. These observations support the conclusion that the complement of glomerular inputs to KCs differs in different individuals. In addition, it is not possible, from the analysis of the glomerular inputs to 200 KCs, to exclude the existence of small subsets of KCs that received determined inputs from the AL. Nonetheless, the data are most consistent with a model in which the majority of individual KCs receive input from a random collection of glomeruli, a finding with important implications for odour processing in the MB (Caron, 2013).

If the connections from AL to MB are indeed random, a given odour will activate a different ensemble of KCs in different flies. However, in an individual fly, a given odour will consistently activate the same ensemble. This representation must acquire valence through experience or unsupervised activity dependent plasticity to dictate an appropriate behavioural output. Uncorrelated glomerular input to KCs affords the fly with the ability to impart meaning to a diversity of novel and unpredictable sensory stimuli that it may encounter throughout its life. Plasticity at highly convergent synapses between KC axons and MB extrinsic neurons could mediate experience-dependent behavioural output, an elemental feature of MB function. Thus, the fly has evolved an olfactory circuit with a connectivity that optimizes its ability to contextualize and respond appropriately to a rich array of olfactory experiences (Caron, 2013).

The mechanisms by which this immature activity is generated, shaped, and terminated vary from system to system. In the embryonic OSNs, the transition from irregular spike-trains to continuous discharge may require the expression of olfactory receptors (OR), because in larvae mutant for the co-receptor Orco Or83b, necessary for OR function, this transition does not occur normally. Since Orco is expressed before the onset of spontaneous activity, it is suggested that the change in the pattern of OSN spontaneous activity is likely to be driven, at least in part, by the onset and level of expression of specific ORs. However, this might not be the only factor shaping spontaneous activity patterns over development, and other factors such as expression of other ion channels may also play a role. This might explain why 16 h AEL Orco mutants have indistinguishable levels of activity when compared with controls, yet the variability in their spike train is significantly increased (Prieto-Godino, 2012).

Previous studies have suggested that spontaneous activity is essential for the normal development of vertebrate OSNs, but that there is no such requirement in insects. However, this study found that there is a role for OSN activity in the development of the larval olfactory network. OSN activity regulates the morphology of OSN terminals independently of activity in neighbouring axons, and without activity terminals appear immature and occupy larger territories. This is similar to what has been described in zebrafish and mouse OSN terminals devoid of activity. There is also a report of a similar phenotype found in the AL of third instar Drosophila larvae after synaptic release was blocked in a large subset of OSNs. The results show that while immature terminal morphology is a cell autonomous phenotype that is independent of activity levels in neighbouring OSN axons, the expansion of OSN terminals is limited by interactions among the OSN terminals. Interestingly a similar process has been found to regulate the morphology and terminal expansion of retinotectal axons. Thus the control of axonal terminal extension via activity-dependent interactions may be a general process in the wiring of nervous systems. The nature of inter-axonal interactions that limit terminal growth remains unknown and is one example of how future work using amenable experimental systems such as the one provided by the larval olfactory network in Drosophila larvae may reveal general mechanisms operating during the assembly of neural circuitry (Prieto-Godino, 2012).

Animals' response to a stimulus in one sensory modality is usually influenced by other modalities. One important type of multisensory integration is the cross-modal modulation, in which one sensory modality modulates (typically inhibits) another. Identification of the mechanisms underlying cross-modal modulations is crucial for understanding how sensory inputs shape animals' perception and for understanding sensory processing disorders. However, the synaptic and circuit mechanisms that underlie cross-modal modulation are poorly understood. This is due to the difficulty of separating cross-modal modulation from multisensory integrations in neurons that receive excitatory inputs from two or more sensory modalities(5)-in which case it is unclear what the modulating or modulated modality is. This study reports a unique system for studying cross-modal modulation by taking advantage of the genetic resources in Drosophila. Gentle mechanical stimuli was shown to inhibit nociceptive responses in Drosophila larvae. Low-threshold mechanosensory neurons inhibit a key second-order neuron in the nociceptive pathway through metabotropic GABA receptors on nociceptor synaptic terminals. Strikingly, this cross-modal inhibition is only effective when nociceptor inputs are weak, thus serving as a gating mechanism for filtering out weak nociceptive inputs. These findings unveil a novel cross-modal gating mechanism for sensory pathways (Pan, 2023).

Evolution has generated an enormous variety of morphological, physiological, and behavioral traits in animals. How do behaviors evolve in different directions in species equipped with similar neurons and molecular components? This study adopted a comparative approach to investigate the similarities and differences of escape behaviors in response to noxious stimuli and their underlying neural circuits between closely related drosophilid species. Drosophilids show a wide range of escape behaviors in response to noxious cues, including escape crawling, stopping, head casting, and rolling. This study found that D. santomea, compared with its close relative D. melanogaster, shows a higher probability of rolling in response to noxious stimulation. To assess whether this behavioral difference could be attributed to differences in neural circuitry, focused ion beam-scanning electron microscope volumes of the ventral nerve cord of D. santomea were generated to reconstruct the downstream partners of mdIV, a nociceptive sensory neuron in D. melanogaster. Along with partner interneurons of mdVI (including Basin-2, a multisensory integration neuron necessary for rolling) previously identified in D. melanogaster, two additional partners of mdVI were identified in D. santomea. Finally, this study showed that joint activation of one of the partners (Basin-1) and a common partner (Basin-2) in D. melanogaster increased rolling probability, suggesting that the high rolling probability in D. santomea is mediated by the additional activation of Basin-1 by mdIV. These results provide a plausible mechanistic explanation for how closely related species exhibit quantitative differences in the likelihood of expressing the same behavior (Zhu, 2023).

To effectively control their bodies, animals rely on feedback from proprioceptive mechanosensory neurons. In the Drosophila leg, different proprioceptor subtypes monitor joint position, movement direction, and vibration. This study investigate how these diverse sensory signals are integrated by central proprioceptive circuits. Signals for leg joint position and directional movement converge in second-order neurons, revealing pathways for local feedback control of leg posture. Distinct populations of second-order neurons integrate tibia vibration signals across pairs of legs, suggesting a role in detecting external substrate vibration. In each pathway, the flow of sensory information is dynamically gated and sculpted by inhibition. Overall, these results reveal parallel pathways for processing of internal and external mechanosensory signals, which are proposed to mediate feedback control of leg movement and vibration sensing, respectively. The existence of a functional connectivity map also provides a resource for interpreting connectomic reconstruction of neural circuits for leg proprioception (Chen, 2021).

This study reports the anatomical structure and functional organization of second-order circuits for leg proprioception in Drosophila. Due to the lack of clear hierarchical structure within the VNC leg neuropil, it has been challenging to infer the flow of proprioceptive sensory signals with existing tools. Therefore genetic driver lines were generated that label specific subtypes of leg proprioceptors and classified candidate second-order neurons based on hemilineage identity. Optogenetics and calcium imaging were used to map the functional connectivity between leg proprioceptors and second-order neurons, followed by EM reconstruction to validate synaptic connectivity and in vivo calcium imaging to understand the function of second-order neurons during leg movement. Spatially targeted and subtype-specific optogenetic stimulation were used to analyze integration of FeCO signals within a subset of second-order neuron classes (Chen, 2021).

Overall, this work reveals the logic of sensory integration in second-order proprioceptive circuits: some populations of second-order neurons integrate tibia vibration signals across pairs of legs, suggesting a role for detection of external substrate vibration. Signals for leg joint position and directional movement converge in other second-order neurons, revealing pathways for local feedback control of leg posture. It is anticipated that this functional wiring diagram (see Summary diagram of circuits processing leg proprioceptive signals from the Drosophila FeCO, based on experiments in this study) will also help guide the interpretation of anatomical wiring diagrams determined through EM reconstruction of VNC circuits (Chen, 2021).

Proprioceptors in the Drosophila FeCO can be classified into three subtypes: club neurons encode bidirectional tibia movement and vibration frequency; claw neurons encode tibia position (flexion or extension); and hook neurons encode the direction of tibia movement.19 Our results show the existence of two distinct central pathways for processing signals from club vs. claw and hook neurons (see Summary diagram of circuits processing leg proprioceptive signals from the Drosophila FeCO, based on experiments in this study). It is proposed that neurons downstream of the club mediate sensing of small mechanical vibrations in the external environment, whereas neurons downstream of the claw and hook provide proprioceptive feedback to motor circuits for controlling the posture and movement of the legs. This division of central pathways for external and internal sensing may be a common motif across limbed animals. Work in a variety of species, including a recent study in mice, has found that many animals can detect low-amplitude, high-frequency, substrate-borne vibrations (Chen, 2021).

Flies may use vibration sensing to monitor acoustic signals in the environment, such as during courtship behavior, or to detect approaching threats. The distinct anatomical organization of neurons downstream of the club vs claw and hook also supports a segregation of vibration sensing and motor control feedback pathways. 9Ba and 10Ba neurons arborize in the mVAC, a common target of descending neuron axons. In contrast, 13Bb arborize in the IntNp, which contains the dendritic branches of the leg motor neurons and premotor interneurons. Based on these differences, it was hypothesized that vibration-sensing neurons interact with ascending and descending signals to and from the brain, whereas neurons downstream of hook and claw axons contribute to local motor control through direct or indirect connections to motor neurons. Leg motor neurons receive position- and movement-tuned proprioceptive input, consistent with feedback from claw and hook neurons (Chen, 2021).

Additional connectomic reconstruction is needed to determine which second-order neurons mediate these feedback connections, but 13Bb neurons are promising pre-motor candidates. VNC neurons postsynaptic to claw and hook axons receive only local input, from individual legs. In contrast, second-order neurons postsynaptic to club axons integrate signals across multiple legs. For example, GABAergic 9Bb neurons pool information from left and right legs in a single VNC segment, whereas cholinergic 10Ba neurons receive convergent input from left and right legs across different segments. Integrating club input across legs may improve detection of external vibration signals, while proprioceptive signals from the claw and hook may be initially processed in parallel to support postural control of individual legs. Bilateral integration also occurs in second-order auditory circuits downstream of the Drosophila Johnston's organ: mechanosensory signals from the two antennae are processed in parallel by second-order neurons in the AMMC but then converge in third-order neurons in the wedge (Chen, 2021).

Although second-order neurons in the vibration pathway integrate club signals across legs, multiple classes of second-order neurons in the motor pathway (13Bb and 8Aa) integrate signals across different FeCO subtypes from the same leg. Using new genetic driver lines that subdivide claw neurons into extension- and flexion-tuned subtypes, it was found that extension-tuned claw and hook neurons converge on 13Bb neurons. These cells are hypothesized to mediate resistance reflexes that stabilize tibia position in response to external perturbations. Prior work in the stick insect has shown that tibia resistance reflexes rely on position and directional movement signals from the FeCO (Chen, 2021).

In support of this hypothesis, another class of neurons in the 13B hemilineage, 13Ba, also encode tibia extension and drive tibia flexion when optogenetically activated. This functional connectivity map reveals interesting parallels with sensorimotor circuitry in the larval Drosophila VNC. Although fly larvae do not have legs, they do possess body wall proprioceptors (class I sensory neurons) and use chordotonal neurons to sense external vibrations in a manner analogous to club neurons in the adult FeCO. As in the adult, larval neurons belonging to lineage 9 (basin neurons) and lineage 8 (eve lateral interneurons) integrate signals from chordotonal sensory neurons and proprioceptors. These examples suggest that some lineage connectivity motifs are likely conserved across the larval and adult nervous systems, which are already known to possess molecular and general anatomical similarities (Chen, 2021).

The results identify a prominent role for inhibition in central processing of proprioceptive information from the FeCO. Of the eight identified second-order cell classes, six are putative inhibitory neurons (i.e., release GABA or glutamate). In other sensory circuits, local inhibitory processing contributes to sharpening spatial and temporal dynamics as well as reducing sensory noise through crossover inhibition (Chen, 2021).

By pharmacologically blocking GABAa and GluCl receptors, we identified a role for inhibition in controlling adaptation within second-order neurons (e.g., 10Ba and 13Bb neurons; Figure 5C). In other cases (20/22Ab or 9Ba; Figure 5A), inhibition was strong enough to completely mask proprioceptive inputs from FeCO axons. We hypothesize that this inhibition may be tuned in certain behavioral contexts, for example, during active movements, to gate the flow of proprioceptive feedback signals in a context-dependent manner. Synaptic transmission in Drosophila can be mediated by chemical synapses, which can be visualized with EM, or electrical gap junctions, which are not typically identifiable at the resolution of current EM volumes. FeCO neurons release acetylcholine but also express gap junction proteins (shakB; data not shown). We therefore used pharmacology to test for the presence of gap junctions between sensory and central neurons. MLA, an effective antagonist of nicotinic acetylcholine receptors in Drosophila eliminated functional connectivity between club and 9Bb neurons but only reduced functional connectivity between club and 10Ba neurons. We observed similar results downstream of the claw: MLA blocked functional connectivity between claw and 13Bb neurons but only reduced functional connectivity between claw and 13Ba neurons (data not shown). These results suggest that second-order proprioceptive circuits receive mixed chemical and electrical input from FeCO neurons. More work is needed to confirm these observations and to investigate the functional significance of why pathways might use one means of signal transmission over the other. One hypothesis is that chemical synapses exhibit adaptation (e.g., synaptic depression), whereas electrical synapses may be more advantageous for sustained synaptic transmission (Chen, 2021).

Each may provide different advantages for pathways that control behavior on a variety of timescales, from slow postural reflexes to rapid escape behaviors (Chen, 2021).

Central processing of sensory signals from the FeCO has been previously studied in other insects, especially the locust and stick insect. In both species, second-order interneurons encode combinations of tibia movement and position and also integrate multimodal signals from different proprioceptive organs (Chen, 2021).

Vibration signals detected by the FeCO appear to be processed by largely segregated populations of VNC interneurons. However, these conclusions were based on mapping of sensory receptive fields, and it was not previously possible to identify specific sources of sensory input, as is done in this study. Overall, comparison of our functional connectivity results in Drosophila with the prior work in other insect species suggests general evolutionary conservation of VNC circuits for leg proprioception and motor control. Although it is currently difficult to identify homologous cell types across insect species, future efforts could leverage conserved developmental programs: the organization of neuroblasts that give rise to the VNC is similar across insect species separated by 350 Ma of evolution (Chen, 2021).

This is an important advantage of using developmental lineages to define VNC cell classes-locusts and stick insects also possess 9A, 10B, and 13B neurons, which could someday be identified based on molecular markers of lineage identity (Chen, 2021).

The functional connectivity approach that was employed in this study has both benefits and drawbacks. On the positive side, it allowed screening a large connectivity matrix of genetically identified sensory and central neurons. Compared to other methods for anatomical mapping (e.g., EM), the use of optogenetics and calcium imaging allowed measuring of connection strength and dynamics across multiple individuals. It was found that second-order VNC neurons varied significantly in their functional connectivity strength and temporal dynamics. 5-fold differences were observed in peak calcium signals in response to optogenetic stimulation with the same light intensity. This range could be due to differences in GCaMP expression or intracellular calcium buffering, but could also reflect differences in synaptic strength between pre- and postsynaptic partners (Chen, 2021).

One limitation of functional connectivity is that it is not possible to measure all possible combinations of pre- and postsynaptic partners. For example, a previous study provided evidence that 9Aa neurons receive input from hook and club neurons, which was not observed in the current screen. This discrepancy could be due to the fact that the driver lines that were used do not label the specific subset of hook and club cells presynaptic to 9Aa neurons. Alternatively, it may be due to differences in signal transmission driven by optogenetic stimulation versus natural tibia movements, as was the case for 9Bb neurons (Chen, 2021).

Functional connectivity mapping also cannot resolve whether inputs are direct, due to the slow kinetics of GCaMP6. This study therefore used sparse, targeted EM tracing to validate some of the functional connections that were identified between FeCO and VNC neurons. A more detailed comparison of functional and anatomical connectivity will require dense, comprehensive reconstruction of the VNC neuropil. Automated reconstruction and manual proofreading have recently led to draft wiring diagrams of neural circuits in the adult Drosophila central brain (Chen, 2021).

Nervous systems have the ability to select appropriate actions and action sequences in response to sensory cues. The circuit mechanisms by which nervous systems achieve choice, stability and transitions between behaviors are still incompletely understood. To identify neurons and brain areas involved in controlling these processes, a large-scale neuronal inactivation screen was combined with automated action detection in response to a mechanosensory cue in Drosophila larva. Behaviors were analyzed from 2.9x105 larvae, and 66 candidate lines were identified for mechanosensory responses out of which 25 for competitive interactions between actions. The neurons in these lines were further characterized in detail and their connectivity was analyzed using electron microscopy. The neurons in the mechanosensory network were found to be located in different regions of the nervous system consistent with a distributed model of sensorimotor decision-making. These findings provide the basis for understanding how selection and transition between behaviors are controlled by the nervous system (Masson, 2020).

In order to identify neurons and brain regions underlying competitive interactions and transitions between actions during mechanosensory responses, a high-throughput inactivation screen was performed where individual neurons and groups of neurons were silenced (using tetanus-toxin) in 567 genetic GAL4 lines in Drosophila larva, and the effects of these manipulations on larval behavioral responses to a mechanosensory cue were examined (Masson, 2020).

The behavioral response of wild-type larvae to the stimulus (air-puff) were characterized and larvae were found to perform a probabilistic sequence of five different actions. An automated approach was developed and used that detects and distinguishes five different discrete behaviors that larvae perform in response to the air-puff. Evidences suggest that the discrete action description is relevant when compared to a continuum approach as parameters associated to larva dynamics tend to naturally cluster. The representation is found to be stable even for large number of larvae while their characteristics (amplitude of actions, duration, size of the larva, shape etc.) can vary significantly. Yet it is pointed out that it does not mean that all behaviors and actions that larvae exhibit are necessarily described as only discrete actions (Masson, 2020).

This analysis was used to describe phenotypes that result from manipulation of different populations of neurons or single neuron types. Phenotypes were found that are consistent with a specific role of neurons in sensory processing or motor control, competitive interactions, and sequence transitions. Neuronal expression data for all of the GAL4 lines used in this screen have been previously published). The number of neurons that were targeted in the tested lines varies from 1 to 7 pairs on average and smaller number of the GAL4 lines the driver is restricted to a single neuron type. The morphology of top hits were analyzed in more detail using single-cell FLP-out and their connectivity was analyzed using electron-microscopy reconstruction (see Putative pathways in the mechanosensory network)(Masson, 2020).

A framework was developed for selectively identifying circuit elements underlying competitive interactions and sequence transitions. Sensory-processing, sensorimotor decisions, and sequence generation are intertwined processes as the latter two will depend on how the sensory information is processed, and the sequence production mechanistically might depend on competitive interaction between distinct actions as suggested by models of sequence generation like competitive queuing or chains of disinhibitory loops. Nevertheless, the reasoning described below was used to identify neurons selectively involved in competitive interactions that underlie sensorimotor decisions and sequence generation (Masson, 2020).

It was reasoned that, if the stimulus cannot be processed and thus perceived accurately the animals might respond less, by performing less of all or some of the actions. If the sensory processing is affected in the opposite way (hypersensitivity), animals might respond more, and perform more of all or some of the actions normally observed. Thus, the neurons that gave such inactivation phenotypes (of less of one or more actions; or more of one or more actions) could be involved in any aspect of sensory processing or motor control. It cannot be excluded that these larvae responded less because the inactivation of the neurons modulates the overall animal state (Masson, 2020).

However, inactivation of neurons involved in mediating competition between actions is expected to result in increased probability of one action and a decreased probability of one or more other actions (or the converse) as the neuron implementing the competitions will promote one action while inhibiting competing options. Based on this logic, 25 hits (GAL4 lines) were identified that were top candidates for selectively mediating affected competitive interactions. Morphologically the neurons in these lines were characterized using light microscopy of multicolor flip-out and for some of the neurons determined their connectivity by identifying them in the electron microscopy volume. It was found that some of these neurons received input from chordotonal sensory neurons, chordotonal related interneurons or multidendritic class III sensory neurons while others were pre-motor neurons. In addition, other neurons were found that project to or are located in the brain. Taken altogether, the GAL4 lines that were identified as hits drive in neurons that are located in the ventral nerve cord (both abdomen and thorax region), suboesophageal ganglion and brain. This suggests that the networks for competitive interactions between actions that occur in response to air-puff are distributed across the nervous system (Masson, 2020).

The idea that sensorimotor decisions are made 'through a distributed consensus that emerges in competitive populations' and that interactive behaviors require sensorimotor and selection system to function in parallel have emerged in various fields, but it has been challenging to elucidate the neuronal architecture that would implement such sensorimotor decisions. The Drosophila larva, because of its numerical simplicity, small size and the existence of multiple experimental approaches for structural and functional connectivity studies, behavioral genetics, optogenetics etc. is an ideal system for investigating how the outcomes of these competitive interactions at the different sites are integrated across the nervous system to give rise to coherent sensorimotor behaviors (Masson, 2020).

The neural architecture that controls the productions of probabilistic action sequences and establishes the order of the individual elements in the sequence is also poorly understood. This study identified a number of hit line phenotypes that were consistent with an implication of the neurons in ensuring proper ordering of individual elements in the sequence. For example, the neurons in the R45D11 line could be inhibiting reversals from Bend to Crawl and promoting transitions from Bend to Back, while neurons in the R69E06 line could be promoting transitions from Bend to Back-up while preventing reversals from Bend to Hunch. In previous work on a two- element Hunch-Bend sequence in response to an air-puff, it was proposed that transitions to the next element in the sequence and reversal to the previous element are controlled through two different motifs: lateral disinhibition from the neuron driving one behavior onto the neuron driving the following behavior and feedback disinhibition that provides a positive feedback that stabilizes the second behavior and prevents reversals back onto the previous actions (Jovanic, 2016).

It was speculated that chains of such disinhibitory loops could be a general mechanism for generating longer behavioral sequences. In the case of longer sequences (more than two elements) the maintenance of a selected action (through a positive feedback) after the transition from the previous action has occurred would need to be balanced with promoting the transition from the current onto the following action in the sequence. The candidate neurons in the R45D11 and the R69E06 could represent a starting point for investigating these mechanisms as their phenotype are consistent with preventing reversals from Bend to Hunch and Crawl and promoting transitions from Bend to Back-up (that represent nearly 80% of transitions from Bend). Another category of phenotypes, increase in transitions from Hunch to Back-up and decreased from Hunch to Bend, suggests that asymmetric competitive interactions exist between transitions to Bend and Back-up (from Hunches) where the transitions from Hunch to Bend inhibit transitions from Hunch to Back-up but not the other way around. Such a mechanism would allow a progression of a sequence in a probabilistic way where the transitions from Hunch to Bend are more likely (50%) than to Back-up (less than 15%) (Masson, 2020).

In summary, this screen provides a roadmap for investigating the neural circuit mechanisms underlying the different computations during mechanosensory responses. It also offers a starting point for identifying the mechanisms underlying the competitive interactions between behaviors as well as the transition between individual actions in probabilistic sequences across the nervous system. While the number of neurons that were targeted in the tested lines varies from one to seven pairs on average, and sometimes more, in the case when the lines label multiple neuron types, intersectional strategies can be used to further refine the expression patterns. In the larva, a volume of electron microscope data has been acquired and more than 60% of the nervous system has been reconstructed through collaborative efforts. The synaptic partners of the identified candidate neurons can therefore be further reconstructed in the electron microscopy volume. Combined with EM reconstruction, physiology, and modeling the candidate lists of neurons can be used to relate circuit structure and function across the nervous system and unravel the principles of how the nervous system selects actions and produces action sequences in response to external stimuli (Masson, 2020).

Proprioceptors provide feedback about body position that is essential for coordinated movement. Proprioceptive sensing of the position of rigid joints has been described in detail in several systems; however, it is not known how animals with a flexible skeleton encode their body positions. Understanding how diverse larval body positions are dynamically encoded requires knowledge of proprioceptor activity patterns in vivo during natural movement. This study used high-speed volumetric swept confocally aligned planar excitation (SCAPE) microscopy in crawling Drosophila larvae to simultaneously track the position, deformation, and intracellular calcium activity of their multidendritic proprioceptors. Most proprioceptive neurons were found to activate during segment contraction, although one subtype was activated by extension. During cycles of segment contraction and extension, different proprioceptor types exhibited sequential activity, providing a continuum of position encoding during all phases of crawling. This sequential activity was related to the dynamics of each neuron's terminal processes, and could endow each proprioceptor with a specific role in monitoring different aspects of body-wall deformation. This study demonstrates this deformation encoding both during progression of contraction waves during locomotion as well as during less stereotyped, asymmetric exploration behavior. The results provide powerful new insights into the body-wide neuronal dynamics of the proprioceptive system in crawling Drosophila, and demonstrate the utility of the SCAPE microscopy approach for characterization of neural encoding throughout the nervous system of a freely behaving animal (Vaadia, 2019).

This study demonstrates a new approach for live volumetric imaging of sensory activity in behaving animals, leveraging an optimized form of high-speed SCAPE microscopy. This methodology was used to examine the activity patterns of a heterogeneous collection of proprioceptive neurons during crawling, as well as during more complex movements such as head turning and retraction, to determine how larvae sense body-shape dynamics. Imaging revealed 3D distortion of proprioceptive dendrites during movement and GCaMP activity that occurred coincident with dendritic deformations. It is noted that the results are consistent with a complementary study (He, 2019), which examined ddaD and ddaE dorsal proprioceptors and also demonstrated increased activity during dendrite folding. The He study elucidated that this deformation-dependent signaling is reliant on the mechanosensory channel TMC (Vaadia, 2019).

This survey of the full set of hypothesized multidendritic proprioceptors in behaving larvae revealed that most neurons (all class I neurons, dmd1, and vbd) increase activity during segment contraction. By contrast, dbd neurons showed increased activity during segment stretch, which is consistent with previous electrophysiological recordings of dbd in a dissected preparation. The temporal precision afforded by high-speed SCAPE microscopy further revealed that different proprioceptors exhibit sequential onset of activity during forward crawling. Timing of activity was associated with distinct dendrite morphologies and movement dynamics, suggesting that proprioceptors monitor different features of segment deformation. The complementary sensing of segment contraction versus stretch in class I, dmd1, and vbd versus dbd neurons provides an additional measure of movement that is conceptually similar to the responses of Golgi tendon organs versus muscle spindles in mammals. Combined, these results indicate that this set of proprioceptors function together to provide a continuum of sensory feedback describing the diverse 3D dynamics of the larval body (Vaadia, 2019).

Prior work suggested that the proprioceptors analyzed in this study have partially redundant functions during forward crawling because silencing different subsets caused similar behavioral deficits, namely slower crawling, whereas silencing both subsets had a more severe effect. Slow locomotion may be a common outcome in a larva that is lacking in part of its sensory feedback circuit, yet the results suggest that each cell type has a unique role. The demonstration of the varying activity dynamics of proprioceptors during crawling and more complex movements indicates that diverse sensory information is available to the larva, and suggests that feedback from a combination of these sensors could be used to infer aspects of speed, angle, restraint, and overall body deformation. This feedback system is likely to be important for a wide range of complex behaviors, such as body bending and nociceptive escape (Vaadia, 2019).

How can an understanding of proprioceptor activity patterns inform models of sensory feedback during locomotion? Electron microscopic reconstruction has shown that ddaD, vbd, and dmd1 proprioceptors synapse onto inhibitory premotor neurons (period-positive median segmental interneurons, A02b), which promote segment relaxation and anterior wave propagation. Thus, activity of these sensory neurons may signal successful segment contraction and promote forward locomotion, in part by promoting segment relaxation. Furthermore, vpda neurons provide input onto excitatory premotor neurons A27h, which acts through GABAergic dorsolateral (GDL) interneurons to inhibit contraction in neighboring anterior segments, thereby preventing premature wave propagation. In this way, vpda feedback could contribute to proper timing of contraction in anterior segments during forward crawling. In contrast to other proprioceptors, dbd neurons are active during segment stretch. Their connectivity also tends to segregate from contraction-sensing neurons, and understanding how the timing of this input promotes wave propagation is an important future question. This study's dynamic recordings of the function of these neurons during not just crawling but also exploration behavior provide essential new boundary data for testing putative network models derived from this anatomical roadmap (Vaadia, 2019).

SCAPE's high-speed 3D imaging capabilities enabled 10 VPS imaging of larvae during rapid locomotion. Fast volumetric imaging not only prevented motion artifacts but also revealed both the 3D motion dynamics and cellular activity associated with crawling behavior. SCAPE's large, 1-mm-wide field of view allowed multiple cells along the larva to be monitored at once, while providing sufficient resolution to identify individual dendrite branches. Because SCAPE data are truly 3D, dynamics could be examined in any section or view. Additionally, fast two-color imaging enabled simultaneous 3D tracking of cells, monitoring of GCaMP activity, and correction for motion-related intensity effects. The demonstration that larvae that are compressed during crawling exhibit altered dendrite deformation, and thus altered proprioceptive signaling, underscores the benefit of being able to image unconstrained larvae, volumetrically, in real time. Furthermore, rapid volumetric imaging allowed for the analysis of sensory responses during non-stereotyped, exploratory head movements in 3 dimensions, revealing activity patterns that could be utilized for encoding of complex, simultaneous movements. This finding also demonstrates the quantitative nature of SCAPE data and its high signal to noise, which enabled real-time imaging of neural responses without averaging from multiple neurons (Vaadia, 2019).

This study provides an example of how high-resolution, high-speed volumetric imaging enabled investigation of the previously intractable question of how different types of proprioceptive neurons encode forward locomotion and exploration behavior during naturalistic movement. Imaging could readily be extended to explore a wider range of locomotor behaviors such as escape behavior, in addition to other sensory modalities such as gustation and olfaction. Detectable signals reveal rich details including the firing dynamics of dendrites and axonal projections during crawling. Waves of activity in central neurons within the ventral nerve cord can also be observed. It is expected that the in vivo SCAPE microscopy platform utilized in this study could ultimately allow complete activity mapping of sensory activity during naturalistic behaviors throughout the larval CNS. Using SCAPE, it is conceivable to assess how activity from proprioceptive neurons modulates central circuits that execute motor outputs, which will provide critical information for a dissection of the neural control of behavior with whole-animal resolution (Vaadia, 2019).

Sensory stimuli with graded intensities often lead to yes-or-no decisions on whether to respond to the stimuli. How this graded-to-binary conversion is implemented in the central nervous system (CNS) remains poorly understood. This study shows that graded encodings of noxious stimuli are categorized in a decision-associated CNS region in the ventral cord of Drosophila larvae, and then decoded by a group of peptidergic neurons for executing binary escape decisions. GABAergic inhibition gates weak nociceptive encodings from being decoded, whereas escalated amplification through the recruitment of second-order neurons boosts nociceptive encodings at intermediate intensities. These two modulations increase the detection accuracy by reducing responses to negligible stimuli whereas enhancing responses to intense stimuli. These findings thus unravel a circuit mechanism that underlies accurate detection of harmful stimuli (Hu, 2020).

This study identified a neural network that categorizes noxious stimuli of graded intensities to generate binary escape decisions in Drosophila larvae, and a gated amplification mechanism was unraveled that underlies such binary categorization. In responding to the noxious stimuli, whereas failure in prompt responses may cause harm, excessive escape responses to negligible stimuli would lead to the loss of resources for survival. The gated amplification mechanism could reduce the responses to negligible stimuli whereas enhancing the responses to intense stimuli. In this way, the accuracy in deciding whether to escape from the stimuli is enhanced (Hu, 2020).

Information processing in the nervous system is affected by noise, which may be embedded in external sensory stimuli (e.g., sensory noise) or generated within the nervous system (e.g., electric noise). A recent study in C. elegans shows that activation mediated by electrical synapses and disinhibition mediated by glutamatergic chemical synapses form an AND logic gate to integrate the presentation of the salience of attractive odors. The AND-gate computation in worm AIA interneurons requires multiple sensory neurons to report the presence of attractive odors and, consequently, filters out the noise embedded in the sensory stimuli. Another study on the olfactory system of adult Drosophila reported a mechanism to address the noise that is produced within the nervous system. A three-layered feedforward network averages the noise to enhance peak detection accuracy and then uses coincidence detection to distinguish real signals arrived synchronously from noise caused by spontaneous neural activities. In the nervous system, the noise can be produced at each stage of the sensori-motor transformation. Compared with the two mechanisms mentioned above, which filter out the existing noise, the graded-to-binary conversion through the gated amplification mechanism reported in this study makes the converted signals less vulnerable to the noise produced at later stages of sensori-motor transformation. This is because after the graded signals become binary, the signals are more separated (either suppressed or amplified) according to stimulus intensities and, consequently, the same level of noise is less likely to cause the binary signals to falsely pass the decision threshold than the graded ones. As a result, the ambiguous encoding range of the stimulus intensity is narrowed and the frequency of false decisions is reduced, as demonstrated by computational modeling (Hu, 2020).

Thresholding of gradually accumulated sensory evidence has been considered to be fundamental for generating yes-or-no decisions. For example, a recent study in mammals has shown that visual evidence of danger can be gradually accumulated by recurrent circuits to overcome the threshold for escape behaviors. Such a mechanism takes time to build up decision-associated activities for decisions with higher accuracy, which leads to the well-known speed-accuracy trade-off in perceptual decision making. However, the current findings add a new dimension to the processing of sensory evidence for perceptual decision making: different from recurrent networks, the recruitment of a number of SONs can instantaneously boost the decision-associated activity to reach the decision threshold, which ensures decision speed. Because the gated amplification mechanism reported here also ensures the detection accuracy, such a mechanism might bypass the speed-accuracy trade-off in sensory signal detection (Hu, 2020).

An electron microscopy connectome study reported 13 types of second-order neurons (SONs) in the Drosophila larval nociceptive system, each of which has distinct connectivity and functions. For example, Basin-4, DnB, and Wave neurons also receive mechanosensory inputs, whereas A08n does not. Moreover, Wave neurons detect stimulus positions on larval body walls. Furthermore, serotonergic modulation acts on this network during development to adjust the nociceptive responses, providing a mechanism for larvae to adjust the escape threshold according to their developmental environment. However, because at least 5 types of SONs are both required and sufficient for larval escape behaviors, it remains a mystery why there exist so many seemingly redundant neurons at the same level in the network. The nociceptive system is a dedicated protective system that responds to potential tissue-damaging insults, so both speed and accuracy of the perceptual decision-making process are important. This is probably why the nociceptive system uses an amplification network formed by a large number of SONs to dissociate time from accuracy in the perceptual decision-making process and avoid the trade-off between decision speed and accuracy (Hu, 2020).

Novel unbiased computational toolsets were developed for automatically analyzing the functional connectivity of all neural structures, including both somas and neurites in the larval VNC. Using these toolsets, a decision-associated CNS region, the PMC, was identified that covers the neuropil structure TP. The TP is concentrated with large amounts of neurites, especially those of peptidergic neurons. Although this anatomical structure was identified previously, its function is unknown. The finding of this study of its important function in sensori-motor transformation suggests that this region is possibly a hub for information exchange and integration. The detailed anatomical and functional connectivity of the TP could be a fascinating direction for future studies (Hu, 2020).

In summary, this study postulates a neural basis for converting graded sensory inputs to yes-or-no behavioral decisions. A previous study showed that neurons in the rat posterior parietal cortex encode a graded value of accumulating evidence whereas those in the prefrontal cortex have a more categorical encoding that indicates the decisions. Thus, the categorization of sensory evidence by making graded encodings binary in perceptual decision making is likely an evolutionarily conserved process. In this study, advantage was taken of the powerful genetic model Drosophila to unravel how such computation might be implemented at the cellular and molecular level. Finally, because whole-CNS functional imaging analysis is a key approach to decipher the neural basis for sensori-motor integration and perceptual decision making, it is anticipated that the computational tools developed in this study will facilitate investigations in these fields (Hu, 2020).

The mechanisms by which synaptic partners recognize each other and establish appropriate numbers of connections during embryonic development to form functional neural circuits are poorly understood. This study combined electron microscopy reconstruction, functional imaging of neural activity, and behavioral experiments to elucidate the roles of (1) partner identity, (2) location, and (3) activity in circuit assembly in the embryonic nerve cord of Drosophila. Postsynaptic partners were found to be able to find and connect to their presynaptic partners even when these have been shifted to ectopic locations or silenced. However, orderly positioning of axon terminals by positional cues and synaptic activity is required for appropriate numbers of connections between specific partners, for appropriate balance between excitatory and inhibitory connections, and for appropriate functional connectivity and behavior. This study reveals with unprecedented resolution the fine connectivity effects of multiple factors that work together to control the assembly of neural circuits (Valdes-Aleman, 2020).

The human nervous system is organized into circuits with specifically matched and tuned cell-to-cell connections essential for proper function. During development, neurons navigate through the nervous system to reach their target location. Surrounded by numerous cells along their trajectories and in their target areas, developing neurons ignore most cells and connect only to specific partners (Valdes-Aleman, 2020).

The absolute numbers of synapses between specific partners can vary across individuals, hemispheres, or repeated network modules in the same individual. However, recent electron microscopy (EM) reconstructions in multiple Drosophila larvae suggest that, at least in some circuits, the relative numbers of synapses between partners are precisely regulated. Thus, the fraction of inputs a neuron receives from a specific partner, relative to its total number of inputs, is remarkably conserved across individuals, across larval stages, and even between larva and adult. For example, the fraction of input varied by an average factor (fold change; i.e., the ratio of two fractions) of 1.07 ± 0.22 between different first instar larvae (n = 13 homologous connections) and 1.09 ± 0.20 from first to third instar (n = 12 homologous connections). Similarly, the average input a mushroom body output neuron receives from a modulatory neuron in the larva and adult is 3.4% and 3.3%, respectivelyh. These examples of conserved fractions of synaptic input across individuals and life stages raise several key questions: (1) How important are the precise numbers of connections between neurons for normal behavior? (2) How are the precise numbers of connections between partners specified? and (3) How is the appropriate balance between excitatory and inhibitory connections in the circuit achieved (Valdes-Aleman, 2020)?

The chemoaffinity hypothesis proposes that pre- and postsynaptic partners express specific matching combinations of cell surface molecules that enable them to seek out and recognize each other during development. However, relatively few examples of partner-recognition molecules have been identified, so it is unclear whether their use is a general principle or if they are used only in some systems. It is also unknown if these partner-recognition mechanisms specify precise numbers of synapses between partners, or only instruct two neurons to form synapses, but not how many (Valdes-Aleman, 2020).

Alternative hypotheses propose that neurons seek out specific locations in the nervous system, rather than specific partners, indiscriminately connecting to whichever neurons are present there. Consistent with this, neurons have been shown to use non-partner-derived positional cues, such as third-party guidepost cells or gradients of repellents, to select their termination and synaptogenesis area independently of their partners. Additionally, activity-dependent mechanisms are thought to refine connections through Hebbian and/or homeostatic plasticity mechanisms. Neurons that fire together preferentially wire together in many areas of the vertebrate nervous system through positive feedback. At the same time, homeostatic mechanisms restore activity toward a specific set point through negative feedback, imposing competition and preventing runaway excitation or complete silencing of the circuit. However, the extent to which activity modulates numbers versus the strength of existing synapses is still an open question (Valdes-Aleman, 2020).

These questions have been difficult to address because they require manipulating candidate factors that could influence connectivity, visualizing synapses between uniquely identified partners, and relating observed structural changes to effects on functional connectivity and behavior. This study therefore used the tractable Drosophila larva as a model system with the following advantages: (1) excellent genetic tools for selective manipulation of uniquely identified neurons; (2) a compact nervous system amenable to rapid imaging with synaptic resolution, and (3) a rich behavioral repertoire with well-established quantitative assay (Valdes-Aleman, 2020).

Recently, comprehensive synaptic-resolution connectivity maps of the circuitry downstream of the mechanosensory Chordotonal (hereafter 'mechanosensory') neurons and nociceptive multidendritic class IV (hereafter 'nociceptive') neurons in an abdominal segment of a first instar larva have been generated. Portions of this circuit were also reconstructed in two different abdominal segments (A1 and A3) of two different first instar individuals and at two different life stages: first (A1) and third instar (A3) (Valdes-Aleman, 2020).

This study selectively altered the location or activity of the mechanosensory neurons and generated new EM volumes of the manipulated samples to investigate the effects on connectivity. These anatomical studies were completed with functional connectivity and behavioral assays. This study reveals that proper location, partner identity, and activity are all required to achieve appropriate connectivity and behavior (Valdes-Aleman, 2020).

In some systems the position of pre- or postsynaptic terminals is specified by non-partner-derived positional cues. In other systems, molecules have been identified that mediate partner matching. However, it was unclear whether both mechanisms could operate in the same system and whether either mechanism specifies numbers of connections between partners (Valdes-Aleman, 2020).

Although developing sensory axons use non-partner-derived positional cues to select their final termination area in the Drosophila nerve cord, the current results suggest that position alone does not specify connectivity and that partner recognition also exists. When the location of sensory axons was altered, their postsynaptic partners extended ectopic branches and formed synaptic connections with them. The shifted axons did not gain any new strongly connected partners at their ectopic location, providing further evidence of remarkable partner selectivity. It is hard to imagine which cue, other than the mechanosensory axons themselves, instructed partner dendrites to form these ectopic branches and synapses. Nevertheless, the final proof of the existence of the partner-derived cues will be their identification in the future (Valdes-Aleman, 2020).

If partner-recognition molecules are sufficient for selective synaptogenesis irrespective of the location of partners, why is the precise location of sensory neuron axon terminals so tightly regulated by non-partner-derived positional cues? Despite partner neurons' connecting in ectopic locations, they did not establish appropriate numbers of synapses, resulting in defective responses to mechanosensory stimuli. This indicates that precise positioning of presynaptic mechanosensory axons is necessary for the formation of appropriate number of synapses (Valdes-Aleman, 2020).

It is not known why some partners received more synapses from shifted mechanosensory axons and others fewer than in wild-type. One possibility could be the involvement of third-party guidepost cells in synaptogenesis which would not be present in the aberrant location. Another speculation is that some neurons are better than others at finding their misplaced partners. Yet another possibility could be that shifting mechanosensory neurons initially resulted in fewer or weaker synaptic connections. This could have triggered compensatory homeostatic changes in the balance of excitation and inhibition within the circuit by increasing mechanosensory connections onto excitatory interneurons and reducing those onto inhibitory interneurons. This latter possibility could explain why similar connectivity effects were observed when sensory neurons were shifted and when they were inactivated during development (Valdes-Aleman, 2020).

Finally, in addition to changes in synapse numbers, silencing or shifting presynaptic partners could have also induced changes in synaptic strength and electrical properties (e.g., through changes in ion channel composition) that could account for some of the observed effects in behavior and functional connectivity. Furthermore, changes in the shapes of arbors could potentially affect electrical signal propagation. Future patch-clamp recordings following the same experimental manipulations could reveal the extent to which this occurs (Valdes-Aleman, 2020).

Activity plays a major role in refining the patterns of neuronal connections during development, especially in vertebrates. However, the effects induced within the network in response to selective silencing of specific neuron types are not fully understood (Valdes-Aleman, 2020).

The role activity plays in the development of the insect central nervous system is less clear. Some studies have shown that a lack of sensory activity during development does not affect neuron morphology or the capacity to form connections. Other studies have reported neural circuits can adapt their morphology, connectivity, or behavior in response to changes in developmental activity. However, a comprehensive synaptic-resolution analysis of the effects of silencing a specific neuron type on the numbers of connections between partners was lacking (Valdes-Aleman, 2020).

EM reconstructions revealed that silenced mechanosensory neurons connected to the appropriate partners, but with inappropriate numbers of synapses. Interestingly, excitatory multisensory interneurons (Basin) received a higher fraction of input from silenced mechanosensory neurons than in controls, while inhibitory interneurons (Ladder and Griddle) received a lower fraction. Selective silencing of mechanosensory neurons also increased input from a different sensory modality (nociceptive) onto Basin interneurons and decreased their input from inhibitory interneurons. This overall effect is similar to observations in the cortex, where sensory deprivation induces network-level homeostasis that alters the balance of excitation and inhibition. Synaptic scaling in the cortex is thought to be multiplicative, such that all excitatory connections onto an excitatory neuron are scaled equally when excitatory drive onto that neuron is reduced. In contrast, the inhibitory connections onto excitatory neurons are reduced. Although the majority of studies in the cortex focus on homeostatic plasticity of functional connections, this study demonstrated a drastic plasticity in the number of synaptic connections between partners. This apparent homeostasis of synapse numbers may follow similar multiplicative rules, because this study found that both mechanosensory and nociceptive inputs onto Basin interneurons were increased when mechanosensory neurons were silenced (Valdes-Aleman, 2020).

It was found that larvae with permanently silenced mechanosensory neurons not only had increased structural connections between nociceptive and Basin neurons but also stronger functional connections and behavioral responses to nociceptive stimuli. This structural and behavioral compensation is reminiscent of findings in mammals, in which if one sensory modality is removed, another modality is restructured and improved (Valdes-Aleman, 2020).

Interestingly, silencing mechanosensory neurons during development permanently decreased responses to mechanosensory stimuli, even days after restoring activity. This is also reminiscent of findings in mammals, in which deprivation of visual input during an early critical period permanently impairs vision. However, this result appears at odds with the increased structural and functional connections from silenced mechanosensory neurons onto the excitatory Basins. A possible explanation is the reduction of mechanosensory connections onto inhibitory neurons under the same conditions. Unlike nociceptive neurons, the mechanosensory neurons have more inhibitory than excitatory postsynaptic partners, and these inhibitory interneurons play a role in triggering mechanosensory behaviors through disinhibition. Silencing the mechanosensory neurons may therefore result in a permanent reduction in disinhibition in the circuit with permanent consequences on behavior (Valdes-Aleman, 2020).

In summary, although partner-recognition molecules can ensure neurons recognize and connect only with appropriate partners, they are not sufficient to robustly specify appropriate numbers of synapses. Conversely, although neither precise location of presynaptic terminals nor neuronal activity in presynaptic partners directly instructs partner specificity, both are crucial to achieve appropriate numbers of connections, appropriate strengths of functional connections, appropriate balance of excitation and inhibition, and appropriate behavior. This study reveals with unprecedented resolution how location, identity, and activity work together to give rise to appropriately wired neural circuits and appropriate behaviors (Valdes-Aleman, 2020).

Starvation enhances olfactory sensitivity that encourage animals to search for food. The molecular mechanisms that enable sensory neurons to remain flexible and adapt to a particular internal state remain poorly understood. The roles of GABA and insulin signaling in starvation-dependent modulation of olfactory sensory neuron (OSN) function was studied in the Drosophila larva. The GABAB-receptor and insulin-receptor play important roles during OSN modulation. Using an OSN-specific gene expression analysis, this study explored downstream targets of insulin signaling in OSNs. The results suggest that insulin and GABA signaling pathways interact within OSNs and modulate OSN function by impacting olfactory information processing. It was further shown that manipulating these signaling pathways specifically in the OSNs impact larval feeding behavior and its body weight. These results challenge the prevailing model of OSN modulation and highlight opportunities to better understand OSN modulation mechanisms and their relationship to animal physiology (Slankster, 2020).

Starvation increases olfactory sensitivity that enhances an animal's search for food. This has been shown in insects, worms, and mammals including humans. However, the mechanisms by which an animal's starved state modulates sensory neuron function remain poorly understood. Understanding of these mechanisms significantly improved in the last decade or so from studies that showed how neuromodulators enable changes in the gain of peripheral sensory inputs. The prevailing mechanistic model for olfactory sensory neuron (OSN) modulation by the animal's starved state is that during the animal's starved-state, lower insulin signaling frees production of the short neuropeptide F receptor (sNPFR1), which increases sNPF signaling. Higher sNPF signaling increases presynaptic facilitation of OSNs, which leads to enhanced responses to odors. Interestingly, insulin and neuropeptide Y (the mammalian ortholog of sNPF) signaling also feature in the vertebrate olfactory bulb (Slankster, 2020).

However, the above model is incomplete and several questions remain. For instance, the model does not account for the role of GABA signaling, which plays important roles during both starvation and olfactory behavior in flies and mammals. The model also does not account for interactions between GABA and insulin signaling pathways that are known to impact neuromodulation in both fly and mammalian systems: For instance, GABAB-Receptor (GABA_BR) mediates a GABA signal from fly brain interneurons, which may be involved in the inhibitory control of Drosophila insulin like peptide (DILP) production; In mammalian CNS neurons, insulin increases the expression of GABA_AR on the postsynaptic and dendritic membranes; GABA administration to humans resulted in a significant increase in circulating Insulin levels under both fasting and fed conditions. Finally, the model does not account for the ultimate targets of insulin/GABA/sNPF signaling that alter OSN sensitivity to odors and its function (Slankster, 2020).

The above questions are significant because the mechanisms driving neural circuit modulations are fundamental to understanding of how neural circuits support animal cognition and behavior. If these mechanisms are better understood, it would be possible to learn how flexibility and the ability to adapt to a particular internal state are built into the sensory circuit. Understanding the mechanisms by which the starved state of an animal modulates its olfactory sensitivity and thereby controls its food-search behavior is important for both olfactory and appetite research. Finally, this connection cannot be ignored in light of the obesity epidemic and the demonstration that obese adults have reduced olfactory sensitivity (Slankster, 2020).

This study builds upon the prevailing model and argue that GABA and insulin signaling pathways interact within OSNs to mediate starvation-dependent modulation of its function and that defects in these signaling pathways impact larval food-search and feeding behaviors, which in turn impact weight gain. The Drosophila larval system is used in this study to demonstrate evidence in support of this argument. Using larval behavior assays, this study shows that GABA_BR and insulin receptor (InR) are required for starvation dependent increases in larval olfactory behavior. Using a novel OSN-specific gene expression analysis, this study shows that insulin and GABA signaling pathways interact within OSNs and modulate OSN function by impacting odor reception, olfactory information processing, and neurotransmission. Finally, this study shows that manipulating these signaling pathways specifically in the OSNs impact larval feeding behavior and its body weight (Slankster, 2020).

Insulin and GABA signaling pathways interact within OSNs and likely modulate OSN function by impacting odor reception (Orco), olfactory information processing (Rut), and/or neurotransmission (Syt1). Defects in GABA/insulin signaling pathways impact the animal's feeding behavior and body weight. These findings suggest a hitherto unsuspected role for GABA signaling in starvation-dependent modulation of OSN function, a role that is likely downstream of insulin signaling. They also raise questions about how individual OSNs may be differentially modulated by the animal's starved state. Finally, these findings imply a potential relationship between nutrient sensing and animal physiology (Slankster, 2020).

GABA and insulin signaling play important roles during both starvation and olfactory behavior. While GABA signaling in different regions of the animal brain is known to mediate starvation-dependent behavior, its role in specific olfactory neurons during starvation is unclear. Similarly, insulin has long been considered as an important mediator of state dependent modulation of feeding behavior. However, its precise role in olfactory neurons during starvation is controversial. According to the prevailing model, insulin signaling decreases upon starvation. However, a previous study showed that there is a three-fold increase in DILP-6 (Drosophila Insulin like Peptide) mRNA expression in larval tissue including fat bodies upon starvation (Slaidina, 2009), which is inconsistent with the model described in this paper. While the significance of DILP-6 increase in larval tissue during starvation is as yet unclear, consistent with the prevailing model, this study shows that InR and DILP-6 expression in larval head samples decrease upon starvation (Slankster, 2020).

This study also shows that higher insulin signaling increases expression levels of GABA_BRs in OSNs. This result is in line with several other studies in flies and mammals that have suggested possible interactions between GABA signaling and insulin signaling in different regions of the brain. The most relevant example supporting the current observation is noted in mice where insulin increases the expression of GABAARs on the postsynaptic and dendritic membranes of CNS neurons. Other examples show how GABA signaling might influence insulin signaling. For instance, in flies, GABA signaling from interneurons has been shown to affect insulin signaling by regulating DILP production; In humans, GABA administration significantly increases circulating insulin levels under both fasting and fed conditions; In diabetic rodent models, combined oral administration of GABA and an anti-diabetic drug (Sitagliptin) promoted beta cell regeneration and reduced blood glucose levels. Overall, this study adds to this growing body of literature and strongly suggests that GABA and insulin signaling pathways interact within larval OSNs to mediate OSN modulation (Slankster, 2020).

It is noted that starvation enhanced larval attraction toward only a subset of the odors tested. A related question in the field is whether starvation enhances an animal's ability to detect food-odors or all odors. Studies are inconclusive so far. Some studies have shown that starvation enhances an animal's ability to detect both food-related odors and nonfood-related odors. While similar results have also been shown in humans, the findings regarding the relevance of odor to feeding are rather mixed. This study along with previous studies raise the possibility that starvation differentially modulates individual OSNs. Indeed, individual OSNs exhibit functional diversity that may lend them to differential modulation by the animal's internal state. This diversity may stem from heterogeneous GABA_BR levels on the terminals of individual OSNs that determine differential presynaptic gain control. It is reasonable to speculate that heterogeneous GABA_BR and/or InR levels in individual OSNs could contribute to differential modulation of OSNs by the animal's starved state, which in turn impacts behavior toward only a subset of odors (Slankster, 2020).

An inability to regulate sensitivity to food odors at appropriate times leads to irregular feeding habits, which in turn leads to weight gain. Obesity researchers will readily acknowledge that while several obvious risk factors for obesity (e.g., genetics, nutrition, metabolism, environment etc.) have been heavily researched, the relationship between nutrient sensing/sensory behavior and obesity remains grossly understudied. The present study sets the stage to further explore this relationship. Interestingly, several of the signaling molecules described in this study that play a role in OSN modulation have also been implicated in hyperphagia and obesity phenotypes. For instance, overexpression of sNPF in Drosophila and NPY injection in the hypothalamus of rats leads to increased food-intake and bigger and heavier phenotypes. Genetically obese rats have low levels of insulin in the brain including in the olfactory bulb and imbalanced insulin signaling via insulin receptors is associated with obesity phenotypes. Adenylyl cyclase (rut) deficient mice were found to be obese and both Adenylyl cyclase and Synaptotagmin have been targeted for anti-obesity drug development. These studies provide added significance to the observation that manipulating mechanisms mediating starvation-dependent modulation of OSNs impact feeding behavior and weight gain in larvae (Slankster, 2020).

Indeed, food odors can be powerful appetitive cues. A previous study showed that larvae engage in appetitive cue-driven feeding behavior and that this behavior required NPF signaling within dopaminergic neurons in higher-order olfactory processing centers (Wang, 2013). The current studies show that manipulating GABA_BR signaling in first-order OSNs impact appetitive cue-driven feeding behavior in larvae. While it remains to be seen whether parallel regulations during different stages of olfactory information processing impact feeding behavior, further studies are needed to reveal the mechanistic relationship between GABA_BR/InR signaling in OSNs, feeding behavior, and changes in body-weight (Slankster, 2020).

Based on the evidence so far, a motivating model is proposed for future investigations (see Model for OSN modulation). In this model, InR expressed on the terminals of larval OSNs act as sensors for the internal state of the animal. Its concerted activity with GABA_BR impacts OSN function either at the level of odor reception by affecting the expression of Orco or at the level of olfactory signal transduction by affecting the expression of Rut or at the level of neurotransmission by affecting the expression of Syt1 and sNPFR1. It is acknowledged that more exhaustive gene expression analyses are required to identify other molecular players downstream of InR and GABA_BR. It would also be valuable to investigate the relationship between InR expression levels on the terminals of individual OSNs and the sensitivity of individual OSNs to modulation by the animal's starved state (Slankster, 2020).

A valid concern in this study is that an innate attraction of larvae toward an odorant does not necessarily equate to food-search behavior. However, it is argued that attractiveness toward an odor source is a reliable measure of food-search behavior because an animal's ability to efficiently smell and move toward an odor source necessarily predicates most forms of such behavior. Another possibility to be considered is that changes in OSN sensitivity, food-search and/or feeding behaviors are independently regulated. For instance, it has been noted that starvation-induced hyperactivity in adult Drosophila was independently regulated from food consumption behavior in the flies. Blocking octopamine signaling in a small group of octopaminergic neurons located in the subesophageal zone (SEZ) of the fly brain neurons eliminated starvation induced hyperactivity but not the increase in food consumption. While such a possibility cannot be ruled out, the evidence presented in this study support the argument that starvation induced-changes in OSN function is related to the observed changes in food search and feeding behaviors. It is acknowledged that other studies have opted to keep larvae on sucrose with the intention of starving them of amino acids and other nutrients. So, the non-starved conditions in the present study actually represents partial starvation of macronutrients other than sugar. This was done to control the nutrient intake in the non-starved state with the intention of measuring the impact of individual macronutrients on OSN modulation in future studies. Finally, while this study tested the hypothesis that increases in body-weight of mutant genotypes are due to altered food consumption, alternate hypotheses that body-weight increases may be due to altered metabolism or increased fat accumulation haven not been tested(Slankster, 2020).

Overall, this study conducted in a simple, tractable, and highly conserved model system builds upon the prevailing model of starved-state dependent modulation of OSN function. It highlights and offers unique opportunities that are now possible to address the inadequate understanding of OSN modulation mechanisms at the resolution of single neurons, which in turn would lead to a better understand how flexibility and the ability to adapt to a particular internal state are built into the sensory circuit (Slankster, 2020).

Several features of the adult nervous systems develop in a "critical period," (CP) during which high levels of plasticity allow neural circuits to be tuned for optimal performance. Through an analysis of long-term olfactory habituation (LTH) in female Drosophila, this study provides new insight into mechanisms by which CPs are regulated in vivo LTH manifests as a persistently reduced behavioural response to an odorant encountered for four continuous days and occurs together with the growth of specific, odorant-responsive glomeruli in the antennal lobe. The CP for behavioral and structural plasticity induced by ethyl butyrate (EB) or carbon dioxide (CO(2)) closes within 48 hours after eclosion. The elaboration of excitatory projection neuron (PN) processes likely contribute to glomerular volume increases: both occur together and similarly require cAMP signalling in the antennal lobe inhibitory local interneurons (iLNs). Further, the CP for structural plasticity could be extended beyond 48 hours if EB- or CO(2)-responsive olfactory sensory neurons (OSNs) are silenced after eclosion; thus, OSN activity is required for closing the CP. Strikingly, silencing of glomerulus-selective OSNs extends the CP for structural plasticity only in respective target glomeruli. This indicates existence of a local, short-range mechanism for regulating CP closure (Chodankar, 2020).

Serotonin plays different roles across networks within the same sensory modality. Previous whole-cell electrophysiology in Drosophila has shown that serotonergic neurons innervating the first olfactory relay are inhibited by odorants. This study shows that network-spanning serotonergic neurons segregate information about stimulus features, odor intensity and identity, by using opposing coding schemes in different olfactory neuropil. A pair of serotonergic neurons (the CSDns) innervate the antennal lobe and lateral horn, which are first and second order neuropils. CSDn processes in the antennal lobe are inhibited by odors in an identity independent manner. In the lateral horn, CSDn processes are excited in an odor identity dependent manner. Using functional imaging, modeling, and EM reconstruction, it was demonstrated that antennal lobe derived inhibition arises from local GABAergic inputs and acts as a means of gain control on branch-specific inputs that the CSDns receive within the lateral horn (Zhang, 2019).

Habituation is the process that enables salience filtering, precipitating perceptual changes that alter the value of environmental stimuli. To discern the neuronal circuits underlying habituation to brief inconsequential stimuli, a novel olfactory habituation paradigm was developed, identifying two distinct phases of the response that engage distinct neuronal circuits. Responsiveness to the continuous odor stimulus is maintained initially, a phase termed habituation latency; it requires Rutabaga Adenylyl-Cyclase-depended neurotransmission from GABAergic Antennal Lobe Interneurons and activation of excitatory Projection Neurons (PNs) and the Mushroom Bodies. In contrast, habituation depends on the inhibitory PNs of the middle Antenno-Cerebral Track, requires inner Antenno-Cerebral Track PN activation and defines a temporally distinct phase. Collectively, these data support the involvement of Lateral Horn excitatory and inhibitory stimulation in habituation. These results provide essential cellular substrates for future analyses of the molecular mechanisms that govern the duration and transition between these distinct temporal habituation phases (Semelidou, 2018).

Drosophila is a premier system for molecular approaches to understand habituation because of its advanced molecular and classical genetics. In fact, it is a well-established model for habituation of various sensory modalities such as taste, vision, mechanosensory and escape responses, reflecting that habituation is apparent in most, if not all, circuits and modalities of the nervous system. However, in most of these paradigms, the circuits engaged to process the stimulus and establish the experimentally measured attenuated behavioral response are unclear. Importantly, the advanced understanding of the Drosophila olfactory circuitry and stimulus processing facilitates exploration of the mechanisms mediating decreased stimulus responsiveness and habituation to inconsequential odors. Such a recently described paradigm of olfactory habituation in Drosophila required 30 min of odor exposure and was mediated entirely by antennal lobe neurons. In contrast, habituation to repetitive 30 s odor pulses required functional Mushroom Bodies, neurons on the central brain also implicated in associative learning and memory in flies (Semelidou, 2018).

To resolve this paradox, this study focused on the early behavioral dynamics of habituation upon continuous odor stimulation. To that end, a novel habituation paradigm was developed and characterized to rather brief continuous odors. The behavioral responses define two distinct phases, an initial phase termed habituation latency, when stimulus responsiveness is maintained, which is followed by a significant response decrement reflecting habituation. Analogous response dynamics have been reported for footshock habituation. In addition, whether these phases engage and are mediated by distinct neuronal circuits was. The results highlight the stimulus duration-dependent activation of specific neuronal subsets and their distinct roles in securing timely habituation latency and habituation induction (Semelidou, 2018).

This study describes a novel olfactory habituation paradigm to brief odor stimuli and operationally defines two distinct phases in the response dynamics. The initial period of ~120 s is termed habituation latency and is characterized by maintenance of responsiveness to the odor. This is followed by manifestation of the habituated response, characterized behaviorally by attenuated osmotaxis. Focusing on the behavioral dynamics early in the process complements previous work olfactory habituation to continuous odor stimulation in Drosophila. A number of criteria differentiate these two paradigms from other types of habituation to olfactory stimuli as discussed below (Semelidou, 2018).

Drosophila habituate equally well to continuous or pulsed olfactory stimuli. This likely reflects the nature of olfactory stimuli, which typically are continuous rather than pulsed. On the other hand, habituation of the startle response to ethanol vapor may specifically require short (30 s) pulses due to its sedative properties and this may also be reflected by the rather long 6 min ITIs compared to the 15 s to 2.5-min intervals used herein for OCT. Short odor pulses are also required for the odor-mediated jump and flight response habituation, suggesting that pulsing may be necessary to evoke the startle response per se (Semelidou, 2018).

An important property shared with all habituation paradigms in Drosophila and other systems is spontaneous recovery of the response. This is another differentiating parameter among habituation paradigms in Drosophila. For the olfactory habituation paradigms, whereas 6 min suffice for spontaneous recovery after 4 and 30 min continuous odor exposure, 15-30 to surprisingly 60 min are required for recovery in the olfactory startle paradigms. Habituation to mechanosensory stimuli typically also requires shorter spontaneous recovery times, with habituation of the giant fiber-mediated jump-and-flight response requiring a mere 2 min and electric footshock habituation 6 min. Interestingly, other non-mechanosensory habituation paradigms require long spontaneous recovery periods with 30 min for habituation of the proboscis extension reflex (PER), and surprisingly, 2 hr for habituation of odor-induced leg response. It is posited that these differences reflect the engagement of distinct neuronal circuits mediating habituation to these diverse stimuli and the properties and connections of the neuronal types that comprise them (Semelidou, 2018).

Overall, these data suggest that latency and habituation to brief odor exposure involve modulation of lateral horn (LH) output, a neuropil innately encoding response valence to odor stimuli. It is propose dthat habituation latency involves processes that are not permissive to, or actively prevent stimulus devaluation. Latency duration depends on stimulus strength and is consistent with the notion that it is adaptive not to devalue strong, hence potentially important stimuli, expediently. In fact, it is posited that habituation latency serves to facilitate associations with concurrent stimuli, a requirement for associative learning. Shortened latency leading to premature habituation is predicted to compromise associative learning (Semelidou, 2018).

Importantly, maintaining responsiveness early upon odorant exposure requires activity of GABAergic inhibitory neurons, which are essential for lateral inhibition of antennal lobe glomeruli. LN activation appears to prevent saturation by strong continuous odors and hence reduce PN activity. Therefore, shortening habituation latency by blocking GABAergic neurotransmission in the antennal lobe may effectively reduce stimulus intensity, expediting habituation as suggested by the dilute odor experiments. This interpretation is further supported by the decreased habituation latency upon silencing the iACT PNs conveying olfactory signals to the MBs and the LH, but not by the mACT neurons innervating only the LH. Since iACT PNs are mainly excitatory, it appears that response maintenance requires excitatory signaling to the LH and the MBs (Semelidou, 2018).

All MB neuronal types except the γ, are essential for habituation latency. This suggests that at least part of the excitatory signal conveyed by the iACT PNs impinges upon the αβ and α' β' MB neurons, which is consistent with their role in associative learning and the proposal that habituation latency facilitates it. Neurotransmission from the MBs to LH neurons mediating aversive responses likely engages MB output neurons (MBONs), to maintain the valence and intensity of the odor and sustain aversion. Distinct MBONs are known to drive both attraction and aversion to odors and their potentially differential involvement in habituation is currently under investigation (Semelidou, 2018).

Dishabituation results in stimulus value recovery and apparently resets habituation latency. Clearly it requires neurotransmission via the GH146-marked neurons and MBs because silencing these neurons disables dishabituation, consistent with their role in response maintenance. These results lead to the hypothesis that dishabituating stimuli might converge on the MBs and/or iACT, possibly stimulating excitatory neurotransmission to the LH, to reinstate stimulus aversion. This hypothesis is currently under investigation as well (Semelidou, 2018).

In contrast, habituation requires prolonged or repeated exposure to the odorant and functional iACT and mACT PNs converging on the LH. Interestingly, the mainly GABAergic mACT PNs receive input both from the olfactory sensory neurons and the excitatory iACT PNs. Their depolarization also activates the excitatory iACT neurons via direct chemical synapses. This apparent feedback loop may be required for mACT activation after prolonged exposure to aversive odors, since these neurons were reported to respond mainly to attractive stimuli. It is proposed that prolonged aversive odor exposure enhances iACT activation, which in turn leads to habituation, while shorter exposure does not activate the iACT neurons, reflected by their dispensability for habituation latency. Importantly, the mACT innervates the LH downstream of the iACT PNs, providing feedforward inhibition. These characteristics likely underlie the necessary and sufficient role of mACT PNs in habituation upon 4-min odor stimulation. Collectively, these results are consistent with the proposal that mACT activation inhibits the innate LH-mediated avoidance response to the aversive odorant, establishing habituation (see A model of the neuronal subsets underlying (A) Habituation Latency and (B) Habituation, after exposure to aversive stimuli.). However, full mACT activation appears to also require iACT neurotransmission, which if abrogated eliminates habituation but is insufficient to establish it on its own (Semelidou, 2018).

Because the MZ699 Gal4 driver also marks ventrolateral protocerebrum (vlpr) neurons it is possible that they also play a role in habituation. In fact, vlpr neurons function in aversive odor responses, are activated by excitatory iACT PNs, inhibited by the inhibitory PNs, and are afferent to the LH. Thus, they could act in parallel or synergistically to mACT PNs to establish the habituated response. As no specific vlpr driver is available, it is impossible at the moment to address this possibility directly. Briefly then, the current collective results strongly suggest a novel role for the inhibitory PNs innervating the LH, and possibly vlpr neurons, in inhibition of the innate response and habituation. The kinetics of inhibitory projection neuron activation and their output on downstream neurons could serve as a measure of the duration of odor exposure. Upon prolonged exposure, these neurons mediate inhibition of odor avoidance, thus devaluing the stimulus (Semelidou, 2018).

Analysis of the neuronal subsets underlying habituation has focused on aversive odors. However, considering the neuronal clusters involved in the process, it would be relatively safe to assume that the results extend to attractive odor habituation as well. It is possible that the neuronal circuitry comprised of PNs, the LH and MBs may be mediating habituation independently of odor valence. However, specific neuronal clusters may differ in odor valance-dependent activation or inhibition of other circuit components with opposing effects on the behavioral readout. For example, inhibitory PNs (iPNs) mediate attraction by releasing GABA in the LH to inhibit avoidance. If inhibited themselves, the resultant attenuated attraction will likely drive a behavioral output of habituation to an attractive odor (Semelidou, 2018).

In accord with this notion, attractive and aversive odors are represented in different AL glomerular clustersand this valence-dependent organization is preserved into higher brain centers. In fact, the posterior-dorsal LH responds to attractive and its ventral complement to aversive odors, while third order neurons convey information from ventral LH to the vlpr and from the dorsal LH to the superior medial protocerebrum. This organization potentially reflects differential recruitment of these neuronal clusters in habituation to aversive and attractive odors. The circuits involved in habituation to attractive odors and their specific contribution to the process will be the focus of future work (Semelidou, 2018).

Although behaviorally there is significant osmotactic attenuation after both 4 and 30 min aversive odor exposure, the experiments suggest that these represent distinct types of olfactory habituation. Habituation after 4 min of odor exposure does not require the MBs, but rather the projection neurons innervating the LH. Habituation after 30 min of exposure is also independent of MB function, but appears to be entirely mediated by iLNs and reside within the AL. This clear difference suggests that the specific potentiation of inhibitory synapses shown to underlie habituation after 30 min of exposure is not necessary for habituation to the brief 4-min exposure. Additionally, while Rut is required within the iLNs during the latency period upon brief odor exposure, it is surprisingly required within the same neurons for habituation to long odor exposure. Therefore, Rut-driven activity within the iLNs yields opposing time-dependent behavioral outputs in accord with the abovementioned notion that the same circuit components may drive opposing outputs (Semelidou, 2018).

Furthermore, the fact that mechanosensory stimuli are not effective dishabituators after 30 min of odor exposure as they are after 4 min, augments the conclusion these are different types of olfactory habituation and suggests that distinct dishabituators likely recruit different neuronal subsets to modulate the habituated response. Such neuronal circuits and the effect of different dishabituators in response recovery are currently under investigatio (Semelidou, 2018).

Altogether, the results indicate different mechanisms for 4 min and 30 min habituation to aversive odors with the former mediated by the interaction between iPNs, ePNs and their targets in the LH, while the latter is based on the inhibition of ePNs by iLNs at the AL level. However, it is possible that the potentiated PN inhibition would decrease their output to the LH to drive reduced avoidance. This argues that the LH could be involved in the behavioral output indicating habituation after 30 min of OCT exposure as well. An AL-mediated reduction in the perceived intensity or valence of a chronically present odor probably serves an adaptive evolutionary role distinct from short exposure to the same stimulus. In fact, filtering away the chronic odor at the antenna, the first olfactory synaptic station, might facilitate evaluation of additional odors at higher order neurons such as the MBs or the LH (Semelidou, 2018).

Significantly, this interpretation is congruent with timescale habituation in mice, where short-timescale odor habituation is mGluR-dependent and mediated by the anterior piriform cortex while long-timescale habituation requires NMDAR and is mediated by the olfactory bulb. In addition, studies in mice, rats and primates have shown that habituation of the higher order neurons is faster and more prominent than in olfactory bulb neurons. Therefore, temporal and spatial principles for olfactory habituation appear broadly conserved between insects and mammals, despite their evolutionary distance (Semelidou, 2018).

Noxious stimuli trigger a stereotyped escape response in animals. In Drosophila larvae, class IV dendrite arborization (C4 da) sensory neurons in the peripheral nervous system are responsible for perception of multiple nociceptive modalities, including noxious heat and harsh mechanical stimulation, through distinct receptors. Silencing or ablation of C4 da neurons largely eliminates larval responses to noxious stimuli, whereas optogenetic activation of C4 da neurons is sufficient to provoke corkscrew-like rolling behavior similar to what is observed when larvae receive noxious stimuli, such as high temperature or harsh mechanical stimulation. How C4 da activation triggers the escape behavior in the circuit level is still incompletely understood. This study identified segmentally arrayed local interneurons (medial clusters of C4 da second-order interneurons [mCSIs]) in the ventral nerve cord that are necessary and sufficient to trigger rolling behavior. GFP reconstitution across synaptic partners (GRASP) analysis indicates that C4 da axons form synapses with mCSI dendrites. Optogenetic activation of mCSIs induces the rolling behavior, whereas silencing mCSIs reduces the probability of rolling behavior upon C4 da activation. Further anatomical and functional studies suggest that the C4 da-mCSI nociceptive circuit evokes rolling behavior at least in part through segmental nerve a (SNa) motor neurons. These findings thus uncover a local circuit that promotes escape behavior upon noxious stimuli in Drosophila larvae and provide mechanistic insights into how noxious stimuli are transduced into the stereotyped escape behavior in the circuit level (Yoshino, 2017).

Insects and mammals share similarities of neural organization underlying the perception of odors, taste, vision, sound, and gravity. This study observed that insect somatosensation also corresponds to that of mammals. In Drosophila, the projections of all the somatosensory neuron types to the insect's equivalent of the spinal cord segregated into modality-specific layers comparable to those in mammals. Some sensory neurons innervate the ventral brain directly to form modality-specific and topological somatosensory maps. Ascending interneurons with dendrites in matching layers of the nerve cord send axons that converge to respective brain regions. Pathways arising from leg somatosensory neurons encode distinct qualities of leg movement information and play different roles in ground detection. Establishment of the ground pattern and genetic tools for neuronal manipulation should provide the basis for elucidating the mechanisms underlying somatosensation (Tsubouchi, 2017).

Only three distinct types of sensory information are transmitted directly to the brain by primary neurons [leg gustatory sensilla (gs), chordotonal organs (co), and wing and haltere campaniform sensilla (cs)]. Such connection has also been reported in other insects, suggesting that this might be a general feature across insecta. Whereas only a small portion of leg co neurons project directly to the brain, most wing and haltere cs neurons innervate the brain; these cs neurons are known to detect various aspects of wing-beat force during flight to provide feedback control. Direct projections to the brain would be important for these neurons to enable fast transmission of information about rapidly changing sensory parameters during flight (Tsubouchi, 2017).

It was found that ground detection for wind-induced suppression of locomotion (WISL), which would require slower temporal resolution than flight control, is mediated by both direct and indirect pathways. Primary neurons and secondary interneurons of the same sensory modality tend to converge in specific subregions of the brain, forming modality-specific somatosensory representation. In spite of the similar axon trajectory in the brain, these neurons convey information about leg movement in different ways (Tsubouchi, 2017).

Interneurons associated with the leg co and es terminate in neighboring but different regions of the lateral brain, yet some of them have shared roles in WISL control. Because their signals are transmitted to distinct parts of the brain, yet-unidentified higher-order neurons in the brain should converge those signals to the motor control circuitry (Tsubouchi, 2017).

In this respect, it is important to note that most ascending secondary interneurons identified in this study have presynaptic output sites, not only in the brain but also in the VNC. Local circuitry in the leg neuropil is important for controlling leg movement. Those local neurons are likely candidates that receive output from the ascending interneurons, because axon terminals of sensory neurons hardly have postsynaptic sites. Similar local output has also been found in other sensory modalities; many olfactory and visual projection interneurons have collateral output synapses in the antennal lobe and optic lobes (Tsubouchi, 2017).

There are three pairs of leg neuropils. Among them, the foreleg neuropil has specialized arborization of the gs neurons that exist only in the foreleg. Other than this, no substantial differences of arborization patterns were found between the fore-, mid-, and hindleg neuropils (Tsubouchi, 2017).

The present results provide data for a systematic comparison of the insect somatosensory system with its mammalian counterparts. Insects and mammals share similarities of neural organization underlying the perception of odors, taste, vision, sound, and gravity, and the current data also reveal marked similarity for the mechanosensory system. In insects, some primary neurons project directly to distinct parts of the ventral and lateral brain, whereas others terminate within the VNC. Likewise, in mammals, some neurons project directly to the ventral brain at the medulla oblongata, whereas others terminate within the spinal cord. Modality-specific pathways tend to converge in different subregions of the medulla, as well as in the thalamus of the mammalian brain. Similarly, direct and indirect pathways tend to converge in common subregions of the insect brain, and neurons conveying information about different somatosensory modalities tend to terminate in different subregions. As in mammals, these subregions often lie adjacent to each other in certain parts of the brain; for example, the entire terminal arborizations of the leg co and es secondary interneurons are confined in a 40-μm-wide, 150-μm-tall cylindrical volume in the lateral brain (Tsubouchi, 2017).

Somatosensory signals are sent predominantly to the ipsilateral brain side in insects and contralateral in mammals. Considering that descending neurons tend to project ipsilaterally in insects but contralaterally in mammals, however, somatosensory signals and motor control computation are processed primarily in the same side of the brain in both cases (Tsubouchi, 2017).

Layers of sensory axon terminals in the insect VNC and mammalian spinal cord are also organized in a similar order. Insect multidendritic neurons and mammalian free nerve endings share various characteristics in common: Their dendrites both have free endings without forming particular sense organs to detect pain, temperature, and other submodalities. The md neurons project to the most ventral layer of the VNC, whereas free nerve endings innervate the most dorsal layer of the spinal cord. Axons from the insect external sensilla and mammalian hair receptors, both of which detect haptic contact to the tips of the bristles and hairs, terminate in the second-ventral and second-dorsal layers, respectively. Insect chordotonal organ and mammalian muscle spindle, as well as insect campaniform sensilla and mammalian Golgi tendon organ, also show similarity with respect to their functions in motor control. These receptor systems supply afferents to the most dorsal and most ventral layers in insects and mammals, respectively. A fly's stretch receptors and mammalian Merkel cell neurites-as well as Meissner, Ruffini, and Pacinian corpuscles-terminate in the third-ventral and third-dorsal layer, respectively. Although correspondence between them is less obvious, they similarly detect deformation of the exoskeleton and skin. Thus, functionally comparable somatosensory terminals are layered in reverse order between the two systems. Considering that the dorsoventral axis of the mammalian body is developmentally upside down compared with the insect one, the corresponding order of sensory arrangements is actually conserved exactly between the two systems (Tsubouchi, 2017).

Do corresponding somatosensory cell types express common genes? Modality-dependent molecular specialization is not apparent even within insects or mammals, because the same genes are often expressed in multiple cell types and only a few genes share expression in the corresponding cell types across taxa. This might be a rather general feature; receptor molecules as well as developmental origins of the sensory organs are not identical between insects and mammals also in olfactory and auditory systems, yet sensory centers in the brain share architectural similarities (Tsubouchi, 2017).

With this somatosensory analysis, transphyletic correspondence of neuronal circuitry has been found in all of the sensory modalities. Corresponding organization has been suggested also for associative centers and motor systems. The fact that essentially all important components of the brain system share conserved features across the two evolutionary clades, which have been separated since at least the end of the Ediacaran period more than 550 million years ago, would suggest that basic development programs for the orderly and secrete segregation of those circuits may have evolved before deuterostome-protostome or deuterostomia-ecdysozoa divergence (Tsubouchi, 2017).

Modulatory neurons project widely throughout the brain, dynamically altering network processing based on an animal's physiological state. The connectivity of individual modulatory neurons can be complex, as they often receive input from a variety of sources and are diverse in their physiology, structure, and gene expression profiles. To establish basic principles about the connectivity of individual modulatory neurons, a pair of identified neurons was examined, the 'contralaterally projecting, serotonin-immunoreactive deutocerebral neurons' (CSDns), within the olfactory system of Drosophila. Specifically, the neuronal classes were determined providing synaptic input to the CSDns within the antennal lobe (AL), an olfactory network targeted by the CSDns, along with the degree to which CSDn active zones are uniformly distributed across the AL. Using anatomical techniques, the CSDns were found to receive glomerulus-specific input from olfactory receptor neurons (ORNs) and projection neurons (PNs), and network-wide input from local interneurons (LNs). Furthermore, the number of CSDn active zones was quantified in each glomerulus; CSDn output was fount to be not uniform, but rather heterogeneous across glomeruli and stereotyped from animal to animal. Finally, it was demonstrated that the CSDns synapse broadly onto LNs and PNs throughout the AL, but do not synapse upon ORNs. These results demonstrate that modulatory neurons do not necessarily provide purely top-down input, but rather receive neuron class-specific input from the networks that they target, and that even a two cell modulatory network has highly heterogeneous, yet stereotyped pattern of connectivity (Coates, 2017).

Neuromodulation confers flexibility to anatomically-restricted neural networks so that animals are able to properly respond to complex internal and external demands. However, determining the mechanisms underlying neuromodulation is challenging without knowledge of the functional class and spatial organization of neurons that express individual neuromodulatory receptors. This study describes the number and functional identities of neurons in the antennal lobe of Drosophila melanogaster that express each of the receptors for one such neuromodulator, serotonin (5-HT). Although 5-HT enhances odor-evoked responses of antennal lobe projection neurons (PNs) and local interneurons (LNs), the receptor basis for this enhancement is unknown. Endogenous reporters of transcription and translation for each of the five 5-HT receptors (5-HTRs) were used to identify neurons, based on cell class and transmitter content, that express each receptor. Specific receptor types are expressed by distinct combinations of functional neuronal classes. For instance, the excitatory PNs express the excitatory 5-HTRs (5-HT2 type and 5-HT7), the 5-HT1 type receptors are generally inhibitory, and distinct classes of LNs each express different 5-HTRs. This study therefore provides a detailed atlas of 5-HT receptor expression within a well-characterized neural network, and enables future dissection of the role of serotonergic modulation of olfactory processing (Sizemore, 2016).

Neuromodulators often act through diverse sets of receptors expressed by distinct network elements and in this manner, differentially affect specific features of network dynamics. Knowing which network elements express each receptor for a given neuromodulator provides a framework for making predictions about the mechanistic basis by which a neuromodulator alters network activity. This study provides an 'atlas' of 5-HTR expression within the AL of Drosophila, thus revealing network elements subject to the different effects of serotonergic modulation. In summary, different receptors are predominantly expressed by distinct neuronal populations. For example, the 5-HT2B is expressed by ORNs, while the 5-HT2A and 7 are expressed by cholinergic PNs. Additionally, each receptor was found to be expressed by diverse populations of LNs, with the exception the 5-HT1B. For instance, 5-HT1A is expressed by GABAergic and peptidergic (TKK and MIP) LNs, while 5-HT2A and 2B are not expressed by peptidergic LNs. However, the vPNs are the exception to the general observation that distinct neuronal classes differ from each other in the 5-HTRs and the implications of this are discussed below. Together, these results suggest that within the AL, 5-HT differentially modulates distinct populations of neurons that undertake specific tasks in olfactory processing (Sizemore, 2016).

A recurring theme of neuromodulation is that the expression of distinct receptor types by specific neural populations allows a single modulatory neuron to differentially affect individual coding features. For instance, GABAergic medium spiny neurons (MSNs) in the nucleus accumbens express either the D1 or D2 dopamine receptor allowing dopamine to have opposite effects on different MSNs via coupling to different Galpha subunits (reviewed in56). MSNs that differ in dopamine receptor expression also differ in their synaptic connectivity. Dopamine activates D1-expressing MSNs that directly inhibit dopaminergic neurons in the ventral tegmental area (VTA), and inhibits D2-expressing MSNs that inhibit GABAergic VTA interneurons thus inducing suppression of dopamine release. In this manner, a single neuromodulator differentially affects two populations of principal neurons via different receptors to generate coordinated network output. This principle also holds true for the effects of 5-HT within the olfactory bulb. For instance, 5-HT enhances presynaptic inhibition of olfactory sensory neurons by 5-HT2C-expressing juxtaglomerular cells57, while increasing excitatory drive to mitral/tufted cells and periglomerular cells via 5-HT2A-expressing external tufted cells. Similarly, distinct classes of AL neurons were observed to differ in their expression of 5-HTRs. For instance, ePNs express the 5-HT2A, 5-HT2B and 5-HT7 receptors, while peptidergic LNs predominantly express the 5-HT1A receptor. This suggests that the cumulative effect of 5-HT results from a combination of differential modulation across neuronal populations within the AL. Interestingly, although it was found that 5-HT2B is expressed by ORNs, previous reports found that 5-HT does not directly affect Drosophila ORNs. In this study, ORNs were stimulated using antennal nerve shock in which the antennae were removed in order to place the antennal nerve within a suction electrode. Thus, if 5-HT2B is localized to the ORN cell body, removal of the antennae would eliminate any effect of 5-HT on ORNs. In several insects, 5-HT within the antennal haemolymph modulates ORN odor-evoked responses. Therefore, it is plausible ORNs are modulated by a source of 5-HT other than the CSD neurons within the AL. Serotonergic modulation of LN activity has widespread, and sometimes odor specific, effects on olfactory processing. LNs allow ongoing activity across the AL to shape the activity of individual AL neurons, often in a glomerulus specific manner creating non-reciprocal relationships. It is fairly clear that 5-HT directly modulates LNs, although 5-HT almost certainly affects synaptic input to LNs. Serotonin modulates isolated Manduca sexta LNs in vitro and, consistent with the current results, a small population of GABAergic LNs in the AL of Manduca also express the 5-HT1A receptor. Furthermore, 5-HT has odor-dependent effects on PN odor-evoked activity, suggesting that odor specific sets of lateral interactions are modulated by 5-HT. Different populations of LNs were found to express different sets of 5-HT receptors, however LNs were categorized based on transmitter type, so it is possible that these categories could be even further sub-divided based on morphological type, synaptic connectivity or biophysical characteristics. Regardless, the results suggest that 5-HT modulates lateral interactions within the AL by selectively affecting LN populations that undertake different tasks. For instance, the TKKergic LNs that express the 5-HT1A receptor provide a form of gain control by presynaptically inhibiting ORNs32. The results suggest that 5-HT may affect TKK mediated gain control differently relative to processes undertaken by other LN populations. Furthermore, the expression of the TKK receptor by ORNs is regulated by hunger, allowing the effects of TKK to vary with behavioral state. It would be interesting to determine if the expression of 5-HTRs themselves also vary with behavioral state as a means of regulating neuromodulation within the olfactory system (Sizemore, 2016).

Although it was primarily found that individual populations of AL neurons chiefly expressed a single or perhaps two 5-HTR types, the vPNs appear to be an exception. As a population, the vPNs express all of the 5-HTRs and the vPNs that express each 5-HTR did not appear to differ in terms of the proportion of those neurons that were GABAergic or cholinergic (roughly 3:2). Unfortunately, the approach does not allow determination of the degree to which individual vPNs co-express 5-HTRs. However, it is estimated that there are ~51 vPNs and even if this is an underestimate, there is likely some overlap of receptor types as a large number of vPNs expressed the 5-HT1A, 1B, 2B and 7 receptors. It is possible that a single vPN expresses one 5-HTR in the AL and a different 5-HTR in the lateral horn. However, the current approach only allows identification of which neurons express a given 5-HTR, not where that receptor is expressed. The CSD neurons ramify throughout both ALs and both lateral horns, thus vPNs could have differential spatial expression of individual 5-HTRs. Individual neurons expressing multiple 5-HTRs has been demonstrated in several neural networks. For instance, pyramidal cells in prefrontal cortex express both the 5-HT1A and 5-HT2A7. This allows 5-HT to have opposing effects that differ in their time course in the same cell. In terms of the vPNs, the results suggest that the current understanding of the diversity of this neuron class is limited. The expression of receptors for different signaling molecules could potentially be a significant component to vPN diversity (Sizemore, 2016).

Neuromodulators are often released by a small number of neurons within a network, yet they can have extremely diverse effects depending upon patterns of receptor expression. For the most part, individual populations of AL neurons differed in the receptor types that they expressed. This suggests that 5-HT differentially acts on classes of neurons that undertake distinct tasks in olfactory processing. In the case of the vPNs, this differential modulation may be fairly complex due to the diversity within this neuronal class. The goal of this study was to establish a functional atlas of 5-HTR expression in the AL of Drosophila. This dataset therefore provides a mechanistic framework for the effects of 5-HT on olfactory processing in this network (Sizemore, 2016).

Glutamatergic neurons are abundant in the Drosophila central nervous system, but their physiological effects are largely unknown. This study investigated the effects of glutamate in the Drosophila antennal lobe, the first relay in the olfactory system and a model circuit for understanding olfactory processing. In the antennal lobe, one-third of local neurons are glutamatergic. Using in vivo whole-cell patch clamp recordings, this study found that many glutamatergic local neurons are broadly tuned to odors. Iontophoresed glutamate hyperpolarizes all major cell types in the antennal lobe, and this effect is blocked by picrotoxin or by transgenic RNAi-mediated knockdown of the GluClα gene, which encodes a glutamate-gated chloride channel. Moreover, antennal lobe neurons are inhibited by selective activation of glutamatergic local neurons using a nonnative genetically encoded cation channel. Finally, transgenic knockdown of GluClα in principal neurons disinhibits the odor responses of these neurons. Thus, glutamate acts as an inhibitory neurotransmitter in the antennal lobe, broadly similar to the role of GABA in this circuit. However, because glutamate release is concentrated between glomeruli, whereas GABA release is concentrated within glomeruli, these neurotransmitters may act on different spatial and temporal scales. Thus, the existence of two parallel inhibitory transmitter systems may increase the range and flexibility of synaptic inhibition (Liu, 2013).

Although glutamatergic neurons are abundant in the Drosophila brain, the role of glutamate as a neurotransmitter in the Drosophila CNS has received little study. In the antennal lobe, where approximately one-third of LNs are glutamatergic, the physiological effects of glutamate have never been characterized. This study shows that glutamate is an inhibito