The Interactive Fly

Genes involved in tissue and organ development

The Drosophila Brain: Central Complex

FlyBrain: From front to back in the brain (i.e along the y axis), the central body complex comprises
the ellipsoid body, the superior arch and fan shaped body above the paired noduli, and the protocerebral bridge.

Drosophila embryonic type II neuroblasts: origin, temporal patterning, and contribution to the adult central complex

Drosophila neuroblasts are an excellent model for investigating how neuronal diversity is generated. Most brain neuroblasts generate a series of ganglion mother cells (GMCs) that each make two neurons (type I lineage), but sixteen brain neuroblasts generate a series of intermediate neural progenitors (INPs) that each produce 4-6 GMCs and 8-12 neurons (type II lineage). Thus, type II lineages are similar to primate cortical lineages, and may serve as models for understanding cortical expansion. Yet the origin of type II neuroblasts remains mysterious: do they form in the embryo or larva? If they form in the embryo, do their progeny populate the adult central complex, as do the larval type II neuroblast progeny? This study presents molecular and clonal data showing that all type II neuroblasts form in the embryo, produce INPs, and express known temporal transcription factors. Embryonic type II neuroblasts and INPs undergo quiescence, and produce embryonic-born progeny that contribute to the adult central complex. These results provide a foundation for investigating the development of the central complex, and tools for characterizing early-born neurons in central complex function (Walsh, 2017).

Development of the anterior visual input pathway to the Drosophila central complex

The anterior visual pathway (AVP) conducts visual information from the medulla of the optic lobe via the anterior optic tubercle (AOTU) and bulb (BU) to the ellipsoid body (EB) of the central complex. This paper analyzes the formation of the AVP from early larval to adult stages. The immature fiber tracts of the AVP, formed by secondary neurons of lineages DALcl1/2 and DALv2, assemble into structurally distinct primordia of the AOTU, BU, and EB within the late larval brain. During the early pupal period (P6-P48) these primordia grow in size and differentiate into the definitive subcompartments of the AOTU, BU, and EB. The primordium of the EB has a complex composition. DALv2 neurons form the anterior EB primordium, which starts out as a bilateral structure, then crosses the midline between P6 and P12, and subsequently bends to adopt the ring shape of the mature EB. Columnar neurons of the central complex, generated by the type II lineages DM1-4, form the posterior EB primordium. Starting out as an integral part of the fan-shaped body (FB) primordium, the posterior EB primordium moves forward and merges with the anterior EB primordium. This paper documents the extension of neuropil glia around the nascent EB and BU and analyzes the relationship of primary and secondary neurons of the AVP lineages (Lovick, 2017).

Neuroarchitecture and neuroanatomy of the Drosophila central complex: A GAL4-based dissection of protocerebral bridge neurons and circuits

Insects exhibit an elaborate repertoire of behaviors in response to environmental stimuli. The central complex plays a key role in combining various modalities of sensory information with an insect's internal state and past experience to select appropriate responses. Progress has been made in understanding the broad spectrum of outputs from the central complex neuropils and circuits involved in numerous behaviors. Many resident neurons have also been identified. However, the specific roles of these intricate structures and the functional connections between them remain largely obscure. Significant gains rely on obtaining a comprehensive catalogue of the neurons and associated GAL4 lines that arborize within these brain regions, and on mapping neuronal pathways connecting these structures. Toward this end, small populations of neurons in the Drosophila melanogaster central complex were stochastically labeled using the multicolor flip-out technique and a catalogue was created of the neurons, their morphologies, trajectories, relative arrangements and corresponding GAL4 lines. This report focuses on one structure of the central complex, the protocerebral bridge, and identifies just 17 morphologically distinct cell types that arborize in this structure. This work also provides new insights into the anatomical structure of the four components of the central complex and its accessory neuropils that are arborized by PB neurons include the crepine (CRE), rubus (RUB), gall (GA), and lateral accessory lobe (LAL). Most strikingly, the protocerebral bridge was found to contain 18 glomeruli, not 16, as previously believed. Revised wiring diagrams that take into account this updated architectural design are presented. This updated map of the Drosophila central complex will facilitate a deeper behavioral and physiological dissection of this sophisticated set of structures (Wolff, 2014).

The work presented here builds on published studies by both defining previously unidentified anatomical features of each of the four components of the central complex as well as updating wiring diagrams to accommodate these new anatomical insights. This paper also reports new cells and new features of previously identified cells and the genetic reporter lines that reveal them, with the prospect that these will form an essential stepping stone both to synaptic studies at the electron microscope level and to functional studies. The most significant new insights from this work are summarized below. As noted earlier, the statements below are drawn from neurons that arborize in the PB (Wolff, 2014).

The most surprising finding of this study is that the Drosophila protocerebral bridge comprises 18 glomeruli. This finding has an important impact on the wiring relationships between the glomeruli and their respective vertical units in the FB, the columns, and in the EB, the wedges and a new volume described in this study, the tiles (Wolff, 2014).

The longstanding belief regarding the correspondence between the PB and FB and PB and EB wedges was that there is a 1:1 relationship between the vertical subdomains of these structures. The finding that there are 18 glomeruli raised the possibility that the FB and EB also exhibit an octodecimal organization. However, compelling evidence is provided that there are just 16 wedges in the EB. and it was further show, that some cells arborize in just half a wedge, indicating the further division of wedges into 32 demi-wedges. The observation that a simple 1:1 correspondence between the PB and EB wedges is lacking and, furthermore, that there are also demi-wedges, has implications for how the system is wired to accommodate this numerical discrepancy (Wolff, 2014).

Another unexpected finding from this work is the existence of a second EB volume that also partitions the EB around its circumference: the tile domain. Tiles are distinct from wedges in that there are half as many tiles as wedges (eight tiles), they are functionally distinct from the wedge (output versus input, respectively), and these two volumes survey different volumes of the EB since they extend to different depths of the EB. Only two PB cell types target the tile domain (Wolff, 2014).

Although a columnar morphology is apparent in layers 1–8 of the FB in nc82-labeled samples, the organization of the cells that populate these layers is not universally columnar. There is a minimum of nine layers in the FB, yet a columnar organization (i.e., vertical stratification) of cell arbors is restricted to layers 1–5 for single column widths (where the columnar organization for layers 4 and 5 is revealed by the PBG1–8.s-FBℓ3,4,5.s.b-rub.b cell); wider, more loosely organized arbors occur in more dorsal layers. With the exception of arbors in layer 1, the column borders are not rigid, as neighboring arbors overlap one another, sometimes extensively. The unique tooth-like structure of layer 1 of the FB definitively shows that there are nine columns in this layer. Due to the overlap of arbors, it is more difficult to count columns in the other layers, but layers 2 and 3, which exhibit a tighter columnar organization than more dorsal layers, likely have 16 columns based on mapping data. This would be consistent with parallel divisions of the EB (wedges) (Wolff, 2014).

Other new anatomical features and subdomains are described in this study. First, it was shown that each of the noduli has subcompartments. The dorsal noduli, NO1, have medial and lateral subcompartments. The medial noduli, NO2, consist of two distinct subcompartments, NO2D and NO2V; no PB cell type arborizes in either of these subcompartments. The ventral noduli, NO3, consist of three distinct subcompartments, NO3A, NO3M, and NO3P. The nubbin is a partial shell on the dorsal, anterior face of the EB, and the "gall tip" is a region at the dorsal tip of the gall. Finally, two undefined regions to which some cells project and that are not clearly demarcated are the dorsal and ventral gall surround (Dga-s and Vga-s) (Wolff, 2014).

Even though the subdomains of the central complex structures can be distinguished from one another, they apparently do not function as isolated subunits. Rather, there is shared communication between most of these subunits. At least for the neurons described here (i.e., those that arborize in the PB), both pre- and postsynaptic arbors in the glomeruli, EB tiles, wedges and shells, FB columns, and NO1 (medial and lateral domains) can extend into neighboring domains. This sharing of information is not obvious between NO2D and NO2V, nor between NO3A and NO3M. The boundary between NO3P and NO3M is too obscure to evaluate if arbors in these two domains are completely restricted or shared, although in the examples they appear to be restricted. The frequency and degree to which arbors overlap in the various subunits is cell type-dependent. While some arbors exhibit no or minimal intrusion into adjacent volumes, overlap between neighboring units could serve important circuit functions (Wolff, 2014).

This work identifies 17 unique cell types that arborize in the protocerebral bridge. These fall into four classes: cells that 1) are intrinsic to the PB (n = 2), 2) are intrinsic to the central complex (an additional 6), 3) arborize in the FB, EB, or NO in addition to extra-central complex regions (e.g., the gall; n = 6), and 4) arborize exclusively in the PB and regions outside the central complex (n = 3). Cells that arborize in the PB receive their input from the EB, LAL, PS, IB, and also from within the PB. One cell previously identified in another study (Lin, 2013) was not targeted by any of the ∼35 lines analyzed in this work. To the extent that it is possible to construct wiring diagrams from the images shown in these two studies, it appears that the circuits for these cells are also identical between Drosophila and Schistocerca. In addition, one cell type was identified in this study that was not characterized in Lin (2013) (Wolff, 2014).

The combined total from this work and Lin (2013) brings the current number of identified cell types that arborize in the protocerebral bridge of Drosophila to 18. A potential 19th cell type was seen just twice, and in neither case could the entire cell be traced. Its PB arbor is spiny and very sparse, and while it clearly arborizes in the central brain, it is not clear if it arborizes elsewhere within the central complex. It is also possible that this "cell type" only constitutes a variant. There will likely be additional cells identified that arborize in the PB, although this number is predicted to be small. A complete inventory of all cells in the Drosophila brain awaits a full reconstruction at an electron microscope level (Wolff, 2014).

The wiring diagrams described in this study differ from published reports, in part due to the fact that previous authors were unaware of the existence of 18 glomeruli in the PB and therefore based their models on the historic interpretation that there are 16 glomeruli. This numerical revision and new insights into the anatomical substructure of the central complex components are the primary basis for revisions of existing circuit diagrams (Wolff, 2014).

Although there are 18 glomeruli, no small-field neuron arborizes in all 18 glomeruli. Instead, most cell types arborize in either G1–G8 or G2–G9. Each of these categories adheres to the following basic wiring principle: Cells that arborize in the lateral four glomeruli of each side of the PB stay ipsilateral in the second neuropil (either the FB or EB, depending on the neuron) and cross to the contralateral side at the third neuropil, whereas cells that arborize in the medial four glomeruli cross to the contralateral side in the second neuropil. Consequently, because there are two subsets of PB neurons, the glomerulus that targets a given column, wedge, or tile is shifted by one glomerulus, depending on the subset of cell type. Furthermore, the observation that no small-field neurons arborize in all 18 glomeruli suggests the number of columns in the FB and wedges in the EB would not need to exceed 16 in order to maintain a 1:1 correspondence between the PB and FB/EB (Wolff, 2014).

Arbors from cells that target the PB alternate with one another in the second neuropil such that arbors from the left glomeruli alternate with those from the right glomeruli. The PB wiring diagrams presented in this study differ somewhat from a recent account (Lin, 2013), as follows. The most lateral FB column (or EB wedge) is occupied not by the ipsilateral G9 (or G8 for the G1–G8 cells), as previously described, but instead by the contralateral G2 (or G1 for the G1–G8 cells). This circuit therefore reverses the pattern in the second neuropil (FB or EB) from one in which the most lateral (L) glomeruli project to the most lateral columns (or wedge or tile) on the ipsilateral side to one in which the medial (M) glomeruli from the contralateral half of the PB project to the most lateral columns. In other words, previously published diagrams indicate a pattern of LMLMLM from lateral to medial in the second neuropil, whereas this report shows that pattern to be MLMLML (Wolff, 2014).

The projection map shown in this study for cells that connect the PB to layer 1 of the FB illustrates conclusively the projection pattern between these domains. Obtaining an accurate map between the PB and layers 2 and 3 is difficult given the greater overlap between arbors of cells in these two layers, but the projection patterns observed between the PB and layers 2 and 3 of the FB are consistent with the PB:FBℓ1 map (Wolff, 2014).

As noted above, distribution of information is not always restricted to the subdomains of each central complex structure. When information is shared between neighboring domains (or alternating domains, in the case of the cells that arborize in the dorsal or ventral gall), generally only a small portion of the arbor is shared. The functional significance of these zones of overlap remains to be determined (Wolff, 2014).

Connections between central complex structures are remarkably restricted. For example, of those neurons that arborize in the PB and FB, only FB layers 1, 2, and 3 connect to the noduli, and only to NO2 and NO3. The only link between the PB and NO1 is via the ellipsoid body, so whereas NO2 and NO3 can be considered to work in conjunction with the FB to elicit a behavior based on output from the PB, NO1 cooperates with the EB to elicit a behavior based on output from the PB. In fact, communication is even more specific: layer 3 of the FB communicates directly only with NO2, layer 2 directly only with the anterior subcompartment of NO3, and layer 1 directly only with NO3M and NO3P. Furthermore, the cells in G1 do not communicate directly with the noduli at all—neither via the FB nor via the EB. The absence of direct connections between the PB and upper layers of the FB is also noteworthy. This streamlined and highly segregated network of connections within and between central complex structures suggests a high degree of regional specialization in function for the components of the central complex (Wolff, 2014).

The roles of central complex structures, their subdomains, and related neuropils are poorly understood. While functions remain largely unknown, many circuits described in this study are informative in various other ways. For example, some identify commonalities in function between neuropil subregions, such as FBℓ2 and NO3A, which are both arborized by a common neuron. Other circuits reveal spatial segregation between neuropils. The most intriguing instance is the exclusive relay of information between the ventral gall and even-numbered glomeruli and the dorsal gall and odd-numbered glomeruli, which demonstrates that both information and information flow can be spatially segregated from the glomeruli to the gall. It will be interesting to learn the functional role of the gall and why it segregates a portion of the information it receives and sends, as well as what sort of behavioral response requires this rigidly alternating spatial distribution in the PB. Finally, the absence of connections between neuropils may prove informative in functional studies. For example, G1 is distinct from G2–G8 in that it lacks direct connections with the noduli, and NO1 is distinct from NO2 and NO3 in that it communicates with the PB via the EB rather than the FB, raising the questions of what behaviors G1 does and does not contribute to, and what the differences are in behavioral outputs from NO1 and NO2/NO3 (Wolff, 2014).

The observation that there are 18 glomeruli in the Drosophila PB has significant implications for both the architecture and evolution of the Drosophila brain with respect to the brains of other neopterans. Although the suite of genetic tools available in locusts, bees, beetles, and other insects does not yet include MCFO, improvements in imaging and histology may prove sufficient to reevaluate the number of glomeruli in these species, given that glomeruli can be accurately counted in brains that are labeled only with nc82. Either some or all of these species also have 18 glomeruli, or an extra pair of glomeruli arose in Drosophila. The latter may be unlikely but would raise some intriguing possibilities about how the anatomical correspondence and circuitry between glomeruli in the PB and equivalent vertical partitions in the FB and EB PB circuits may differ between flies and other insects thought to have the same basic cellular composition and organization within the central complex, and how the geometric coordinates would then have had to shift along the axis of the PB to effect accurate behavioral responses (Wolff, 2014).

A comprehensive wiring diagram of the protocerebral bridge for visual information processing in the Drosophila brain

How the brain perceives sensory information and generates meaningful behavior depends critically on its underlying circuitry. The protocerebral bridge (PB) is a major part of the insect central complex (CX), a premotor center that may be analogous to the human basal ganglia. By deconstructing hundreds of PB single neurons and reconstructing them into a common three-dimensional framework, this study has constructed a comprehensive map of PB circuits with labeled polarity and predicted directions of information flow. The analysis reveals a highly ordered information processing system that involves directed information flow among CX subunits through 194 distinct PB neuron types. Circuitry properties such as mirroring, convergence, divergence, tiling, reverberation, and parallel signal propagation were observed; their functional and evolutional significance is discussed. This layout of PB neuronal circuitry may provide guidelines for further investigations on transformation of sensory (e.g., visual) input into locomotor commands in fly brains (Lin, 2014: PubMed).

Ring attractor dynamics in the Drosophila central brain

Ring attractors are a class of recurrent networks hypothesized to underlie the representation of heading direction. Such network structures, schematized as a ring of neurons whose connectivity depends on their heading preferences, can sustain a bump-like activity pattern whose location can be updated by continuous shifts along either turn direction. A population of fly neurons in the ellipsoid body has been shown to represent the animal's heading via bump-like activity dynamics (see Bump attractors and spontaneous pattern formation). This study combined two-photon calcium imaging in head-fixed flying flies with optogenetics to overwrite the existing population representation with an artificial one, which was then maintained by the circuit with naturalistic dynamics. A network with local excitation and global inhibition enforces this unique and persistent heading representation. Ring attractor networks have long been invoked in theoretical work; this study provides physiological evidence of their existence and functional architecture (Kim 2017).

Studies of neural circuits near the sensory periphery have produced deep mechanistic insights into circuit functions. However, it has been more challenging to understand circuit functions in central brain regions dominated by recurrent networks, which often produce complex neural activity patterns. These dynamics play a major role in shaping cognitive functions, such as the maintenance of heading information during navigation. A heading representation must be unique (because an animal can face only one direction at a given time) and persistent (to allow an animal to keep its bearings in darkness), yet must allow updating that matches the magnitude and speed of heading changes expected from the animal's movements. Theoretically, this can be accomplished by ring attractor networks (see, for example, Balanced neural architecture and the idling brain), wherein the position of a localized subset of active neurons in a topological ring represents the animal's heading direction. However, whether the brain uses these hypothesized networks is still unknown. A recent study reported that a population of neurons, called E-PG neurons [to signify their predominantly spiny (and, thus, putatively post-synaptic) projections within the ellipsoid body ('E-') and their predominantly bouton-like projections within the protocerebral bridge ('-P') and the gall ('G')], in the Drosophila melanogaster ellipsoid body (EB) appears to use bump-like neural activity dynamics to represent the animal's heading in visual environments and in darkness. This study establishes essential properties of the network that enables this representation (Kim 2017).

Whether the E-PG population activity bump tracks the fly's heading direction relative to its visual surroundings during tethered flight was determined first. Two-photon imaging with the genetically encoded calcium indicator GCaMP6f was performed to record dendritic calcium activity of the entire E-PG population in the EB while the fly was flying in a virtual-reality LED arena. The azimuthal velocity of the visual scene was proportional to the fly's yaw velocity. As with walking flies, E-PG population activity during flight was organized into a single bump, whether the visual scene contained a single bar or a more complex pattern. The activity bump closely tracked the fly's heading in flight and persisted in darkness. However, unlike in walking, the activity bump seldom tracked the fly's motor actions in darkness, potentially because tethering deprives the fly of normal sensory feedback about its rotational movements from its halteres. Although the location of the activity bump eventually drifted in some flies, the bump's movement was, on average, uncorrelated to the animal's turning movements in darkness. These findings suggest that the representation of heading in the E-PG population has intact, visually driven dynamics as well as persistence, but is largely uncoupled from updating by self-motion cues during tethered flight (Kim 2017).

To test whether the fly's compass network enforces a unique bump within the EB, advantage was taken of the relative persistence of the visually evoked activity bump in darkness, and asked whether this bump could coexist with an 'artificial' bump of activity. Localized optogenetic stimulation was used to create artificial activity bumps in different locations within the E-PG population. Using a transgenic fly line in which E-PG neurons coexpressed CsChrimsonand GCaMP6f, alternating two-photon laser scan lines of excitation (higher laser intensity) and imaging (normal laser intensity) were used to monitor changes in E-PG population dynamics in response to an optogenetically created spot of local activity. By varying the intensity of stimulation light delivered to the target location, bumps were created of increased calcium activity. As the new bump formed, activity at the previous location began to decline and eventually disappeared without significantly perturbing the fly's behavior. When the optogenetic excitation was terminated, the amplitude of the artificially created bump settled at levels typically evoked by sensory stimuli and did not disappear; it either stayed in the induced location for several seconds or slowly drifted away (Kim 2017).

The bump's uniqueness may arise through either recurrent mutual suppression or an indirect mechanism whereby strong bump activity in the EB functionally inhibits feedforward sensory inputs to other E-PG neurons. To discriminate between these alternatives, two locations on the EB ring were simultaneously excited. A reference location was excited at a fixed laser power, and a second, spatially offset location was excited at increasing levels of laser power. The reference bump could always be suppress by increasing laser power at the second location above a certain threshold, consistent with mutual suppression (Kim 2017).

Recurrent suppression can ensure a unique activity bump through a simple winner-take-all (WTA) circuit. However, an animal's representation of its angular orientation should favor more continuous updates based on turning actions. Such gradual, ordered drift to nearby locations would be more consistent with continuous, or ring, attractor models. This study therefore examined changes in the location of an artificially created bump after the stabilization of its peak activity at the 'natural' level. The experiments were performed in darkness to untether the bump from any potentially lingering visual input. If EB dynamics were driven by a WTA network, bumps would be expected to disappear at times and to jump to random distant locations. In contrast, the bump drifted gradually around the EB; this finding suggests that the fly's heading representation is updated through functionally excitatory interactions between neighboring E-PG neurons, consistent with a ring attractor model. These observations together rule out the possibility that network dynamics in darkness result purely from cell-intrinsic mechanisms or slowly decaying visual input. Most important, direct manipulation of E-PG neuron activity changed the network state, which implies that E-PG neurons do not merely mirror dynamics occurring in a different circuit, but are themselves an important component of the ring attractor (Kim 2017).

The next area of focus was the effective connectivity pattern underlying ring attractor dynamics in the E-PG population. A wide range of network structures can, in principle, implement ring attractors. This study focused efforts to a model space between two extreme network architectures that are analytically solvable: (1) a 'global model' based on global cosine-shaped interactions and (ii) a 'local model' based on relatively local excitatory interactions. Under constraints of a fixed bump width of 90° to match physiological observations and an assumption of effectively excitatory visual input without any negative bias, both models could explain the basic properties of bump dynamics, including its uniqueness and its persistence in darkness. The network's response to more artificial conditions, such as abrupt visual stimulus shifts, was therefore probed (Kim 2017).

How the E-PG population responded to unnatural, abrupt visual shifts was examined experimentally first. Depending on the distance of the shift, the E-PG bump either 'flowed' continuously (shorter shift distances) or 'jumped' to the new location (longer shift distances). In simulations, both models predicted a mixture of jump and flow responses, depending on the strength and width of the abruptly shifting visual input. For example, weak wide input induced flows and strong narrow input evoked jumps. However, the jump-flow balance predicted by the two models differed and was more consistent with the local model in several aspects. First, the visual input strength inferred from normal conditions was much weaker than required by the global model for bump jumps. Second, the global model required a much-wider-than-normal range of visual input strengths to explain jumps at multiple distances. Third, using parameters consistent with the rest of the findings, it was possible to reproduce the jump-flow ratio that was observed with the local model but not with the global model (Kim 2017).

To obtain more concrete evidence, model predictions were compared to experimentally observed bump dynamics, under conditions in which input strength, polarity, and shift distance were controlled through optogenetic stimulation. To simulate moderate and large input shift distances, two small regions were sequentially stimulated in the EB-each with an angular width of 22.5°-separated by either 90° or 180°. The stimulation laser power was varied to detect the threshold required for the bump to jump. The laser power required to elicit a jump was not significantly different between the two different shift distances, favoring the local model. The strength of input to the network was then inferred by comparing the amplitude of the optogenetically evoked bump to natural bump amplitudes in darkness. The optogenetic input strength required to induce jumps was smaller than the global model's prediction but matched that of the local model and the range of the inferred visual input strength under normal conditions. Finally, intermediate models that lie between the extremes of the local and global models were then test; any model that exhibited the observed jumps in response to a weak 22.5°-wide input had narrow connectivity profiles was then found. All these observations were once again consistent with the local model (Kim 2017).

In mammals, heading representations are thought to be distributed across multiple neural populations and multiple brain areas. In Drosophila as well, the compass system likely involves multiple cell types, including neurons in the protocerebral bridge (PB). Further, occasional changes observed in the dynamics suggest network modulation by other factors not yet known. For example, sometimes sudden changes were observed in E-PG dynamics, as when the amplitude of the sensory-evoked activity bump changed depending on whether or not the tethered fly was flying and, occasionally, during flight. Nonetheless, the E-PG population provides a powerful physiological handle on the internal representation of heading: a single activity bump moving through topographically arranged neurons. The experimental approach this enabled provides one avenue for investigating which of multiple populations are key circuit components of a computation and which simply read out the results of that computation. It was found that the artificial bump created by directly manipulating E-PG population activity displays natural dynamics, which indicates that these neurons are a key component of the heading circuit (Kim 2017).

The finding that the uniqueness of the E-PG activity bump is ensured via global competition strengthens the conclusion that this population encodes an abstract internal representation of the fly's heading direction. Such abstract representations permit an animal to untether its actions from the grasp of its immediate sensory environment and thereby confer flexibility in both time and behavioral use. Combining an analysis of artificially induced bump dynamics with theoretical modeling allowed interrogation of this recurrent circuit architecture. It was found that the effective network connectivity profile was consistent with ring attractor models characterized by narrow local excitation and flat long-range inhibition. This neural circuit motif of local excitation and long-range inhibition is ubiquitous across many brain areas and across animal taxa. Such observations support the idea that common circuit motifs might be evolutionarily adapted to serve as crucial building blocks of cognitive function (Kim 2017).

Neural dynamics for landmark orientation and angular path integration

Many animals navigate using a combination of visual landmarks and path integration. In mammalian brains, head direction cells integrate these two streams of information by representing an animal's heading relative to landmarks, yet maintaining their directional tuning in darkness based on self-motion cues. This study used two-photon calcium imaging in head-fixed Drosophila melanogaster walking on a ball in a virtual reality arena to demonstrate that landmark-based orientation and angular path integration are combined in the population responses of neurons whose dendrites tile the ellipsoid body, a toroidal structure in the centre of the fly brain. The neural population encodes the fly's azimuth relative to its environment, tracking visual landmarks when available and relying on self-motion cues in darkness. When both visual and self-motion cues are absent, a representation of the animal's orientation is maintained in this network through persistent activity, a potential substrate for short-term memory. Several features of the population dynamics of these neurons and their circular anatomical arrangement are suggestive of ring attractors, network structures that have been proposed to support the function of navigational brain circuits (Seelig, 2015).

Visual landmarks can provide animals with a reliable indicator of their whereabouts. In the absence of such cues, many animals track their position relative to a reference point by continuously monitoring their own motion, a process called path integration. Estimates of position based purely on self-motion cues, however, can accumulate error over time. Successful navigation then, requires animals to flexibly combine these distinct sources of information. In mammalian brains this process of integration is evident in head direction cells, which are neurons sensitive to an animal's heading relative to visual cues in its surroundings that maintain their representation of heading in total darkness using self-motion cues. With their smaller brains and identifiable neurons, insects offer tractable systems to examine the integrative neural computations underlying navigation. Indeed, many insects (for example, desert ants and honeybees) are known to navigate using landmarks and path integration. Experiments in a variety of insects indicate the involvement of the central complex (CX)-a brain region conserved across insects-in such behaviour. In the fruitfly, behavioural genetics experiments have suggested that the CX is required for several components of navigation, including memory for visual landmarks, patterns and places, and directional motor control. Electrophysiological recordings in immobilized locusts and butterflies have revealed a map-like representation for the orientation of electric field vectors of polarized light, which may enable sun-compass navigation. Extracellular recordings from CX neurons in tethered walking cockroaches have shown encodings of turning direction and of wide-field optic flow, a potential cue for self-motion. However, previous studies of visual responses in the CX were conducted under conditions in which insects passively viewed visual stimuli. This study sought to uncover integrative neural processes relevant to navigation in the CX by allowing a tethered fly to control and respond to visual stimuli while simultaneously recording its neural activity and behaviour (Seelig, 2015).

Two-photon imaging was used with the genetically encoded calcium indicator GCaMP6f to monitor neural responses in the CX while a head-fixed fly walked on an air-supported ball within a light-emitting-diode (LED) arena. In previous experiments, a subset of neurons was identified with projections to the CX, and specifically to rings of the ellipsoid body (EB), that show strong tuning to localized visual features including vertical stripes, a class of stimuli that also induce innate fixational responses in flies. To probe how such visual information might be used within the CX this study focused on a class of columnar neurons of the CX, each of which sends dendrites to a specific wedge of the EB. These neurons are termed EBw.s neurons. The dendritic responses of the entire EBw.s population were monitored in the EB during walking, both under closed-loop virtual reality conditions in which the rotation of visual patterns was driven by the fly's turning movement on the ball and in darkness (Seelig, 2015).

This network was found to use information from both landmark-based and angular path integration systems to create a compass-like representation of the animal's orientation in the environment. Previous studies have described static visual maps in the CX. Such maps may allow navigating insects to maintain a sun-compass-based heading direction. This study found that EBw.s neurons track the fly's orientation relative to visual landmarks in a variety of different visual environments, suggesting that the CX dynamically adapts to estimate the fly's orientation within its visual surroundings. Subsets of ring neurons are likely to bring information about spatially localized visual features to specific rings of the ellipsoid body. It is not yet clear how this information is converted into an abstract and flexible representation of the animal's orientation relative to landmarks, but EBw.s responses in a symmetric environment with two indistinguishable cues hint at an underlying winner-take-all process for landmark selection. Combining landmark orientation with information about the animal's movement effectively creates an internal reference frame for the animal in its surroundings. Many of the proposed functions of the CX in directed locomotion, visual place learning, and action-selection, may rely on this internal reference. Although the EBw.s population tracks the fly's rotational movements in darkness, it is not yet known where and how translational motion, an important component of a complete navigational system, is incorporated. Additionally, although the calcium sensor that was chosen for imaging experiments has the temporal resolution necessary to capture EBw.s representations of the fly's angular rotation, it lacks the precision necessary for determination of whether EBw.s activity represents the fly's predicted future orientation or its estimate of current orientation (Seelig, 2015).

The observation that EBw.s activity was maintained in the absence of self-motion suggests that internal dynamics play a significant role in shaping neural activity in the fly brain, much as they do in the brains of larger animals. Persistent activity in the CX can maintain compass information when the fly is standing in darkness for 30 s - two orders of magnitude longer than might be explained by calcium sensor decay kinetics. Persistent activity has been shown to support maintenance of eye position in the goldfish and has been proposed to underlie working memory in mammals. In the CX, this activity may allow the fly to retain a short-term orientation memory even when landmarks are temporarily out of sight. Consistent with this notion, the EBw.s activity bump largely remained tethered to the position of one landmark even in the presence of another identical landmark in front of the fly. The bump also did not always shift instantaneously following an abrupt displacement of visual landmarks, as if temporarily retaining the original orientation reference before locking on to its new position (Seelig, 2015).

Several models have been proposed to explain how visual landmark and self-motion cues are integrated at the level of head direction cell activity in mammals. Most rely on circuits organized as ring attractors: neurons are schematized as being arranged in a circle based on their preferred directions, with connection strengths that depend on their angular separation. With initial sensory input and an appropriate balance of recurrent excitation and inhibition, such a circuit can generate and sustain a localized activity bump. The bump's position on the circle corresponds to the animal's heading which is then updated by directional drive from self-motion signalling neurons. Direct experimental evidence in support of these models has been difficult to obtain in mammals owing to the distributed nature of the underlying circuits. Although the functional connectivity between EBw.s neurons is not yet known, several of the expected features of ring attractor models were observed in the dynamics of this population of CX neurons: organization of activity into a localized bump, movement of the bump to neighbouring wedges based on self-motion, drift in bump location in darkness, persistent activity, and both abrupt jumps and gradual transitions of the activity bump when triggered by strong visual input. Cell-intrinsic mechanisms could also underlie some of these features, including, for example, persistent activity. The genetic tools available in Drosophila to target and manipulate the activity of identified cell types should allow different models for visually guided orientation and angular path integration to be discriminated at the level of synaptic, cellular and network mechanism (Seelig, 2015).

Visual input to the Drosophila central complex by developmentally and functionally distinct neuronal populations

The Drosophila central brain consists of developmental-structural units of macrocircuitry formed by the sibling neurons of single neuroblasts. Lineage guides the connectivity and function of neurons, providing input to the central complex, a collection of neuropil compartments important for visually guided behaviors. The ellipsoid body (EB) is formed largely by the axons of ring (R) neurons, all of which are generated by a single lineage, DALv2. Two further lineages, DALcl1 and DALcl2, produce neurons that connect the anterior optic tubercle, a central brain visual center, with R neurons. Finally, DALcl1/2 receive input from visual projection neurons of the optic lobe medulla, completing a three-legged circuit that is called the anterior visual pathway (AVP). The AVP bears a fundamental resemblance to the sky-compass pathway, a visual navigation circuit described in other insects. DALcl1 and DALcl2 form two parallel channels, establishing connections with R neurons located in the peripheral and central domains of the EB, respectively. Although neurons of both lineages preferentially respond to bright objects, DALcl1 neurons have small ipsilateral, retinotopically ordered receptive fields, whereas DALcl2 neurons share a large excitatory receptive field in the contralateral hemifield. DALcl2 neurons become inhibited when the object enters the ipsilateral hemifield and display an additional excitation after the object leaves the field of view. Thus, the spatial position of a bright feature, such as a celestial body, may be encoded within this pathway (Omoto, 2017).

Functional divisions for visual processing in the central brain of flying Drosophila

Although anatomy is often the first step in assigning functions to neural structures, it is not always clear whether architecturally distinct regions of the brain correspond to operational units. Whereas neuroarchitecture remains relatively static, functional connectivity may change almost instantaneously according to behavioral context. This study imaged panneuronal responses to visual stimuli in a highly conserved central brain region in the fruit fly, Drosophila, during flight. In one substructure, the fan-shaped body, automated analysis reveals three layers that are unresponsive in quiescent flies but become responsive to visual stimuli when the animal is flying. The responses of these regions to a broad suite of visual stimuli suggest that they are involved in the regulation of flight heading. To identify the cell types that underlie these responses, activity was imaged in sets of genetically defined neurons with arborizations in the targeted layers. The responses of this collection during flight also segregate into three sets, confirming the existence of three layers, and they collectively account for the panneuronal activity. These results provide an atlas of flight-gated visual responses in a central brain circuit (Weir, 2015).

The visual orientation memory of Drosophila requires Foraging (PKG) upstream of Ignorant (RSK2) in ring neurons of the central complex

Orientation and navigation in a complex environment requires path planning and recall to exert goal-driven behavior. Walking Drosophila flies possess a visual orientation memory for attractive targets which is localized in the central complex of the adult brain. This study shows that this type of working memory requires the cGMP-dependent protein kinase encoded by the foraging gene in just one type of ellipsoid-body ring neurons. Moreover, genetic and epistatic interaction studies provide evidence that Foraging functions upstream of the Ignorant Ribosomal-S6 Kinase 2, thus revealing a novel neuronal signaling pathway necessary for this type of memory in Drosophila (Kuntz, 2012).

For signaling has previously been implicated in different types of memories; however, in contrast to the working memory in the detour paradigm, these memories require a longer time frame to be established. In mammals, nitric oxide, the initiating molecule of the cGMP/PKG-pathway, is thought to act as a retrograde messenger during the induction of long-term potentiation (LTP). A LTP enhancement has been reported after adding PKG activators and a long-term depression after the addition of PKG inhibitors. Mice carrying a knock-out for the Pkg gene show reduced ability of motor learning due to a loss of synaptic plasticity in the cerebellum. Furthermore, mice lacking Pkg in the amygdala exhibit an impairment in fear conditioning and cGMP/PKG signaling in the hippocampus is required for novel object recognition. In insects, For is involved in different types of food searching behavior and associative memories in which establishing the learning traces takes at least seconds. In contrast, the orientation memory observed in the detour paradigm presented in this study represents a form of working memory which has to be updated continuously in fractions of seconds. Whereas the phosphorylation and activation of For and Ignorant might be the mechanism by which these kinases affect longer-lasting memories, it is thought unlikely that this mechanism is involved in the constantly and rapidly changing orientation memory. Both kinases would have to be activated or inactivated in an online fashion during every turn of the fly. On the other hand, RSK2 has been implicated in multiple cellular processes and transcriptional control. It is therefore speculated that the biochemical pathway both kinases work in is necessary to endow the ring neurons with the capacity to efficiently change signaling rapidly to encode orientation. For instance, ring neurons might need a higher density of synaptic release sites and/or dendritic neurotransmitter receptors to exert their specific function (Kuntz, 2012).

Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. This study investigated the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called 'ring neurons', projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing an investigation of how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, this study shows that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons' receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, it was then assessed how easy it is to decode more general information about stimulus shape from the ring neuron population codes. These neurons were shown to be well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads to a suggestion that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour (Dewar, 2017).

Specific kinematics and motor-related neurons for aversive chemotaxis in Drosophila

Chemotaxis, the ability to direct movements according to chemical cues in the environment, is important for the survival of most organisms. The vinegar fly, Drosophila melanogaster, displays robust olfactory aversion and attraction, but how these behaviors are executed via changes in locomotion remains poorly understood. In particular, it is not clear whether aversion and attraction bidirectionally modulate a shared circuit or recruit distinct circuits for execution. Using a quantitative behavioral assay, this study has determined that both aversive and attractive odorants modulate the initiation and direction of turns but display distinct kinematics. Using genetic tools to perturb these behaviors, specific populations of neurons were identified that are required for aversion, but not for attraction. Inactivation of these populations of cells affects the completion of aversive turns, but not their initiation. Optogenetic activation of the same populations of cells triggers a locomotion pattern resembling aversive turns. Perturbations in both the ellipsoid body and the ventral nerve cord, two regions involved in motor control, were shown to result in defects in aversion. It is concluded that aversive chemotaxis in vinegar flies triggers ethologically appropriate kinematics distinct from those of attractive chemotaxis and requires specific motor-related neurons (Gao, 2013).

This study is the first direct demonstration that odorants modulate turn initiation and direction in freely walking insects. Moreover, aversive and attractive turns involve distinct kinematics. Intuitively, these quantitative analyses reveal that flies speed up and follow straighter trajectories after turning away from a noxious smell, which should shorten their exposure to potential harm. Such a strategy is not employed for attraction. Chemotaxis has been studied in tethered adult flies, paradigms in which mimicking the olfactory inputs a freely moving fly would encounter proved challenging. For example, in one group of studies, a 'fly-on-the-ball' paradigm, aversion was not triggered even using a strong repellent. In another study, flying flies responded symmetrically to aversive and attractive odorants. The current more naturalistic approach provides new insights into the relationship between aversive and attractive chemotaxes (Gao, 2013).

In bacteria, aversion and attraction are achieved through bidirectional modulation of the same mechanism. Similarly, in C. elegans, aversion and attraction are thought to utilize a push-pull mechanism on one set of antagonizing command neurons (Faumont, 2012). Genetic inactivation experiments suggest that, in flies, aversion is executed through specific neurons distinct from attraction. In this study. two candidate circuit components, the EB and a subset of the VNC neurons, were identified that appear redundantly necessary for aversive chemotaxis. The EB is part of the central complex, defects in which are associated with uncoordinated walking. In grasshoppers and cockroaches, activating central complex neurons induces specific kinematics. In the VNC, dTdc2+ neurons are prominent candidates for mediating aversion, although other neurons might be involved. These othere neurons are homologous to dorsal/ventral unpaired median neurons in other insects because they are octopaminergic and show similar projection patterns]. The activity in these neurons is correlated with specific aspects of locomotion in locusts, crickets, and moths. Given that 441 > shits1 flies display defects in aversive turn completion but not initiation, it is postulated that the current genetic manipulation does not interfere with the perception, processing, decision-making, or even initiation steps of aversive chemotaxis, but rather the execution of motor programs specifically necessary for this behavior. These discoveries bridge the extensive investigation of olfactory processing in insects such as honeybees and moths with studies focused on motor control mechanisms in species such as cockroaches and stick insects (Gao, 2013).

It is intriguing that part of the aversion-specific circuit resides in the VNC, and can be artificially activated to generate a pattern similar to aversive turns. Although the larval VNC is sufficient for substrate exploration, VNC autonomy in motor pattern generation in adult flies has only been established for escape flight and courtship song, both highly specialized for certain ethological functions. When a fly continuously explores the environment and updates its walking pattern, the division of labor between the brain and the VNC is less clear. Conceptually, one possibility is that circuit modules in the VNC only encode basic elements of locomotion. For example, right turns may always involve the same VNC circuit, and the only difference is their embedding within different sequences of actions based on the combination of descending signals from the brain. Alternatively, VNC circuit modules could be task-specific; once a descending signal specifies the task, the details of the motor output will unfold according to a pre-wired VNC circuit. The current findings support the latter possibility in the context of aversive chemotaxis. In both vertebrates and invertebrates, artificial activation of neurons in the spinal cord or the VNC generates specific motor outputs, but rarely have these neurons been demonstrated to be necessary for specific sensory-driven tasks. It would be interesting to test the generality of having autonomous motor-related circuits specifically responsive to certain sensory triggers (Gao, 2013).

Two clusters of GABAergic ellipsoid body neurons modulate olfactory labile memory in Drosophila

In Drosophila, aversive olfactory memory is believed to be stored in a prominent brain structure, the mushroom body (MB), and two pairs of MB intrinsic neurons, the dorsal paired medial (DPM) and the anterior paired lateral (APL) neurons, are found to regulate the consolidation of middle-term memory (MTM). This study reports that another prominent brain structure, the ellipsoid body (EB), is also involved in the modulation of olfactory MTM. Activating EB R2/R4m neurons does not affect the learning index, but specifically eliminates anesthesia-sensitive memory (ASM), the labile component of olfactory MTM. It was further demonstrated that approximately two-thirds of these EB neurons are GABAergic and are responsible for the suppression of ASM. Using GRASP (GFP reconstitution across synaptic partners), potential synaptic connections were revealed between the EB and MB in regions covering both the presynaptic and postsynaptic sites of EB neurons, suggesting the presence of bidirectional connections between these two important brain structures. These findings suggest the existence of direct connections between the MB and EB, and provide new insights into the neural circuit basis for olfactory labile memory in Drosophila (Zhang, 2014).

Previous studies have shown that the EB plays an essential role in visual pattern memory, orientation memory and place learning (Pan, 2009; Ofstad, 2011; Kuntz, 2012), and thus it is usually considered to be a center of visual learning and mem- ory. Interestingly, one study on NMDA receptors reported that the EB is required for olfactory long-term memory (LTM) consolidation; however, the underlying neural circuits remain uninvestigated. The current results reveal that a group of EB neurons, the c819-labeled R2/R4m neurons, plays an inhibitory role in the modulation of MTM but not the immediate memory. This points to a new function of the EB in olfactory cognition and further demonstrates that the EB could be involved in the process of olfactory aversive learning and memory from an earlier stage than previously thought (Zhang, 2014).

The presence of dense GABA-like immunoreactivity has been demonstrated in the EB ring and RF tract suggesting that the bulk of EB neurons are GABAergic. Although this finding has been confirmed by several other studies, the function of these GABAergic neurons in cognition is still unclear. The current results further reveal that approximately two-thirds of the c819-EB neurons are GABAergic, and they play an inhibitory role in ASM modulation. The GABAA receptor, resistant to dieldrin (RDL), has been shown to be highly expressed in the MB lobes and the EB. It is thus possible that these EB GABAergic neurons function through RDL receptors (Zhang, 2014).

As a component of MTM, ASM has been suggested to be stored in the MB and to be modulated by MB intrinsic APL and DPM neurons, of which the neural terminals are restricted to the MB. This study reports the EB, a brain structure separate from the MB, is also involved in the modulation of ASM. WEB neurons may be both presynaptic and postsynaptic to MB neurons, suggesting that they may suppress 3 h ASM via putative direct connections between the EB and MB. That the MB and EB are two discrete but possibly interconnected and interacting brain regions, suggests that it is important to study the process of learning and memory over a more widely distributed neural network. However, the interaction between neurons from different structures may endow the network with greater capacity for more complex activities (Zhang, 2014).

It is also interesting to discover that activating c819-EB GABAergic neurons during training impaired 3 h ASM instead of immediate learning performance. Recently it has been reported that blocking two pairs of dopaminergic neurons during intertrial intervals in spaced training suppresses the formation of 24 h LTM by interfering with the slow oscillations in these dopaminergic neurons. Since this study has shown that bidirectional connections may exist between the EB and MB, it is proposed that there may be a small feedback circuit between the EB and MB, which may have prolonged oscillations and therefore affect 3 h ASM consolidation. Further functional imaging studies may provide more clues on how this neural circuit functions (Zhang, 2014).

Spatio-temporal in vivo recording of dCREB2 dynamics in Drosophila long-term memory processing

CREB (cAMP response element-binding protein) is an evolutionarily conserved transcription factor, playing key roles in synaptic plasticity, intrinsic excitability and long-term memory (LTM) formation. The Drosophila homologue of mammalian CREB, dCREB2, is also important for LTM. However, the spatio-temporal nature of dCREB2 activity during memory consolidation is poorly understood. Using an in vivo reporter system, this study examined dCREB2 activity continuously in specific brain regions during LTM processing. Two brain regions that have been shown to be important for Drosophila LTM are the ellipsoid body (EB) and the mushroom body (MB). dCREB2 reporter activity is persistently elevated in EB R2/R4m neurons, but not neighboring R3/R4d neurons, following LTM-inducing training. In multiple subsets of MB neurons, dCREB2 reporter activity is suppressed immediately following LTM-specific training, and elevated during late windows. In addition, heterogeneous responses were observed across different subsets of neurons in MB αβ lobe during LTM processing. All of these changes suggest that dCREB2 functions in both the EB and MB for LTM formation, and that this activity contributes to the process of systems consolidation (Zhang, 2014).

A transcriptional reporter of intracellular Ca(2+) in Drosophila

Intracellular Ca(2+) is a widely used neuronal activity indicator. This study describes a transcriptional reporter of intracellular Ca(2+) (TRIC) in Drosophila that uses a binary expression system to report Ca(2+)-dependent interactions between calmodulin and its target peptide. In vitro assays predicted in vivo properties of TRIC. TRIC signals in sensory systems were show to depend on neuronal activity. TRIC was able to quantitatively monitor neuronal responses that changed slowly, such as those of neuropeptide F-expressing neurons to sexual deprivation and neuroendocrine pars intercerebralis (PI) cells to food and arousal. Furthermore, TRIC-induced expression of a neuronal silencer in nutrient-activated cells enhanced stress resistance, providing a proof of principle that TRIC can be used for circuit manipulation. Thus, TRIC facilitates the monitoring and manipulation of neuronal activity, especially those reflecting slow changes in physiological states that are poorly captured by existing methods. TRIC's modular design should enable optimization and adaptation to other organisms (Gao, 2015).

Using cultured cells and multiple in vivo assays, this study found that TRIC reports changes in Ca2+ levels under diverse conditions in visual, olfactory and neuromodulatory systems. The results provide quantitative assessments for choosing TRIC variants with appropriate sensitivity and stringency, and proof of principle that TRIC can be used to express a circuit manipulator. Thus, TRIC acts as a useful complement to functional Ca2+ imaging by integrating changes in activity over long periods of time and offering genetic access to neurons on the basis of their activity (Gao, 2015).

Vertebrate immediate-early-gene (IEGs) which evolved to be expressed in a high signal-to-baseline ratio in response to neuronal activation, are widely used to report neuronal activity. However, as they rely on endogenous signaling networks, their response properties and cell-type biases are difficult to modify. TRIC can be considered a rationally designed IEG, by exogenously introducing a protein-peptide interaction to detect Ca2+. The modular design of TRIC renders it more amenable to optimization. TRIC reports a rise in nuclear Ca2+ levels, which have previously been used to monitor pan-neuronal activity in C. elegans, and also accompanies neuronal activation in mammalian neurons likely shuttled by Ca2+-binding proteins. The current experiments indicate that nuclear Ca2+ correlates with activity in diverse neuronal classes in flies. It is likely that not all cell types have the same efficiency in converting cytoplasmic Ca2+ signal to nuclear Ca2+ signal. Thus, TRIC efficiency and optimization may differ for different neuronal types (Gao, 2015).

While this manuscript was in review, a Ca2+ integrator (CaMPARI) was reported in which the ultraviolet conversion of emission spectrum of a fluorescent protein was engineered to be contingent on Ca2+ concentration. CaMPARI can capture neuronal activity on a shorter time scale than TRIC or IEG. However, access of neurons to ultraviolet may limit the use of CaMPARI in deep tissues, at least in large animals, whereas TRIC and IEG report neuronal activity in the entire nervous system non-invasively. Notably, unlike CaMPARI or IEG, TRIC offers genetic access to active neurons, allowing activity-based circuit manipulation (Gao, 2015).

The results underscore the importance of optimizing TRIC for specific neuronal types. In this study, TRIC was optimized for multiple cell types, and many variants were described that can help users in other cells. It is recommended that users begin with CaM/MKII-mediated TRIC in their neurons of interest. If TRIC signal is detected, the users can attempt QA-mediated or FLP-mediated regulation of the timing of TRIC onset. The signal-to-baseline ratio can be further improved by titrating expression of TRIC using QA, choosing reporters with different stabilities, or switching to nlsLexADBDo or the MKIIK11A variant. Stoichiometry can also be leveraged to boost TRIC signal (Gao, 2015).

With the current version of TRIC, the signal accumulates and decays over many hours. To detect shorter periods of neuronal activity, an important future goal is to increase signal strength while avoiding saturation by basal Ca2+ concentrations. One solution to this problem would be to restrict TRIC to a narrower time window than that offered by the QA- or the FLP-mediated strategy. For example, TRIC could be split into DBD-X, Y-target peptide and CaM-AD, where X and Y are two interacting modules controlled by light. One could then synchronize TRIC with a specific manipulation, or even trigger TRIC repetitively with specific behavioral features using feedback from automated tracking. To preserve phasic information about neuronal activity, reporters with faster decays than CD8::GFP could be used or the TRIC components could be destabilized with tags for protein degradation. Given that the current TRIC was able to interact with endogenous CaM and its target peptides, another important direction is to 'isolate' TRIC by co-engineering the CaM and MKII components to lose binding to their endogenous partners, but maintain their mutual interaction. Future TRIC optimization could be achieved using high throughput screens in cultured cells, which can predict in vivo performance (Gao, 2015).

Previous studies used Ilp2 immunostaining, epitope-tagged Ilp2 or a secreted GFP as indirect indicators of PI activity. The major conclusions of these studies were validated using TRIC. After enhancing the dynamic range of TRIC, additional insight was gained into how PI activity is regulated. In particular, given that PI cells affect diverse processes, how do these cells determine their output according to all relevant inputs? For example, an animal may encounter conflicting metabolic needs, such as conserving energy versus defending territory in an impoverished environment. Nutrient and OA comparison could be viewed as a minimal model of such a dilemma, as OA contributes to arousal and is necessary for 'fight or flight' in insects. PI cells exhibited graded, yet more readily saturated, responses to such events. In contrast, the linear PI response to nutrients extended over a wider range. These distinctions, as well as the additive interaction between yeast and OA, point to the independent operation of these two categories of inputs. To further survey the input landscape, one could genetically manipulate candidate receptors autonomously or candidate upstream neurons non-autonomously while monitoring PI activity using TRIC (Gao, 2015).

The physiological states of flies can change over hours to days and can be accompanied by changes in the activities of neurons expressing modulatory neurotransmitters or neuropeptides. Although previous work has focused on the targets of modulatory neurotransmitters, inputs to these cells remain largely unknown. In addition, there are ~75 predicted neuropeptides in flies, only a small subset of which have been examined. TRIC can be applied to neurons expressing specific transmitters or neuropeptides and tested in different physiological states (for example, the NPF neurons). It is noted that the current TRIC variants might not fit the dynamic range of all neuronal types, and it might be necessary to test other AD/DBD ratios or other MKII mutants following the examples in this paper of optimization for PI cells (Gao, 2015).

Finally, TRIC can report a rise of intracellular Ca2+ that accompanies any cellular, developmental or physiological processes in flies and can be adapted for similar use in other model organisms. TRIC modules can be introduced as transgenes or by viral vectors, and specific stoichiometry can be achieved by specifying the number of AD and DBD sequences in multi-cistronic constructs. TRIC expression can be made contingent on recombinase or other binary systems in model organisms, such as mice, where many Cre lines are available for spatiotemporal control, which can help refine activity monitoring and circuit manipulation in specific cell types (Gao, 2015).

Metabolic learning and memory formation by the brain influence systemic metabolic homeostasis

Metabolic homeostasis is regulated by the brain, but whether this regulation involves learning and memory of metabolic information remains unexplored. This study use a calorie-based, taste-independent learning/memory paradigm to show that Drosophila form metabolic memories that help in balancing food choice with caloric intake; however, this metabolic learning or memory is lost under chronic high-calorie feeding. Loss of individual learning/memory-regulating genes causes a metabolic learning defect, leading to elevated trehalose and lipid levels. Importantly, this function of metabolic learning requires not only the mushroom body but also the hypothalamus-like pars intercerebralis, while NF-κB activation in the pars intercerebralis mimics chronic overnutrition in that it causes metabolic learning impairment and disorders. Finally, this study evaluated this concept of metabolic learning/memory in mice, suggesting that the hypothalamus is involved in a form of nutritional learning and memory, which is critical for determining resistance or susceptibility to obesity. In conclusion, these data indicate that the brain, and potentially the hypothalamus, direct metabolic learning and the formation of memories, which contribute to the control of systemic metabolic homeostasis (Zhang, 2015).

This work consists of a series of studies in Drosophila: these animals were found to temporarily develop metabolic learning to balance food choice with caloric intake. In Drosophila research, sugar has often been used for studying the appetitive reward value of food taste. Of interest, recent research has suggested that fruit flies can distinguish caloric values from the taste property of food. Using tasteless sorbitol as a carbohydrate source to generate an environmental condition that contained NC versus HC food, this study revealed that Drosophila can develop a form of metabolic learning and memory independently of taste, by which flies are guided to have a preference for normal caloric environment rather than high-caloric environment. However, this form of metabolic memory does not seem robust, as it is vulnerably diminished under genetic or environmental influences. It is postulated that this vulnerability to overnutrition is particularly prominent for mammals (such as C57BL/6J mice), and overnutritional reward-induced excess in caloric intake can quickly become dominant. This effect can be consistently induced in Drosophila when learning/memory-regulating genes are inhibited in the brain or the hypothalamus-like PI region. It was observed that each of these genetic disruptions led to impaired metabolic learning, resulting in increased caloric intake and, on a chronic basis, the development of lipid excess and diabetes-like phenotype. Indeed, it has been documented that chronic high-sugar feeding is sufficient to cause insulin resistance, obesity and diabetes in Drosophila. It is yet unclear whether this metabolic learning can induce an appetitive memory of normal caloric environment or an aversive memory of high-caloric environment. Regardless, the findings in this work have provoked a stimulating question, that is, whether this form of metabolic learning and memory is present in the mammals and, if so, whether this mechanism can be consolidated to improve the control of metabolic physiology and prevent against diseases. These mouse studies may provide an initial support to this concept and strategy, but clearly, in-depth future research is much needed (Zhang, 2015).

In light of the underlying neural basis for this metabolic learning, this study indicates that multiple brain regions are required, including the hypothalamus-like PI region in addition to the MB (equivalent to the hippocampus in mammals), which is classically needed for learning and memory formation. Anatomically, the PI region is located in the unpaired anteromedial domain of the protocerebral cortex, which is near the calyces of the MB and the dorsal part of the central complex (another brain region for regulating learning and memory). Functionally, the PI region has been demonstrated to coordinate with the MB in regulating various physiological activities in Drosophila. Thus, it is very possible that some PI neurons present nutritional information to the MB and thus induce metabolic learning and memory formation. However, the underlying detailed mechanism is still unknown, especially if this process involves a role of dilps, which represent the prototypical neuropeptides produced by the PI neurons. Considering that the PI region in flies is equivalent to the mammalian hypothalamus, this study was extended to mouse models by comparatively analysing A/J versus C57BL/6J mice—which are known to have different diet preference as well as different susceptibilities to obesity development. While A/J mice showed a learning process of distinguishing NC versus HC food, C57BL/6J mice failed to do so. It is particularly notable that this difference of learning and memory between these two strains is associated with differential expression profiles of learning/memory genes in the hypothalamus rather than the hippocampus. This finding, in conjunction with the Drosophila study, highlights a potential that the hypothalamus has a unique role in mediating metabolic learning and memory formation. Although the mouse experiments cannot exclude the impacts from the taste/smell properties of the studied food, the results demonstrated that there is a form of nutritional memory, which seems dissociable from the memory of overnutritional reward. These initial observations in mice lend an agreement with findings in Drosophila, suggesting that the brain and potentially the hypothalamus can link nutritional environment to a form of metabolic learning and memory homeostasis (Zhang, 2015).

From a disease perspective, metabolic learning in Drosophila is impaired under chronic overnutrition, and the mouse study was in line with this understanding. This response to overnutrition is useful when famine is outstanding; however, it is a dilemma when metabolic disease is of concern, much like the scenario pertaining to leptin resistance under chronic overnutrition, whereas an increase in leptin sensitivity is demanded to reduce obesity. Recently, it was established that NF-κB-dependent hypothalamic inflammation links chronic overnutrition to the central dysregulation of metabolic balance. This study showed that activation of the NF-κB pathway in the PI region weakened the function of metabolic learning and, conversely, NF-κB inhibition in this region provided a protective effect against chronic overnutrition-impaired metabolic learning. These findings are in alignment with the literature, for example, pan-neuronal NF-κB inhibition was shown to improve activity-dependent synaptic signalling and cognitive function including learning and memory formation, and persistent NF-κB activation inhibits neuronal survival and the function of learning and memory formation. Hence, overnutrition-induced neural NF-κB activation has a negative impact on metabolic learning and memory formation in regulating metabolic homeostasis homeostasis (Zhang, 2015).

To summarize the findings in this work, a series of behavioural studies was performed revealing that Drosophila have a form of metabolic learning and memory, through which the flies are directed to balance food choice with caloric intake in relevant environments. Several learning/memory-regulating genes including rut, dnc and tequila are involved in this function, and brain regions including the PI in addition to the MB are required to induce this mechanism. On the other hand, metabolic learning is impaired under chronic overnutrition through NF-κB activation, leading to excess exposure to calorie-enriched environment, which causes metabolic disorders. Overall, metabolic learning and memory formation by the brain and potentially the hypothalamus play a role in controlling metabolic homeostasis homeostasis (Zhang, 2015).

Sleep drive is encoded by neural plastic changes in a dedicated circuit

Prolonged wakefulness leads to an increased pressure for sleep, but how this homeostatic drive is generated and subsequently persists is unclear. From a neural circuit screen in Drosophila, this study identified a subset of ellipsoid body (EB) neurons whose activation generates sleep drive. Patch-clamp analysis indicates these EB neurons are highly sensitive to sleep loss, switching from spiking to burst-firing modes. Functional imaging and translational profiling experiments reveal that elevated sleep need triggers reversible increases in cytosolic Ca(2+) levels, NMDA receptor expression, and structural markers of synaptic strength, suggesting these EB neurons undergo 'sleep-need'-dependent plasticity. Strikingly, the synaptic plasticity of these EB neurons is both necessary and sufficient for generating sleep drive, indicating that sleep pressure is encoded by plastic changes within this circuit. These studies define an integrator circuit for sleep homeostasis and provide a mechanism explaining the generation and persistence of sleep drive (Liu, 2016).

Operation of a homeostatic sleep switch

In Drosophila, a crucial component of the machinery for sleep homeostasis is a cluster of neurons innervating the dorsal fan-shaped body (dFB) of the central complex. dFB neurons in sleep-deprived flies tend to be electrically active, with high input resistances and long membrane time constants, while neurons in rested flies tend to be electrically silent. This study demonstrates state switching by dFB neurons, identifies dopamine as a neuromodulator that operates the switch, and delineates the switching mechanism. Arousing dopamine causes transient hyperpolarization of dFB neurons within tens of milliseconds and lasting excitability suppression within minutes. Both effects are transduced by Dop1R2 receptors and mediated by potassium conductances. The switch to electrical silence involves the downregulation of voltage-gated A-type currents carried by Shaker and Shab, and the upregulation of voltage-independent leak currents through a two-pore-domain potassium channel that was termed Sandman. Sandman is encoded by the CG8713 gene and translocates to the plasma membrane in response to dopamine. dFB-restricted interference with the expression of Shaker or Sandman decreases or increases sleep, respectively, by slowing the repetitive discharge of dFB neurons in the ON state or blocking their entry into the OFF state. Biophysical changes in a small population of neurons are thus linked to the control of sleep-wake state (Pimentel, 2016).

Recordings were made from dFB neurons (which were marked by R23E10-GAL4 or R23E10-lexA-driven green fluorescent protein (GFP) expression) while head-fixed flies walked or rested on a spherical treadmill. Because inactivity is a necessary correlate but insufficient proof of sleep, the analysis was restricted to awakening, which is defined as a locomotor bout after >5 min of rest, during which the recorded dFB neuron had been persistently spiking. To deliver wake-promoting signals, the optogenetic actuator CsChrimson was expressed under TH-GAL4 control in the majority of dopaminergic neurons, including the PPL1 and PPM3 clusters, whose fan-shaped body (FB)-projecting members have been implicated in sleep control. Illumination at 630 nm, sustained for 1.5 s to release a bolus of dopamine, effectively stimulated locomotion. dFB neurons paused in successful (but not in unsuccessful) trials, and their membrane potentials dipped by 2-13 mV below the baseline during tonic activity. When flies bearing an undriven CsChrimson transgene were photostimulated, neither physiological nor behavioural changes were apparent. The tight correlation between the suppression of dFB neuron spiking and the initiation of movement might, however, merely mirror a causal dopamine effect elsewhere, as TH-GAL4 labels dopaminergic neurons throughout the brain. Because localized dopamine applications to dFB neuron dendrites similarly caused awakening, this possibility is considered remote (Pimentel, 2016).

Flies with enhanced dopaminergic transmission exhibit a short-sleeping phenotype that requires the presence of a D1-like receptor in dFB neurons, suggesting that dopamine acts directly on these cells. dFB-restricted RNA interference (RNAi) confirmed this notion and pinpointed Dop1R2 as the responsible receptor, a conclusion reinforced by analysis of the mutant Dop1R2MI08664 allele. Previous evidence that Dop1R1, a receptor not involved in regulating baseline sleep, confers responsiveness to dopamine when expressed in the dFB indicates that either D1-like receptor can fulfill the role normally played by Dop1R2. Loss of Dop1R2 increased sleep during the day and the late hours of the night, by prolonging sleep bouts without affecting their frequency. This sleep pattern is consistent with reduced sensitivity to a dopaminergic arousal signal (Pimentel, 2016).

To confirm the identity of the effective transmitter, avoid dopamine release outside the dFB, and reduce the transgene load for subsequent experiments, optogenetic manipulations of the dopaminergic system were replaced with pressure ejections of dopamine onto dFB neuron dendrites. Like optogenetically stimulated secretion, focal application of dopamine hyperpolarized the cells and suppressed their spiking. The inhibitory responses could be blocked at several nodes of an intracellular signalling pathway that connects the activation of dopamine receptors to the opening of potassium conductances: by RNAi-mediated knockdown of Dop1R2; by the inclusion in the patch pipette of pertussis toxin (PTX), which inactivates heterotrimeric G proteins of the Gi/o family; and by replacing intracellular potassium with caesium, which obstructs the pores of G-protein-coupled inward-rectifier channels. Elevating the chloride reversal potential above resting potential left the polarity of the responses unchanged, corroborating that potassium conductances mediate the bulk of dopaminergic inhibition (Pimentel, 2016).

Coupling of Dop1R2 to Gi/o, although documented in a heterologous system, represents a sufficiently unusual transduction mechanism for a predicted D1-like receptor to prompt verification of its behavioural relevance. Like the loss of Dop1R2, temperature-inducible expression of PTX in dFB neurons increased overall sleep time by extending sleep bout length (Pimentel, 2016).

While a single pulse of dopamine transiently hyperpolarized dFB neurons and inhibited their spiking, prolonged dopamine applications (50 ms pulses at 10 Hz, or 20 Hz optogenetic stimulation, both sustained for 2-10 min) switched the cells from electrical excitability (ON) to quiescence (OFF). The switching process required dopamine as well as Dop1R2, but once the switch had been actuated the cells remained in the OFF state-and flies, awake-without a steady supply of transmitter. Input resistances and membrane time constants dropped to 53.3 ± 1.8 and 24.0 ± 1.3% of their initial values (means ± s.e.m.), and depolarizing currents no longer elicited action potentials (15 out of 15 cells). The biophysical properties of single dFB neurons, recorded in the same individual before and after operating the dopamine switch, varied as widely as those in sleep-deprived and rested flies (Pimentel, 2016).

Dopamine-induced changes in input resistance and membrane time constant occurred from similar baselines in all genotypes and followed single-exponential kinetics with time constants of 1.07-1.10 min. The speed of conversion points to post-translational modification and/or translocation of ion channels between intracellular pools and the plasma membrane as the underlying mechanism(s). In 7 out of 15 cases, recordings were held long enough to observe the spontaneous recommencement of spiking, which was accompanied by a rise to baseline of input resistance and membrane time constant, after 7-60 min of quiescence (mean ± s.e.m. = 25.86 ± 7.61 min). The temporary suspension of electrical output is thus part of the normal activity cycle of dFB neurons and not a dead end brought on by the experimental conditions (Pimentel, 2016).

dFB neurons in the ON state expressed two types of potassium current: voltage-dependent A-type (rapidly inactivating) and voltage-independent non-A-type currents. The current-voltage (I-V) relation of iA resembled that of Shaker, the prototypical A-type channel: no current flowed below -50 mV, the approximate voltage threshold of Shaker; above -40 mV, peak currents increased steeply with voltage and inactivated with a time constant of 7.5 ± 2.1 ms (mean ± s.e.m.). Non-A-type currents showed weak outward rectification with a reversal potential of -80 mV, consistent with potassium as the permeant ion, and no inactivation (Pimentel, 2016).

Switching the neurons OFF changed both types of potassium current. iA diminished by one-third, whereas inon-A nearly quadrupled when quantified between resting potential and spike threshold. The weak rectification of inon-A in the ON state vanished in the OFF state, giving way to the linear I-V relationship of an ideal leak conductance. dFB neurons thus upregulate iA in the sleep-promoting ON state. When dopamine switches the cells OFF, voltage-dependent currents are attenuated and leak currents augmented. This seesaw form of regulation should be sensitive to perturbations of the neurons' ion channel inventory: depletion of voltage-gated A-type (KV) channels (which predominate in the ON state) should tip the cells towards the OFF state; conversely, loss of leak channels (which predominate in the OFF state) should favour the ON state. To test these predictions, sleep was examined in flies carrying R23E10-GAL4-driven RNAi transgenes for dFB-restricted interference with individual potassium channel transcripts (Pimentel, 2016).

RNAi-mediated knockdown of two of the five KV channel types of Drosophila (Shaker and Shab) reduced sleep relative to parental controls, while knockdown of the remaining three types had no effect. Biasing the potassium channel repertoire of dFB neurons against A-type conductances thus tilts the neurons' excitable state towards quiescence, causing insomnia, but leaves transient and sustained dopamine responses unaffected. The seemingly counterintuitive conclusion that reducing a potassium current would decrease, not increase, action potential discharge is explained by a requirement for A-type channels in generating repetitive activity of the kind displayed by dFB neurons during sleep. Depleting Shaker from dFB neurons shifted the interspike interval distribution towards longer values, as would be expected if KV channels with slow inactivation kinetics replaced rapidly inactivating Shaker as the principal force opposing the generation of the next spike. These findings identify a potential mechanism for the short-sleeping phenotypes caused by mutations in Shaker, its β subunit Hyperkinetic, or its regulator sleepless (Pimentel, 2016).

Leak conductances are typically formed by two-pore-domain potassium (K2P) channels. dFB-restricted RNAi of one member of the 11-strong family of Drosophila K2P channels, encoded by the CG8713 gene, increased sleep relative to parental controls; interference with the remaining 10 K2P channels had no effect. Recordings from dFB neurons after knockdown of the CG8713 gene product, which this study termed Sandman, revealed undiminished non-A-type currents in the ON state and intact responses to a single pulse of dopamine but a defective OFF switch: during prolonged dopamine applications, inon-A failed to rise, input resistances and membrane time constants remained at their elevated levels, and the neurons continued to fire action potentials (7 out of 7 cells). Blocking vesicle exocytosis in the recorded cell with botulinum neurotoxin C (BoNT/C) similarly disabled the OFF switch. This, combined with the absence of detectable Sandman currents in the ON state, suggests that Sandman is internalized in electrically active cells and recycled to the plasma membrane when dopamine switches the neurons OFF (Pimentel, 2016).

Because dFB neurons lacking Sandman spike persistently even after prolonged dopamine exposure, voltage-gated sodium channels remain functional in the OFF state. The difficulty of driving control cells to action potential threshold in this state must therefore be due to a lengthening of electrotonic distance between sites of current injection and spike generation. This lengthening is an expected consequence of a current leak, which may uncouple the axonal spike generator from somatodendritic synaptic inputs or pacemaker currents when sleep need is low (Pimentel, 2016).

The two kinetically and mechanistically distinct actions of dopamine on dFB neurons-instant, but transient, hyperpolarization and a delayed, but lasting, switch in excitable state-ensure that transitions to vigilance can be both immediate and sustained, providing speedy alarm responses and stable homeostatic control. The key to stability lies in the switching behaviour of dFB neurons, which is driven by dopaminergic input accumulated over time. Unlike bistable neurons, in which two activity regimes coexist for the same set of conductances, dFB neurons switch regimes only when their membrane current densities change. This analysis of how dopamine effects such a change, from activity to silence, has uncovered elements familiar from other modulated systems: simultaneous, antagonistic regulation of multiple conductances; reduction of iA; and modulation of leak currents. Currently little is known about the reverse transition, from silence to activity, except that mutating the Rho-GTPase-activating protein Crossveinless-c locks dFB neurons in the OFF state, resulting in severe insomnia and an inability to correct sleep deficits. Discovering the signals and processes that switch sleep-promoting neurons back ON will hold important clues to the vital function of sleep (Pimentel, 2016).

A computational model of the integration of landmarks and motion in the insect central complex

The insect central complex (CX) has been implicated in a wide range of behaviours. Recent experimental evidence from Drosophila and the cockroach (Blaberus discoidalis) has demonstrated the existence of neural activity corresponding to the animal's orientation within a virtual arena (a neural 'compass'), and this provides an insight into one component of the CX structure. There are two key features of the compass activity: an offset between the angle represented by the compass and the true angular position of visual features in the arena, and the remapping of the 270 degrees visual arena onto an entire circle of neurons in the compass. This study presents a computational model which can reproduce this experimental evidence in detail, and predicts the computational mechanisms that underlie the data. It is predicted that both the offset and remapping of the fly's orientation onto the neural compass can be explained by plasticity in the synaptic weights between segments of the visual field and the neurons representing orientation. Furthermore, it is predicted that this learning is reliant on the existence of neural pathways that detect rotational motion across the whole visual field and uses this rotation signal to drive the rotation of activity in a neural ring attractor. This model also reproduces the 'transitioning' between visual landmarks seen when rotationally symmetric landmarks are presented. This model can provide the basis for further investigation into the role of the central complex, which promises to be a key structure for understanding insect behaviour, as well as suggesting approaches towards creating fully autonomous robotic agents (Cope, 2017).

Angular velocity integration in a fly heading circuit

Many animals maintain an internal representation of their heading as they move through their surroundings. Such a compass representation was recently discovered in a neural population in the Drosophila melanogaster central complex (see Anatomy suggests a potential circuit mechanism to update a compass representation), a brain region implicated in spatial navigation. This study used two-photon calcium imaging and electrophysiology in head-fixed walking flies to identify a different neural population that conjunctively encodes heading and angular velocity, and is excited selectively by turns in either the clockwise or counterclockwise direction. These mirror-symmetric turn responses combine with the neurons' connectivity to the compass neurons to create an elegant mechanism for updating the fly's heading representation when the animal turns in darkness. This mechanism, which employs recurrent loops with an angular shift, bears a resemblance to those proposed in theoretical models for rodent head direction cells. These results provide a striking example of structure matching function for a broadly relevant computation (Turner-Evans, 2017).

A stable internal representation of heading is fundamental to successful navigation. Neurons that maintain such a representation in darkness have been reported across various species. Several computational models have been proposed to explain how a population representation of heading might be updated using angular velocity signals from different neural populations, but identifying connections between neurons that carry and integrate these disparate signals has been challenging in mammals. This study took advantage of the small size, strong topography and well-described anatomy and cell types of the fly central complex to identify a candidate neuron population, P-ENs, which carry angular velocity signals. Cell-type-specific genetic tools were used to perform electrophysiological recordings from single P-EN neurons and two-photon calcium imaging from entire populations of both P-ENs and the previously described 'compass neurons' (E-PGs) in head-fixed walking flies to demonstrate how these neurons together create an elegant circuit mechanism to update a heading representation when the fly turns in darkness. The circuit motif underlying this mechanism shares some characteristics with past conceptual models of head-direction cell function (Turner-Evans, 2017).

The rate model that was implemented in this study was able to capture the essence of the observed network activity, reproducing physiological activity in response to an input that is specific to one side of the protocerebral bridge, but uniform otherwise. This suggests a level of control over moving the activity bump that is quite simple to implement in neural circuitry. In addition, the model is agnostic to the type of input that is needed to rotate the bump. It does, however, require inputs that are activated when the fly turns, with a strength proportional to the strength of the turn, and that such inputs preferentially innervate one hemisphere to create a mirror-symmetry in the system. This description anatomically matches at least one known cell type: PBG1/2-9.b-SPSi.s (Wolff, 2015). The model also requires inhibition to maintain a stationary bump and linear velocity integration. The widely arborizing and glutamatergic PB18.s-GxΔ7Gy.b neurons may provide such large-scale inhibition onto the P-EN neurons (Turner-Evans, 2017).

Some discrepancies remain between the proposed model and the experimental evidence presented in this study. The model assumes only one P-EN neuron per protocerebral bridge glomerulus (Wolff, 2015), which puts a strong constraint on the angular velocity integration properties of the circuit. In particular, although the circuit displays linear velocity integration within the typical range of angular velocities, the activity bump gets 'stuck' at individual P-EN neurons for small turns. That is, when the fly turns slowly, the corresponding small inputs to the circuit do not trigger bump movements. No such bump dynamics were observed in the imaging experiments, indicating that other, unexplored factors may help smooth bump movement in the actual circuit. Noise in the circuit, potential gap junctions and dendro-dendritic connections within and between E-PG and P-EN neurons, as well as the activity of other cell types in the circuit, such as the PBG1-8.s-EBt.b-D/Vgall.b neurons (Wolff, 2015), may all play a role in smoothing bump movement. These factors may also contribute to differences in bump shape and width between the model and experimental data. Further, the model suggests that E-PG activity is directly passed to the P-EN neurons in the protocerebral bridge, possibly with some anatomical offset and modulation through inhibition. Indeed, almost coincident bumps of activity were observed in the bridge for the two cell types. However, while functional connectivity showed a clear connection from the P-EN neurons to the E-PG neurons, the connectivity, electrophysiology, and imaging results suggested that the E-PG to P-EN connection might be more indirect and also recruit inhibition. In the functional connectivity experiments, very strong activation of the E-PG population reliably excited the P-EN neurons, but weaker excitation evoked a variety of responses. Electrophysiological recordings also revealed an unanticipated complexity in the tuning of the P-ENs' membrane potential. Membrane potential tuning curves generally showed a peak at the same heading as the spike rate tuning curves, but also a pronounced trough about 150° distant from that peak. That trough, likely a result of inhibition in the circuit, was not always evident in the spike rate tuning. Finally, in two-color imaging, offsets were observed of up to one glomerulus between the E-PG and P-EN activity on the ipsilateral side of the bridge, and unexpected P-EN activity on the contralateral side, also offset from the E-PG activity. These results were consistent for both color indicator pairings, as well as in experiments involving a second driver line, suggesting that the effects are not merely an artifact of indicator kinetics or co-expression in another population of neurons. It was noted that during slow rotations, when P-EN activity is low and the E-PG bump is weak, these offsets decreased and, depending on the driver line used, also differed between the ipsi- and contralateral side during a turn. These ae taken as indications that the connectivity between the E-PG and P-EN neurons in the protocerebral bridge may be partly indirect. Future studies will address how excitatory and inhibitory connectivity between these populations and others shape the circuit's compass function (Turner-Evans, 2017).

Still uncertain is whether an activity bump can be independently sustained in the P-EN and E-PG populations, or in the left vs. right P-EN subpopulations. The connections from P-ENs to E-PGs may be the substrate that sustains the maintenance of E-PG bump position in the ellipsoid body in the absence of both visual and self-motion cues (Seelig, 2015), as in the current model. The significant reduction seen in E-PG bump amplitude and PVA (population vector average) strength when synaptic transmission from P-ENs was blocked is supportive of such an idea. Whether E-PG input is similarly essential to the maintenance of P-EN bump strength is less clear, but P-EN heading tuning hints at a dependence on E-PG input. On the other hand, appropriate local connections between nearby neurons either in the ellipsoid body or in the protocerebral bridge may allow bumps of activity to be independently sustained in the E-PG neurons. Signs of such internal connections come, for example, from evidence of presynaptic specializations of E-PGs in the ellipsoid body. Bump persistence could also be achieved through long time-scale cellular biophysics. Future experiments and electron microscopy-based circuit reconstruction efforts should provide stronger constraints on the space of possible models, and clarify the functional and behavioral relevance of the actual circuit structure (Turner-Evans, 2017).

For a circuit mechanism in which phase relationships and conjunctive coding are important, calcium imaging may seem an unreliable arbiter of truth. Somatic single cell recordings, on the other hand, can be hard to interpret given the intricate projection patterns of fly neurons and the compartmentalization of information processing that this can produce. However, the results from calcium imaging and electrophysiology experiments in P-EN neurons were found to be in broad agreement. The electrical signature of P-EN responses to angular and forward velocity mirrored seem with calcium imaging in the noduli. The measured width of a single P-EN neuron's receptive field (~60°) was lower than that observed with calcium imaging (~110°), but this may arise from the slow decay kinetics of calcium indicators. One inconsistency between results, however, related to the imaging of neural activity in the protocerebral bridge. Based on imaging in the noduli and electrophysiology, it was expected that turns in one direction would evoke a steady decrease in activity on the other (contralateral) side of the bridge with increasing rotational velocity. Instead, imaging in the bridge showed a mild increase in activity at higher velocities, albeit while preserving the expected asymmetry between the ipsi- and contralateral side. It is hypothesized that this calcium signal might represent synaptic inputs to the P-ENs more than their spiking activity (Turner-Evans, 2017).

Blocking P-EN output using shiTS had two effects on the E-PG bump: Its amplitude was reduced and its position sometimes changed dramatically during small turns (visible as an increase in variability and low R2 for the correlation of changes in heading versus PVA. The bump amplitude decrease in shiTS flies at high temperature can be readily explained by the reduction in synaptic input to the E-PGs -- indeed, in the firing rate model P-EN input is essential to the maintenance of the E-PG bump. Several factors may explain why the E-PG bump did not completely disappear during this manipulation. First, cell-intrinsic properties of the E-PG neurons may contribute to the persistence of activity in those neurons even in the absence of external input. Second, the shiTS block may have been incomplete, meaning that there was sufficient P-EN drive even at high temperatures to keep the E-PG bump alive. Third, it the possibility of gap junctions between P-EN and E-PG neurons, which the experiments would not block, cannot be ruled out. Finally, other neuron types may also provide synaptic input to the E-PG population in the ellipsoid body. Some of these possibilities have been suggested in a recent study that used the anatomy of protocerebral bridge neurons to create a spiking model that generates ring attractor dynamics (Kakaria, 2017; Turner-Evans, 2017 and references therein).

Further, the conceptual and firing rate models would imply that if the P-EN to E-PG connections were entirely removed, E-PG activity would be unable to follow the fly's turns. However, the E-PG activity does still track the fly's turns at high temperature, when the P-EN synaptic output should be blocked. This may, once again, be the result of an incomplete block. It is speculated that one reason that the E-PG bump makes large movements across the ellipsoid body even during small turns is that a reduction in bump amplitude destabilizes the compass representation. Thus, fluctuations in the activity of the E-PGs elsewhere in the ellipsoid body may exert a greater influence on the movements of the bump than under normal conditions, when activity in distant E-PGs is likely to be suppressed. Yet another possibility that could explain the bump's movements is raised by a parallel study, which provides further evidence for P-ENs serving a role in angular integration and describes a second subtype of P-EN neurons that likely also influences the position of the E-PG bump (Green, 2017; Turner-Evans, 2017 and references therein).

The coordinated activity of the E-PG population and its control by the P-EN population when the fly turns are strongly evocative of a compass. The animal could, in principle, use such a neural compass to tether its actions to local landmarks or other sensory cues during navigation, and maintain its bearings in the temporary absence of such cues . Consistent with this idea, the PVA computed with E-PG population activity tracks the fly's heading quite accurately even in darkness. However, it is not yet known how downstream circuits read out E-PG population activity. Thus, although the PVA metric is a useful representation of E-PG compass-like activity, whether downstream circuits perform similar computations to extract the fly's heading is unclear. Further, the PVA was derived by combining the strength and angular position of activity in the E-PG population. Although both these features of E-PG activity likely influence downstream neurons, their specific influence on such neurons will depend on the precise connectivity of the circuit, something that a combination of functional connectivity studies and electron microscopy may reveal in time. Although there is considerable evidence across insects suggesting that CX neurons influence action initiation and turning movements , the connection of E-PG and P-EN neurons to the largely unidentified class of CX neurons that drive behavioral decisions is as yet unclear (Turner-Evans, 2017).

This study has focused on the effects of self-motion cues on bump movement, to which end most of the experiments were conducted with flies walking in the dark. However, E-PG activity is strongly influenced by visual cues, as evidenced by the fact that cue jumps can reset the bump position (Seelig, 2015; Kim, 2017). The angular velocity representation of P-EN neurons, by contrast, seemed unaffected by the presence of closed loop visual feedback. Thus, while a circuit mechanism was suggested for updating heading representation in the dark using self-motion signals, it is anticipated that strong sensory inputs, including those from visual cues, control updating in other circumstances. For example, it was previously observed that the ring neurons retinotopically respond to visual cues (Seelig, 2013). As the putative ring neuron axons arborize in the ellipsoid body along with the E-PG dendrites, it may be possible for them to convey visual information to the E-PG neurons, influencing the movement of the bump of activity. Further, it was suggested above that the E-PG to P-EN connection in the protocerebral bridge may be indirect and recruit sources of inhibition. There exist a few classes of bridge interneurons which may serve as intermediaries in E-PG to P-EN connections. Future studies should help clarify their role in the compass network (Turner-Evans, 2017).

Fly E-PG neurons share several characteristics with mammalian head direction cells. Both head direction cells and E-PG neurons maintain one stable bump of activity and both track the animal's heading in darkness, a feature that is well described by appropriately wired ring attractor models. Rodents that are deprived of proprioceptive and motor efference signals, as in passive transport experiments, show impaired heading representation. To update their heading in darkness, head direction cells in rodent thalamic nuclei and post-subiculum are thought to depend on angular velocity input from the vestibular system, mediated by the dorsal tegmental nucleus. Although 75% of neurons in this region were found to encode angular head velocity, only about a third of those did so in the mirror-symmetric, turn-direction-selective fashion of Drosophila P-EN neurons that were describe in this study (Turner-Evans, 2017).

Individual P-EN neurons were deterministic in their left-right mirror-symmetric rotation tuning, but diverse in the range of rotational velocities that their tuning curves spanned. Indeed, the measured bandwidth of individual P-ENs ranged anywhere between 30° and 270°/s. This diversity may reflect the diversity of tuning of the three to four P-EN neurons that innervate each protocerebral bridge glomerulus (estimated from cell body counts. Such a range of sensitivities and bandwidths would permit a more precise tracking of the flies' turns across a wide range of rotational velocities (Turner-Evans, 2017).

The origins of angular velocity responses in P-ENs are as yet unclear, but these responses show a latency relative to the fly's turning movements that are estimated to be ~150 ms, suggesting that they arise from proprioception rather than motor efference. Anatomically, both the two halves of the protocerebral bridge as well as the two noduli are mirror-symmetric structures innervated by a number of neuron types in a lateralized manner, making them likely candidates for receiving such rotation-tuned input. In the cockroach, neurons encoding angular as well as forward velocity have been recorded in the fan-shaped body, a substructure of the central complex that is evolutionarily conserved in flies. Of note, only one of the forty turn responsive neurons in the latter study showed bidirectional modulation, with excitation for turns in the preferred direction and inhibition for turns the other way, a hallmark of the P-EN neurons. These studies, which relied on extracellular recordings and did not identify cell types, found that changes in spike rate regularly preceded locomotor changes instead of tracking them as was found for the fly P-ENs. If it is assumed that neurons of the type recorded in the cockroach also exist in the fly, it is not yet clear whether the P-EN/E-PG compass network that is described in this article exploit advance information about expected changes in angular velocity (Turner-Evans, 2017).

A striking aspect of the fly compass system is its structural symmetry. Mirror symmetry is a prominent feature of the anatomical layout of the protocerebral bridge. The developmental origins of the anatomical positions of central complex neurons have been the focus of numerous studies. However, although the two sides of the protocerebral bridge and the noduli are tuned to rotations in opposite directions, maintaining symmetry at the large scale, the activity of the E-PGs and P-ENs at the scale of bridge glomeruli breaks this symmetry. During a turn, bumps of activity propagate through the left and right sides of the bridge in parallel, in a manner reminiscent of windshield wipers, rather than obeying mirror symmetry. This pattern of activity, together with the connectivity of protocerebral bridge glomeruli and ellipsoid body sectors ensures that the E-PG bump moves smoothly around the ellipsoid body when the fly turns (Turner-Evans, 2017).

More broadly, topographical organization is a striking feature of many sensory circuits, but structure often follows computational function in neural circuits in the central brain as well. The feedforward pathways to and from the Mauthner cell make clear these neurons role in rapid escape behavior, and the parallel delay loops of the barn owl auditory system and the electric fish point to their comparative roles in localizing prey. The anatomical shift of the P-EN neurons with respect to the E-PG neurons provided an immediate clue to a potential structure/function relationship, that of a mechanism for shifting the bump of E-PG activity to update their internal representation of heading. The fact that topography often matches topology in the small fly brain makes the system ideal for the identification of circuit mechanisms underlying complex computations. Only time -- and perhaps large scale circuit reconstruction efforts -- will tell whether such network motifs are also present, but perhaps better hidden, in the more distributed circuits of much larger brains (Turner-Evans, 2017).

The topographical mapping in Drosophila central complex network and its signal routing

Neural networks regulate brain functions by routing signals. Therefore, investigating the detailed organization of a neural circuit at the cellular levels is a crucial step toward understanding the neural mechanisms of brain functions. To study how a complicated neural circuit is organized, this study analyzed recently published data on the neural circuit of the Drosophila central complex, a brain structure associated with a variety of functions including sensory integration and coordination of locomotion. Except for a small number of 'atypical' neuron types, it was found that the network structure formed by the identified 194 neuron types can be described by only a few simple mathematical rules. Specifically, the topological mapping formed by these neurons can be reconstructed by applying a generation matrix on a small set of initial neurons. By analyzing how information flows propagate with or without the atypical neurons, it was found that while the general pattern of signal propagation in the central complex follows the simple topological mapping formed by the 'typical' neurons, some atypical neurons can substantially re-route the signal pathways, implying specific roles of these neurons in sensory signal integration. The present study provides insights into the organization principle and signal integration in the central complex (Chang, 2017).

A conserved plan for wiring up the fan-shaped body in the grasshopper and Drosophila

The central complex comprises an elaborate system of modular neuropils which mediate spatial orientation and sensory-motor integration. The neuroarchitecture of the largest of these modules, the fan-shaped body, is characterized by its stereotypic set of decussating fiber bundles. These are generated during development by axons from four homologous protocerebral lineages which enter the commissural system and subsequently decussate at stereotypic locations across the brain midline. It is not clear how the decussating bundles relate to individual lineages, or if the projection pattern is conserved across species. This study traced the axonal projections from the homologous central complex lineages into the commissural system of the embryonic and larval brains of both the grasshopper and Drosophila. Projections into the primordial commissures of both species are found to be lineage-specific and allow putatively equivalent fascicles to be identified. Comparison of the projection pattern before and after the commencement of axon decussation in both species reveals that equivalent commissural fascicles are involved in generating the columnar neuroarchitecture of the fan-shaped body. Further, the tract-specific columns in both the grasshopper and Drosophila can be shown to contain axons from identical combinations of central complex lineages, suggesting that this columnar neuroarchitecture is also conserved (Boyan, 2017).

In both the grasshopper S. gregaria and Drosophila, the fan-shaped body with its prominent columnar neuroarchitecture (see Wiring of the central complex subserves information processing) comprises the largest module of the adult central complex. In the grasshopper, this columnar neuroarchitecture develops from an initially orthogonal primary axon scaffold during the second half of embryogenesis and is functional at the time of hatching. The neuroarchitecture is generated when subsets of axons from four lineages (termed W, X, Y, Z) in each protocerebral hemisphere innervate the existing commissural system but then decussate from anterior to more posterior lying fascicles at stereotypic locations across the central brain in a process known as 'fascicle switching'. In Drosophila, decussation of axons from four putatively equivalent lineages to those of the grasshopper also occurs , but during the larval to pupal transition, so that the resulting neuroarchitecture is essentially an adult feature. Species comparisons reveal that fascicle switching is present at some stage of development in the central brain of all arthropods and so may be considered a conserved mode of axogenesis (Boyan, 2017).

A major drawback in understanding of axon decussation in the insect brain has been the lack of a systematic identification of the embryonic commissural fascicles involved. In the grasshopper, for example, although a map of all commissures for the adult brain has been available for some time, the embryonic commissures have to date only been superficially allocated into anterior (ac) and posterior (pc) subsets, in keeping with the nomenclature for the early ventral nerve cord. Further, while the pioneers of the w, x, y, and z tracts from each protocerebral hemisphere have been shown to project into the primary commissural fascicle of the brain just after its formation early in embryogenesis and then to fasciculate with its pioneers, the early axons from the W, X, Y, and Z lineages remain within the commissural fascicles they originally pioneer. Later, in growing axons from these same lineages, however, subsequently decussate but the commissural fascicles involved have remained undescribed. This study reconstructed the axon projections from representative lineages of the central complex into the commissural system of the brain at various developmental stages in both the grasshopper and Drosophila. At the same time, the commissural organization itself was analyzed at these stages using the nomenclature applicable to the grasshopperand Drosophila. This analysis leads to the conclusion that in setting up the columnar neuroarchitecture of the fan-shaped body, comparable choices are being made by subsets of axons from equivalent lineages in both the grasshopper and Drosophila, consistent with a conserved wiring plan for this brain region (Boyan, 2017).

In both Drosophila and the grasshopper, the axon scaffold of the embryonic brain comprises an orthogonal system of axonal projections around the stomodeum. Anterior to the stomodeum, this scaffold in Drosophila had earlier been resolved to the level of grouped anterior or posterior commissures, but not individual fascicles. Recent studies, using specific Gal-4 lines, on the other hand, have documented a large number of single projections from larval/pupal neurons of the protocerebrum to the protocerebral bridge and then to the fan-shaped body, ellipsoid body and noduli, many of a commissural nature. Such commissural elements may now be integrated into a plan equivalent to that developed for the grasshopper (Boyan, 2017).

The conserved nature of fan-shaped body neuroarchitecture in insects such as Drosophila and the grasshopper makes it likely that there is also a high degree of correspondence among their commissural fascicles. The current study now enables the development of the fan-shaped body to be understood at the level of individually identified commissural fascicles, and so provides the basis for interspecific comparisons of central complex development involving Drosophila, Tenebrio and the sightless dipluran Campodea where similar patterns of axon decussation are found. Equally, mutant analyses in Drosophila may allow the pattern of decussation present in the grasshopper to be understood with greater precision. For example, the topographic decussation of axons at stereotypic locations in both species suggests the presence of choice points across the midbrain similar to that reported for the ventral nerve cord. Although the mechanism has yet to be identified in the brain, a dysregulation of cell surface adhesion/recognition molecules in a graded manner across the midbrain represents one possibility. In the peripheral nervous system of Drosophila mutant for the cell surface molecule fasciclin III, for example, axons switch fascicles to an incorrect branch of the segmental nerve and so project to inappropriate body wall muscles, while in the visual system, relative expression levels of adhesion molecules have been found to regulate the wiring of neurite fascicles. Glia have also been shown to direct neuronal axogenesis in the CNS and midline glia are present in the both the grasshopper and Drosophila brain during commissure formation. In the karussell mutant (mutation affecting β-spectrin), for example, dysregulation of midline glia belonging to the pointed group results in commissural axons of the ventral nerve cord decussating between anterior and posterior fascicles, a neuroarchitecture not found in the wild type (Hummel, 1999).

Neural signatures of dynamic stimulus selection in Drosophila

Many animals orient using visual cues, but how a single cue is selected from among many is poorly understood. This study shows that Drosophila ring neurons-central brain neurons implicated in navigation-display visual stimulus selection. Using in vivo two-color two-photon imaging with genetically encoded calcium indicators, this study demonstrates that individual ring neurons inherit simple-cell-like receptive fields from their upstream partners. Stimuli in the contralateral visual field suppressed responses to ipsilateral stimuli in both populations. Suppression strength depended on when and where the contralateral stimulus was presented, an effect stronger in ring neurons than in their upstream inputs. This history-dependent effect on the temporal structure of visual responses, which was well modeled by a simple biphasic filter, may determine how visual references are selected for the fly's internal compass. This approach highlights how two-color calcium imaging can help identify and localize the origins of sensory transformations across synaptically connected neural populations (Sun, 2017).


Boyan, G., Liu, Y., Khalsa, S. K. and Hartenstein, V. (2017). A conserved plan for wiring up the fan-shaped body in the grasshopper and Drosophila. Dev Genes Evol 227(4): 253-269. PubMed ID: 28752327

Chang, P. Y., Su, T. S., Shih, C. T. and Lo, C. C. (2017). The topographical mapping in Drosophila central complex network and its signal routing. Front Neuroinform 11: 26. PubMed ID: 28443014

Cope, A. J., Sabo, C., Vasilaki, E., Barron, A. B. and Marshall, J. A. (2017). A computational model of the integration of landmarks and motion in the insect central complex. PLoS One 12(2): e0172325. PubMed ID: 28241061

Dewar, A. D. M., Wystrach, A., Philippides, A. and Graham, P. (2017). Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?. PLoS Comput Biol 13(10): e1005735. PubMed ID: 29016606

Faumont, S., Lindsay, T. H. and Lockery, S. R. (2012). Neuronal microcircuits for decision making in C. elegans. Curr Opin Neurobiol 22: 580-591. PubMed ID: 22699037

Gao, X. J., Potter, C. J., Gohl, D. M., Silies, M., Katsov, A. Y., Clandinin, T. R. and Luo, L. (2013). Specific kinematics and motor-related neurons for aversive chemotaxis in Drosophila. Curr Biol 23: 1163-1172. PubMed ID: 23770185

Gao, X. J., Riabinina, O., Li, J., Potter, C. J., Clandinin, T. R. and Luo, L. (2015). A transcriptional reporter of intracellular Ca(2+) in Drosophila. Nat Neurosci 18: 917-925. PubMed ID: 25961791

Green, J., Adachi, A., Shah, K. K., Hirokawa, J. D., Magani, P. S. and Maimon, G. (2017). A neural circuit architecture for angular integration in Drosophila. Nature 546(7656): 101-106. PubMed ID: 28538731

Kakaria, K. S. and de Bivort, B. L. (2017). Ring Attractor Dynamics Emerge from a Spiking Model of the Entire Protocerebral Bridge. Front Behav Neurosci 11: 8. PubMed ID: 28261066

Kim, S. S., Rouault, H., Druckmann, S. and Jayaraman, V. (2017). Ring attractor dynamics in the Drosophila central brain. Science 356(6340): 849-853. PubMed ID: 28473639

Kuntz, S., Poeck, B., Sokolowski, M. B. and Strauss, R. (2012). The visual orientation memory of Drosophila requires Foraging (PKG) upstream of Ignorant (RSK2) in ring neurons of the central complex. Learn Mem 19: 337-340. PubMed ID: 22815538

Lin, C. Y., Chuang, C. C., Hua, T. E., Chen, C. C., Dickson, B. J., Greenspan, R. J. and Chiang, A. S. (2013). A comprehensive wiring diagram of the protocerebral bridge for visual information processing in the Drosophila brain. Cell Rep 3: 1739-1753. PubMed ID: 23707064

Liu, S., Liu, Q., Tabuchi, M. and Wu, M. N. (2016). Sleep drive is encoded by neural plastic changes in a dedicated circuit. Cell 165: 1347-1360. PubMed ID: 27212237

Lovick, J. K., Omoto, J. J., Ngo, K. T. and Hartenstein, V. (2017). Development of the anterior visual input pathway to the Drosophila central complex. J Comp Neurol. PubMed ID: 28675433

Ofstad, T. A., Zuker, C. S. and Reiser, M. B. (2011). Visual place learning in Drosophila melanogaster. Nature 474: 204-207. PubMed ID: 21654803

Omoto, J. J., Keles, M. F., Nguyen, B. M., Bolanos, C., Lovick, J. K., Frye, M. A. and Hartenstein, V. (2017). Visual input to the Drosophila central complex by developmentally and functionally distinct neuronal populations. Curr Biol 27(8): 1098-1110. PubMed ID: 28366740

Pan, Y., Zhou, Y., Guo, C., Gong, H., Gong, Z. and Liu, L. (2009). Differential roles of the fan-shaped body and the ellipsoid body in Drosophila visual pattern memory. Learn Mem 16: 289-295. PubMed ID: 19389914

Pimentel, D., Donlea, J.M., Talbot, C.B., Song, S.M., Thurston, A.J. and Miesenbock, G. (2016). Operation of a homeostatic sleep switch. Nature 536(7616):333-7. PubMed ID: 27487216

Seelig, J. D. and Jayaraman, V. (2013). Feature detection and orientation tuning in the Drosophila central complex. Nature 503(7475): 262-266. PubMed ID: 24107996

Seelig, J. D. and Jayaraman, V. (2015). Neural dynamics for landmark orientation and angular path integration. Nature 521(7551): 186-191. PubMed ID: 25971509

Sun, Y., Nern, A., Franconville, R., Dana, H., Schreiter, E. R., Looger, L. L., Svoboda, K., Kim, D. S., Hermundstad, A. M. and Jayaraman, V. (2017). Neural signatures of dynamic stimulus selection in Drosophila. Nat Neurosci [Epub ahead of print]. PubMed ID: 28604683

Turner-Evans, D., Wegener, S., Rouault, H., Franconville, R., Wolff, T., Seelig, J. D., Druckmann, S. and Jayaraman, V. (2017). Angular velocity integration in a fly heading circuit. Elife 6. PubMed ID: 28530551

Walsh, K. T. and Doe, C. Q. (2017). Drosophila embryonic type II neuroblasts: origin, temporal patterning, and contribution to the adult central complex. Development 144: 4552-4562. PubMed ID: 29158446

Weir, P.T. and Dickinson, M.H. (2015). Functional divisions for visual processing in the central brain of flying Drosophila. Proc Natl Acad Sci U S A 112(40):E5523-32. PubMed ID: 26324910

Wolff, T., Iyer, N. A. and Rubin, G. M. (2014). Neuroarchitecture and neuroanatomy of the Drosophila central complex: A GAL4-based dissection of protocerebral bridge neurons and circuits. J Comp Neurol [Epub ahead of print]. PubMed ID: 25380328

Zhang, J., Tanenhaus, A. K., Davis, J. C., Hanlon, B. M. and Yin, J. C. (2014). Spatio-temporal in vivo recording of dCREB2 dynamics in Drosophila long-term memory processing. Neurobiol Learn Mem 118C: 80-88. PubMed ID: 25460038

Zhang, Y., Liu, G., Yan, J., Zhang, Y., Li, B. and Cai, D. (2015). Metabolic learning and memory formation by the brain influence systemic metabolic homeostasis. Nat Commun 6: 6704. PubMed ID: 25848677

Zhang, Z., Li, X., Guo, J., Li, Y. and Guo, A. (2013). Two clusters of GABAergic ellipsoid body neurons modulate olfactory labile memory in Drosophila. J Neurosci 33: 5175-5181. PubMed ID: 23516283

genes expressed in brain morphogenesis

Genes involved in organ development

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