A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection

Inhibitory control explains locomotor statistics in walking Drosophila

In order to forage for food, many animals regulate not only specific limb movements but the statistics of locomotor behavior, switching between long-range dispersal and local search depending on resource availability. How premotor circuits regulate locomotor statistics is not clear. Here, we analyze and model locomotor statistics and their modulation by attractive food odor in walking Drosophila. Food odor evokes three motor regimes in flies: baseline walking, upwind running during odor, and search behavior following odor loss. During search, we find that flies adopt higher angular velocities and slower ground speeds and turn for longer periods in the same direction. We further find that flies adopt periods of different mean ground speed and that these state changes influence the length of odor-evoked runs. We next developed a simple model of neural locomotor control that suggests that contralateral inhibition plays a key role in regulating the statistical features of locomotion. As the fly connectome predicts decussating inhibitory neurons in the premotor lateral accessory lobe (LAL), we gained genetic access to a subset of these neurons and tested their effects on behavior. We identified one population whose activation induces all three signature of local search and that regulates angular velocity at odor offset. We identified a second population, including a single LAL neuron pair, that bidirectionally regulates ground speed. Together, our work develops a biologically plausible computational architecture that captures the statistical features of fly locomotion across behavioral states and identifies neural substrates of these computations.

Cell type-specific driver lines targeting the Drosophila central complex and their use to investigate neuropeptide expression and sleep regulation

The central complex (CX) plays a key role in many higher-order functions of the insect brain including navigation and activity regulation. Genetic tools for manipulating individual cell types, and knowledge of what neurotransmitters and neuromodulators they express, will be required to gain mechanistic understanding of how these functions are implemented. We generated and characterized split-GAL4 driver lines that express in individual or small subsets of about half of CX cell types. We surveyed neuropeptide and neuropeptide receptor expression in the central brain using fluorescent in situ hybridization. About half of the neuropeptides we examined were expressed in only a few cells, while the rest were expressed in dozens to hundreds of cells. Neuropeptide receptors were expressed more broadly and at lower levels. Using our GAL4 drivers to mark individual cell types, we found that 51 of the 85 CX cell types we examined expressed at least one neuropeptide and 21 expressed multiple neuropeptides. Surprisingly, all co-expressed a small molecule neurotransmitter. Finally, we used our driver lines to identify CX cell types whose activation affects sleep, and identified other central brain cell types that link the circadian clock to the CX. The well-characterized genetic tools and information on neuropeptide and neurotransmitter expression we provide should enhance studies of the CX.

The fruit fly, Drosophila melanogaster, as a microrobotics platform

Engineering small autonomous agents capable of operating in the microscale environment remains a key challenge, with current systems still evolving. Our study explores the fruit fly, Drosophila melanogaster, a classic model system in biology and a species adept at microscale interaction, as a biological platform for microrobotics. Initially, we focus on remotely directing the walking paths of fruit flies in an experimental arena. We accomplish this through two distinct approaches: harnessing the fruit flies' optomotor response and optogenetic modulation of its olfactory system. These techniques facilitate reliable and repeated guidance of flies between arbitrary spatial locations. We guide flies along predetermined trajectories, enabling them to scribe patterns resembling textual characters through their locomotion. We enhance olfactory-guided navigation through additional optogenetic activation of attraction-inducing mushroom body output neurons. We extend this control to collective behaviors in shared spaces and navigation through constrained maze-like environments. We further use our guidance technique to enable flies to carry a load across designated points in space, establishing the upper bound on their weight-carrying capabilities. Additionally, we demonstrate that visual guidance can facilitate novel interactions between flies and objects, showing that flies can consistently relocate a small spherical object over significant distances. Last, we demonstrate multiagent formation control, with flies alternating between distinct spatial patterns. Beyond expanding tools available for microrobotics, these behavioral contexts can provide insights into the neurological basis of behavior in fruit flies.

Cell-specific tools for understanding behavior

Novel tools that allow neuron-specific investigations of the structure controlling sleep regulation in fruit flies reveal the extent of neuronal heterogeneity.

Developmental Genetic and Molecular Analysis of Drosophila Central Complex Lineages

Complex behaviors are mediated by a diverse class of neurons and glia produced during development. Both neural stem cell-intrinsic and -extrinsic temporal cues regulate the appropriate number, molecular identity, and circuit assembly of neurons. The Drosophila central complex (CX) is a higher-order brain structure regulating various behaviors, including sensory-motor integration, celestial navigation, and sleep. Most neurons and glia in the adult CX are formed during larval development by 16 Type II neural stem cells (NSCs). Unlike Type I NSCs, which directly give rise to the ganglion mother cells (GMCs), Type II NSCs give rise to multiple intermediate neural progenitors (INPs), and each INP in turn generates multiple GMCs, hence fostering the generation of longer and more diverse lineages. This makes Type II NSCs a suitable model to unravel the molecular mechanisms regulating neural diversity in more complex lineages. In this review, we elaborate on the classification and identification of NSCs based on the types of division adopted and the molecular markers expressed in each type. In the end, we discuss genetic methods for lineage analysis and birthdating. We also explain the temporal expression of stem cell factors and genetic techniques to study how stem cell factors may regulate neural fate specification.

What Drosophila can tell us about state-dependent peptidergic signaling in insects

Plasticity in animal behavior and physiology is largely due to modulatory and regulatory signaling with neuropeptides and peptide hormones (collectively abbreviated NPHs). The NPHs constitute a very large and versatile group of signaling substances that partake at different regulatory levels in most daily activities of an organism. This review summarizes key principles in NPH actions in the brain and in interorgan signaling, with focus on Drosophila. NPHs are produced by neurons, neurosecretory cells (NSCs) and other endocrine cells in NPH-specific and stereotypic patterns. Most of the NPHs have multiple (pleiotropic) functions and target several different neuronal circuits and/or peripheral tissues. Such divergent NPH signaling ensures orchestration of behavior and physiology in state-dependent manners. Conversely, many neurons, circuits, NSCs, or other cells, are targeted by multiple NPHs. This convergent signaling commonly conveys various signals reporting changes in the external and internal environment to central neurons/circuits. As an example of wider functional convergence, 26 different Drosophila NPHs act at many different levels to regulate food search and feeding. Convergence is also seen in hormonal regulation of peripheral functions. For instance, multiple NPHs target renal tubules to ensure osmotic homeostasis. Interestingly, several of the same osmoregulatory NPHs also regulate feeding, metabolism and stress. However, for some NPHs the cellular distribution and functions suggests multiple unrelated functions that are restricted to specific circuits. Thus, NPH signaling follows distinct patterns for each specific NPH, but taken together they form overlapping networks that modulate behavior and physiology.

Spatiotemporal computations in the insect celestial compass

Obtaining a geocentric directional reference from a celestial compass requires compensation for the sun's movement during the day (relative to the observer), which depends on the earth's rotation, time of year and the observer's latitude. We examine how insects could solve this problem, assuming they have clock neurons that represent time as a sinusoidal oscillation, and taking into account the neuroanatomy of their celestial compass pathway. We show how this circuit could exploit trigonometric identities to perform the required spatiotemporal calculations. Our basic model assumes a constant change in sun azimuth (the 'hour angle'), which is recentred on solar noon for changing day lengths. In a more complete model, the time of year is represented by an oscillation with an annual period, and the latitude is estimated from the inclination of the geomagnetic field. Evaluating these models in simulated migration and foraging behaviours shows the hour angle may be sufficient.

FlyVISTA, an integrated machine learning platform for deep phenotyping of sleep in Drosophila

There is great interest in using genetically tractable organisms such as Drosophila to gain insights into the regulation and function of sleep. However, sleep phenotyping in Drosophila has largely relied on simple measures of locomotor inactivity. Here, we present FlyVISTA, a machine learning platform to perform deep phenotyping of sleep in flies. This platform comprises a high-resolution closed-loop video imaging system, coupled with a deep learning network to annotate 35 body parts, and a computational pipeline to extract behaviors from high-dimensional data. FlyVISTA reveals the distinct spatiotemporal dynamics of sleep and wake-associated microbehaviors at baseline, following administration of the sleep-inducing drug gaboxadol, and with dorsal fan-shaped body drivers. We identify a microbehavior ("haltere switch") exclusively seen during quiescence that indicates a deeper sleep stage. These results enable the rigorous analysis of sleep in Drosophila and set the stage for computational analyses of microbehaviors in quiescent animals.

Vector-based navigation in desert ants: the significance of path-integration vectors

In the longstanding discussion of whether insects, especially central place foragers such as bees and ants, use metric representations of their landmark surroundings (so-called "cognitive maps"), the ability to find novel shortcuts between familiar locations has been considered one of the most decisive proofs for the use of such maps. Here we show by channel-based field experiments that desert ants Cataglyphis can travel such shortcuts between locations (defined by memorized goal vectors) just on the basis of path integration. When trained to visit two spatially separated feeders A and B they later travel the hitherto novel route A→B. This behavior may originate from the interaction of goal vectors retrieved from long-term memory and the current vector computed by the continuously running path integrator. Based on former experiments, we further argue that path integration is a necessary requirement also for acquiring landmark information (in form of learned goal-directed views). This emphasizes the paramount importance of path integration in these central place foragers. Finally we hypothesize that the ant's overall system of navigation consists in the optimal combination of path-integration vectors and view-based vectors, and thus handles and uses vectorial information without the need of constructing a "vector map", in which vectors are linked to known places in the environment others than to the origin of all journeys, the nest.

The dorsal fan-shaped body is a neurochemically heterogeneous sleep-regulating center in Drosophila

Sleep is a behavior that is conserved throughout the animal kingdom. Yet, despite extensive studies in humans and animal models, the exact function or functions of sleep remain(s) unknown. A complicating factor in trying to elucidate the function of sleep is the complexity and multiplicity of neuronal circuits that are involved in sleep regulation. It is conceivable that distinct sleep-regulating circuits are only involved in specific aspects of sleep and may underlie different sleep functions. Thus, it would be beneficial to assess the contribution of individual circuits in sleep's putative functions. The intricacy of the mammalian brain makes this task extremely difficult. However, the fruit fly Drosophila melanogaster, with its simpler brain organization, available connectomics, and unparalleled genetics, offers the opportunity to interrogate individual sleep-regulating centers. In Drosophila, neurons projecting to the dorsal fan-shaped body (dFB) have been proposed to be key regulators of sleep, particularly sleep homeostasis. We recently demonstrated that the most widely used genetic tool to manipulate dFB neurons, the 23E10-GAL4 driver, expresses in 2 sleep-regulating neurons (VNC-SP neurons) located in the ventral nerve cord (VNC), the fly analog of the vertebrate spinal cord. Since most data supporting a role for the dFB in sleep regulation have been obtained using 23E10-GAL4, it is unclear whether the sleep phenotypes reported in these studies are caused by dFB neurons or VNC-SP cells. A recent publication replicated our finding that 23E10-GAL4 contains sleep-promoting neurons in the VNC. However, it also proposed that the dFB is not involved in sleep regulation at all, but this suggestion was made using genetic tools that are not dFB-specific and a very mild sleep deprivation protocol. In this study, using a newly created dFB-specific genetic driver line, we demonstrate that optogenetic activation of the majority of 23E10-GAL4 dFB neurons promotes sleep and that these neurons are involved in sleep homeostasis. We also show that dFB neurons require stronger stimulation than VNC-SP cells to promote sleep. In addition, we demonstrate that dFB-induced sleep can consolidate short-term memory (STM) into long-term memory (LTM), suggesting that the benefit of sleep on memory is not circuit-specific. Finally, we show that dFB neurons are neurochemically heterogeneous and can be divided in 3 populations. Most dFB neurons express both glutamate and acetylcholine, while a minority of cells expresses only one of these 2 neurotransmitters. Importantly, dFB neurons do not express GABA, as previously suggested. Using neurotransmitter-specific dFB tools, our data also points at cholinergic dFB neurons as particularly potent at regulating sleep and sleep homeostasis.

Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems

Uncertainty is a fundamental aspect of the natural environment, requiring the brain to infer and integrate noisy signals to guide behavior effectively. Sampling-based inference has been proposed as a mechanism for dealing with uncertainty, particularly in early sensory processing. However, it is unclear how to reconcile sampling-based methods with operational principles of higher-order brain areas, such as attractor dynamics of persistent neural representations. In this study, we present a spiking neural network model for the head-direction (HD) system that combines sampling-based inference with attractor dynamics. To achieve this, we derive the required spiking neural network dynamics and interactions to perform sampling from a large family of probability distributions-including variables encoded with Poisson noise. We then propose a method that allows the network to update its estimate of the current head direction by integrating angular velocity samples-derived from noisy inputs-with a pull towards a circular manifold, thereby maintaining consistent attractor dynamics. This model makes specific, testable predictions about the HD system that can be examined in future neurophysiological experiments: it predicts correlated subthreshold voltage fluctuations; distinctive short- and long-term firing correlations among neurons; and characteristic statistics of the movement of the neural activity "bump" representing the head direction. Overall, our approach extends previous theories on probabilistic sampling with spiking neurons, offers a novel perspective on the computations responsible for orientation and navigation, and supports the hypothesis that sampling-based methods can be combined with attractor dynamics to provide a viable framework for studying neural dynamics across the brain.

A Critical Role for the Fascin Family of Actin Bundling Proteins in Axon Development, Brain Wiring and Function

Actin-based cell motility drives many neurodevelopmental events including guided axonal growth. Fascin is a major family of F-actin bundling proteins, but its role in axon development and brain wiring is unknown. Here, we report that fascin is required for axon development, brain wiring and function. We show that fascin is enriched in the motile filopodia of axonal growth cones and its inhibition impairs axonal extension and branching of hippocampal neurons in culture. We next provide evidence that fascin is essential for axon development and brain wiring using Drosophila melanogaster as an in vivo model. Drosophila express a single ortholog of mammalian fascin called Singed (SN), which is highly expressed in the mushroom body (MB) of the central nervous system. We observe that loss of SN results in drastic MB disruption, highlighted by α- and β-lobe defects that are consistent with altered axonal guidance. SN-null flies also exhibit defective sensorimotor behaviors as assessed by the negative geotaxis assay. MB-specific expression of SN in SN-null flies rescues MB structure and sensorimotor deficits, indicating that SN functions autonomously in MB neurons. Together, our data from primary neuronal culture and in vivo models highlight a critical role for fascin in brain development and function.

Significance statement

Fascin is a major family of actin-binding proteins (ABPs) that crosslink and bundle actin filaments to underline membrane protrusions essential for cell motility and neuronal development.Inhibition of fascin in cultured cells causes it to dissipate from F-actin-rich membrane protrusions and become cytosolic, reduces filopodia formation, and decreases axon outgrowth and branching.In fruit flies, loss of the fascin orthologue, Singed, causes defects in brain development, specifically in mushroom body (MB) lobes, and leads to behavioral impairments which can be rescued upon MB-specific expression of Singed.

A vector-based strategy for olfactory navigation in Drosophila

Odors serve as essential cues for navigation. Although tracking an odor plume has been modeled as a reflexive process, it remains unclear whether animals can use memories of their past odor encounters to infer the spatial structure of their chemical environment or their location within it. Here we developed a virtual-reality olfactory paradigm that allows head-fixed Drosophila to navigate structured chemical landscapes, offering insight into how memory mechanisms shape their navigational strategies. We found that flies track an appetitive odor corridor by following its boundary, alternating between rapid counterturns to exit the plume and directed returns to its edge. Using a combination of behavioral modeling, functional calcium imaging, and neural perturbations, we demonstrate that this 'edge-tracking' strategy relies on vector-based computations within the Drosophila central complex in which flies store and dynamically update memories of the direction to return them to the plume's boundary. Consistent with this, we find that FC2 neurons within the fan-shaped body, which encode a fly's navigational goal, signal the direction back to the odor boundary when flies are outside the plume. Together, our studies suggest that flies leverage the plume's boundary as a dynamic landmark to guide their navigation, analogous to the memory-based strategies other insects use for long-distance migration or homing to their nests. Plume tracking thus uses components of a conserved navigational toolkit, enabling flies to use memory mechanisms to navigate through a complex shifting chemical landscape.

The neuroethology of ant navigation

Unlike any other group of animals, all ant species are social: individual ants share the food they gather with their nestmates and as a consequence they must repeatedly leave their nest to find food and then return home with it. These back-and-forth foraging trips have been studied for about a century and much of our growing understanding of the strategies underlying animal navigation has come from these studies. One important strategy that ants use to keep track of where they are on a foraging trip is 'path integration', in which they continuously update a 'home vector' that gives their estimated distance and direction from the nest. As path integration accumulates errors, it cannot be relied on to bring ants precisely home: such precision is accomplished by using views of the nest acquired before they start foraging. Further learning is scaffolded by home vectors or remembered food vectors, which guide a route and help in learning useful views experienced on the way. Many species rely on olfaction as well as vision for route guidance and the full details of their foraging paths have revealed how ants use a mix of innate and learnt multisensory cues. Wood ants, a species on which we focus in this review, take an oscillating path along a pheromone trail to sample odours, but acquire visual information only at the peaks and troughs of the oscillations. To provide a working model of the neural basis of the multimodal navigational strategies of ants, we outline the anatomy and functioning of major central brain areas and neural circuits - the central complex, mushroom bodies and lateral accessory lobes - that are involved in the coordination of navigational behaviour and the learning of visual and olfactory patterns. Because ant brains have not yet been well-studied, we rely on the work that has been done with other species - notably, Drosophila, silkworm moths and bees - to derive plausible neural circuitry that can deliver the ants' navigational strategies.

Theory of morphodynamic information processing: Linking sensing to behaviour

The traditional understanding of brain function has predominantly focused on chemical and electrical processes. However, new research in fruit fly (Drosophila) binocular vision reveals ultrafast photomechanical photoreceptor movements significantly enhance information processing, thereby impacting a fly's perception of its environment and behaviour. The coding advantages resulting from these mechanical processes suggest that similar physical motion-based coding strategies may affect neural communication ubiquitously. The theory of neural morphodynamics proposes that rapid biomechanical movements and microstructural changes at the level of neurons and synapses enhance the speed and efficiency of sensory information processing, intrinsic thoughts, and actions by regulating neural information in a phasic manner. We propose that morphodynamic information processing evolved to drive predictive coding, synchronising cognitive processes across neural networks to match the behavioural demands at hand effectively.

Driver lines for studying associative learning in Drosophila

The mushroom body (MB) is the center for associative learning in insects. In Drosophila, intersectional split-GAL4 drivers and electron microscopy (EM) connectomes have laid the foundation for precise interrogation of the MB neural circuits. However, investigation of many cell types upstream and downstream of the MB has been hindered due to lack of specific driver lines. Here we describe a new collection of over 800 split-GAL4 and split-LexA drivers that cover approximately 300 cell types, including sugar sensory neurons, putative nociceptive ascending neurons, olfactory and thermo-/hygro-sensory projection neurons, interneurons connected with the MB-extrinsic neurons, and various other cell types. We characterized activation phenotypes for a subset of these lines and identified a sugar sensory neuron line most suitable for reward substitution. Leveraging the thousands of confocal microscopy images associated with the collection, we analyzed neuronal morphological stereotypy and discovered that one set of mushroom body output neurons, MBON08/MBON09, exhibits striking individuality and asymmetry across animals. In conjunction with the EM connectome maps, the driver lines reported here offer a powerful resource for functional dissection of neural circuits for associative learning in adult Drosophila.

A split-GAL4 driver line resource for Drosophila neuron types

Techniques that enable precise manipulations of subsets of neurons in the fly central nervous system (CNS) have greatly facilitated our understanding of the neural basis of behavior. Split-GAL4 driver lines allow specific targeting of cell types in Drosophila melanogaster and other species. We describe here a collection of 3060 lines targeting a range of cell types in the adult Drosophila CNS and 1373 lines characterized in third-instar larvae. These tools enable functional, transcriptomic, and proteomic studies based on precise anatomical targeting. NeuronBridge and other search tools relate light microscopy images of these split-GAL4 lines to connectomes reconstructed from electron microscopy images. The collections are the result of screening over 77,000 split hemidriver combinations. Previously published and new lines are included, all validated for driver expression and curated for optimal cell-type specificity across diverse cell types. In addition to images and fly stocks for these well-characterized lines, we make available 300,000 new 3D images of other split-GAL4 lines.

Cell type-specific driver lines targeting the Drosophila central complex and their use to investigate neuropeptide expression and sleep regulation

The central complex (CX) plays a key role in many higher-order functions of the insect brain including navigation and activity regulation. Genetic tools for manipulating individual cell types, and knowledge of what neurotransmitters and neuromodulators they express, will be required to gain mechanistic understanding of how these functions are implemented. We generated and characterized split-GAL4 driver lines that express in individual or small subsets of about half of CX cell types. We surveyed neuropeptide and neuropeptide receptor expression in the central brain using fluorescent in situ hybridization. About half of the neuropeptides we examined were expressed in only a few cells, while the rest were expressed in dozens to hundreds of cells. Neuropeptide receptors were expressed more broadly and at lower levels. Using our GAL4 drivers to mark individual cell types, we found that 51 of the 85 CX cell types we examined expressed at least one neuropeptide and 21 expressed multiple neuropeptides. Surprisingly, all co-expressed a small neurotransmitter. Finally, we used our driver lines to identify CX cell types whose activation affects sleep, and identified other central brain cell types that link the circadian clock to the CX. The well-characterized genetic tools and information on neuropeptide and neurotransmitter expression we provide should enhance studies of the CX.

Configuration of electrical synapses filters sensory information to drive behavioral choices

Synaptic configurations underpin how the nervous system processes sensory information to produce a behavioral response. This is best understood for chemical synapses, and we know far less about how electrical synaptic configurations modulate sensory information processing and context-specific behaviors. We discovered that innexin 1 (INX-1), a gap junction protein that forms electrical synapses, is required to deploy context-specific behavioral strategies underlying thermotaxis behavior in C. elegans. Within this well-defined circuit, INX-1 couples two bilaterally symmetric interneurons to integrate sensory information during migratory behavior across temperature gradients. In inx-1 mutants, uncoupled interneurons display increased excitability and responses to subthreshold sensory stimuli due to increased membrane resistance and reduced membrane capacitance, resulting in abnormal responses that extend run durations and trap the animals in context-irrelevant tracking of isotherms. Thus, a conserved configuration of electrical synapses enables differential processing of sensory information to deploy context-specific behavioral strategies.

Degenerate neuronal and circuit mechanisms important for generating rhythmic motor patterns

In 1996, we published a review article (Marder E, Calabrese RL. Physiol Rev 76: 687-717, 1996) describing the state of knowledge about the structure and function of the central pattern-generating circuits important for producing rhythmic behaviors. Although many of the core questions persist, much has changed since 1996. Here, we focus on newer studies that reveal ambiguities that complicate understanding circuit dynamics, despite the enormous technical advances of the recent past. In particular, we highlight recent studies of animal-to-animal variability and our understanding that circuit rhythmicity may be supported by multiple state-dependent mechanisms within the same animal and that robustness and resilience in the face of perturbation may depend critically on the presence of modulators and degenerate circuit mechanisms. Additionally, we highlight the use of computational models to ask whether there are generalizable principles about circuit motifs that can be found across rhythmic motor systems in different animal species.

Castor is a temporal transcription factor that specifies early born central complex neuron identity

The generation of neuronal diversity is important for brain function, but how diversity is generated is incompletely understood. We used the development of the Drosophila central complex (CX) to address this question. The CX develops from eight bilateral Type 2 neuroblasts (T2NBs), which generate hundreds of different neuronal types. T2NBs express broad opposing temporal gradients of RNA-binding proteins. It remains unknown whether these protein gradients are sufficient to directly generate all known neuronal diversity, or whether there are temporal transcription factors (TTFs) with narrow expression windows that each specify a small subset of CX neuron identities. Multiple candidate TTFs have been identified, but their function remains uncharacterized. Here, we show that: (1) the adult E-PG neurons are born from early larval T2NBs; (2) the candidate TTF Castor is expressed transiently in early larval T2NBs when E-PG and P-EN neurons are born; and (3) Castor is required to specify early born E-PG and P-EN neuron identities. We conclude that Castor is a TTF in larval T2NB lineages that specifies multiple, early born CX neuron identities.

Neuronal circuit mechanisms of competitive interaction between action-based and coincidence learning

How information is integrated across different forms of learning is crucial to understanding higher cognitive functions. Animals form classic or operant associations between cues and their outcomes. It is believed that a prerequisite for operant conditioning is the formation of a classical association. Thus, both memories coexist and are additive. However, the two memories can result in opposing behavioral responses, which can be disadvantageous. We show that Drosophila classical and operant olfactory conditioning rely on distinct neuronal pathways leading to different behavioral responses. Plasticity in both pathways cannot be formed simultaneously. If plasticity occurs at both pathways, interference between them occurs and learning is disrupted. Activity of the navigation center is required to prevent plasticity in the classical pathway and enable it in the operant pathway. These findings fundamentally challenge hierarchical views of operant and classical learning and show that active processes prevent coexistence of the two memories.

Synaptic connectome of the Drosophila circadian clock

The circadian clock and its output pathways play a pivotal role in optimizing daily processes. To obtain insights into how diverse rhythmic physiology and behaviors are orchestrated, we have generated a comprehensive connectivity map of an animal circadian clock using the Drosophila FlyWire brain connectome. Intriguingly, we identified additional dorsal clock neurons, thus showing that the Drosophila circadian network contains ~240 instead of 150 neurons. We revealed extensive contralateral synaptic connectivity within the network and discovered novel indirect light input pathways to the clock neurons. We also elucidated pathways via which the clock modulates descending neurons that are known to regulate feeding and reproductive behaviors. Interestingly, we observed sparse monosynaptic connectivity between clock neurons and downstream higher-order brain centers and neurosecretory cells known to regulate behavior and physiology. Therefore, we integrated single-cell transcriptomics and receptor mapping to decipher putative paracrine peptidergic signaling by clock neurons. Our analyses identified additional novel neuropeptides expressed in clock neurons and suggest that peptidergic signaling significantly enriches interconnectivity within the clock network.

Building egocentric models of local space from retinal input

Determining the location of objects relative to ourselves is essential for interacting with the world. Neural activity in the retina is used to form a vision-independent model of the local spatial environment relative to the body. For example, when an animal navigates through a forest, it rapidly shifts its gaze to identify the position of important objects, such as a tree obstructing its path. This seemingly trivial behavior belies a sophisticated neural computation. Visual information entering the brain in a retinocentric reference frame must be transformed into an egocentric reference frame to guide motor planning and action. This, in turn, allows the animal to extract the location of the tree and plan a path around it. In this review, we explore the anatomical, physiological, and computational implementation of retinocentric-to-egocentric reference frame transformations - a research area undergoing rapid progress stimulated by an ever-expanding molecular, physiological, and computational toolbox for probing neural circuits. We begin by summarizing evidence for retinocentric and egocentric reference frames in the brains of diverse organisms, from insects to primates. Next, we cover how distance estimation contributes to creating a three-dimensional representation of local space. We then review proposed implementations of reference frame transformations across different biological and artificial neural networks. Finally, we discuss how an internal egocentric model of the environment is maintained independently of the sensory inputs from which it is derived. By comparing findings across a variety of nervous systems and behaviors, we aim to inspire new avenues for investigating the neural basis of reference frame transformation, a canonical computation critical for modeling the external environment and guiding goal-directed behavior.

Neuroarchitecture of the Central Complex in the Madeira Cockroach Rhyparobia maderae: Tangential Neurons

Navigating in diverse environments to find food, shelter, or mating partners is an important ability for nearly all animals. Insects have evolved diverse navigational strategies to survive in challenging and unknown environments. In the insect brain, the central complex (CX) plays an important role in spatial orientation and directed locomotion. It consists of the protocerebral bridge (PB), the central body with upper (CBU) and lower division (CBL), and the paired noduli (NO). As shown in various insect species, the CX integrates multisensory cues, including sky compass signals, wind direction, and ego-motion to provide goal-directed vector output used for steering locomotion and flight. While most of these data originate from studies on day-active insects, less is known about night-active species such as cockroaches. Following our analysis of columnar and pontine neurons, the present study complements our investigation of the cellular architecture of the CX of the Madeira cockroach by analyzing tangential neurons. Based on single-cell tracer injections, we provide further details on the internal organization of the CX and distinguished 27 types of tangential neuron, including three types of neuron innervating the PB, six types of the CBL, and 18 types of the CBU. The anterior lip, a brain area unknown in flies and highly reduced in bees, and the crepine are strongly connected to the cockroach CBU in contrast to other insect species. One tangential neuron of the CBU revealed a direct connection between the mushroom bodies and the CBU.

A computational model for angular velocity integration in a locust heading circuit

Accurate navigation often requires the maintenance of a robust internal estimate of heading relative to external surroundings. We present a model for angular velocity integration in a desert locust heading circuit, applying concepts from early theoretical work on heading circuits in mammals to a novel biological context in insects. In contrast to similar models proposed for the fruit fly, this circuit model uses a single 360° heading direction representation and is updated by neuromodulatory angular velocity inputs. Our computational model was implemented using steady-state firing rate neurons with dynamical synapses. The circuit connectivity was constrained by biological data, and remaining degrees of freedom were optimised with a machine learning approach to yield physiologically plausible neuron activities. We demonstrate that the integration of heading and angular velocity in this circuit is robust to noise. The heading signal can be effectively used as input to an existing insect goal-directed steering circuit, adapted for outbound locomotion in a steady direction that resembles locust migration. Our study supports the possibility that similar computations for orientation may be implemented differently in the neural hardware of the fruit fly and the locust.

Structural changes shaping the Drosophila ellipsoid body ER-neurons during development and aging

The ellipsoid body (EB) of the insect brain performs pivotal functions in controlling navigation. Input and output of the EB is provided by multiple classes of R-neurons (now referred to as ER-neurons) and columnar neurons which interact with each other in a stereotypical and spatially highly ordered manner. The developmental mechanisms that control the connectivity and topography of EB neurons are largely unknown. One indispensable prerequisite to unravel these mechanisms is to document in detail the sequence of events that shape EB neurons during their development. In this study, we analyzed the development of the Drosophila EB. In addition to globally following the ER-neuron and columnar neuron (sub)classes in the spatial context of their changing environment we performed a single cell analysis using the multi-color flip out (MCFO) system to analyze the developmental trajectory of ER-neurons at different pupal stages, young adults (4d) and aged adults (∼60d). We show that the EB develops as a merger of two distinct elements, a posterior and anterior EB primordium (prEBp and prEBa, respectively. ER-neurons belonging to different subclasses form growth cones and filopodia that associate with the prEBp and prEBa in a pattern that, from early pupal stages onward, foreshadows their mature structure. Filopodia of all ER-subclasses are initially much longer than the dendritic and terminal axonal branches they give rise to, and are pruned back during late pupal stages. Interestingly, extraneous branches, particularly significant in the dendritic domain, are a hallmark of ER-neuron structure in aged brains. Aging is also associated with a decline in synaptic connectivity from columnar neurons, as well as upregulation of presynaptic protein (Brp) in ER-neurons. Our findings advance the EB (and ER-neurons) as a favorable system to visualize and quantify the development and age-related decline of a complex neuronal circuitry.

Four SpsP neurons are an integrating sleep regulation hub in Drosophila

Sleep is essential and highly conserved, yet its regulatory mechanisms remain largely unknown. To identify sleep drive neurons, we imaged Drosophila brains with calcium-modulated photoactivatable ratiometric integrator (CaMPARI). The results indicate that the activity of the protocerebral bridge (PB) correlates with sleep drive. We further identified a key three-layer PB circuit, EPG-SpsP-PEcG, in which the four SpsP neurons in the PB respond to ellipsoid body (EB) signals from EPG neurons and send signals back to the EB through PEcG neurons. This circuit is strengthened by sleep deprivation, indicating a plasticity response to sleep drive. SpsP neurons also receive inputs from the sensorimotor brain region, suggesting that they may encode sleep drive by integrating sensorimotor and navigation cues. Together, our experiments show that the four SpsP neurons and their sleep regulatory circuit play an important and dynamic role in sleep regulation.

Analysis methods for large-scale neuronal recordings

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

Stem cell-specific ecdysone signaling regulates the development of dorsal fan-shaped body neurons and sleep homeostasis

Complex behaviors arise from neural circuits that assemble from diverse cell types. Sleep is a conserved behavior essential for survival, yet little is known about how the nervous system generates neuron types of a sleep-wake circuit. Here, we focus on the specification of Drosophila 23E10-labeled dorsal fan-shaped body (dFB) long-field tangential input neurons that project to the dorsal layers of the fan-shaped body neuropil in the central complex. We use lineage analysis and genetic birth dating to identify two bilateral type II neural stem cells (NSCs) that generate 23E10 dFB neurons. We show that adult 23E10 dFB neurons express ecdysone-induced protein 93 (E93) and that loss of ecdysone signaling or E93 in type II NSCs results in their misspecification. Finally, we show that E93 knockdown in type II NSCs impairs adult sleep behavior. Our results provide insight into how extrinsic hormonal signaling acts on NSCs to generate the neuronal diversity required for adult sleep behavior. These findings suggest that some adult sleep disorders might derive from defects in stem cell-specific temporal neurodevelopmental programs.

Neural circuits for goal-directed navigation across species

Across species, navigation is crucial for finding both resources and shelter. In vertebrates, the hippocampus supports memory-guided goal-directed navigation, whereas in arthropods the central complex supports similar functions. A growing literature is revealing similarities and differences in the organization and function of these brain regions. We review current knowledge about how each structure supports goal-directed navigation by building internal representations of the position or orientation of an animal in space, and of the location or direction of potential goals. We describe input pathways to each structure - medial and lateral entorhinal cortex in vertebrates, and columnar and tangential neurons in insects - that primarily encode spatial and non-spatial information, respectively. Finally, we highlight similarities and differences in spatial encoding across clades and suggest experimental approaches to compare coding principles and behavioral capabilities across species. Such a comparative approach can provide new insights into the neural basis of spatial navigation and neural computation.

Maintaining and updating accurate internal representations of continuous variables with a handful of neurons

Many animals rely on persistent internal representations of continuous variables for working memory, navigation, and motor control. Existing theories typically assume that large networks of neurons are required to maintain such representations accurately; networks with few neurons are thought to generate discrete representations. However, analysis of two-photon calcium imaging data from tethered flies walking in darkness suggests that their small head-direction system can maintain a surprisingly continuous and accurate representation. We thus ask whether it is possible for a small network to generate a continuous, rather than discrete, representation of such a variable. We show analytically that even very small networks can be tuned to maintain continuous internal representations, but this comes at the cost of sensitivity to noise and variations in tuning. This work expands the computational repertoire of small networks, and raises the possibility that larger networks could represent more and higher-dimensional variables than previously thought.

Fine-grained descending control of steering in walking Drosophila

Locomotion involves rhythmic limb movement patterns that originate in circuits outside the brain. Purposeful locomotion requires descending commands from the brain, but we do not understand how these commands are structured. Here, we investigate this issue, focusing on the control of steering in walking Drosophila. First, we describe different limb "gestures" associated with different steering maneuvers. Next, we identify a set of descending neurons whose activity predicts steering. Focusing on two descending cell types downstream of distinct brain networks, we show that they evoke specific limb gestures: one lengthens strides on the outside of a turn, while the other attenuates strides on the inside of a turn. Our results suggest that a single descending neuron can have opposite effects during different locomotor rhythm phases, and we identify networks positioned to implement this phase-specific gating. Together, our results show how purposeful locomotion emerges from specific, coordinated modulations of low-level patterns.

The RNA exosome maintains cellular RNA homeostasis by controlling transcript abundance in the brain

Intracellular ribonucleases (RNases) are essential in all aspects of RNA metabolism, including maintaining accurate RNA levels. Inherited mutations in genes encoding ubiquitous RNases are associated with human diseases, primarily affecting the nervous system. Recessive mutations in genes encoding an evolutionarily conserved RNase complex, the RNA exosome, lead to syndromic neurodevelopmental disorders characterized by progressive neurodegeneration, such as Pontocerebellar Hypoplasia Type 1b (PCH1b). We establish a CRISPR/Cas9-engineered Drosophila model of PCH1b to study cell-type-specific post-transcriptional regulatory functions of the nuclear RNA exosome complex within fly head tissue. Here, we report that pathogenic RNA exosome mutations alter activity of the complex, causing widespread dysregulation of brain-enriched cellular transcriptomes, including rRNA processing defects-resulting in tissue-specific, progressive neurodegenerative effects in flies. These findings provide a comprehensive understanding of RNA exosome function within a developed animal brain and underscore the critical role of post-transcriptional regulatory machinery in maintaining cellular RNA homeostasis within the brain.

Ancestral neural circuits potentiate the origin of a female sexual behavior in Drosophila

Courtship interactions are remarkably diverse in form and complexity among species. How neural circuits evolve to encode new behaviors that are functionally integrated into these dynamic social interactions is unknown. Here we report a recently originated female sexual behavior in the island endemic Drosophila species D. santomea, where females signal receptivity to male courtship songs by spreading their wings, which in turn promotes prolonged songs in courting males. Copulation success depends on this female signal and correlates with males' ability to adjust his singing in such a social feedback loop. Functional comparison of sexual circuitry across species suggests that a pair of descending neurons, which integrates male song stimuli and female internal state to control a conserved female abdominal behavior, drives wing spreading in D. santomea. This co-option occurred through the refinement of a pre-existing, plastic circuit that can be optogenetically activated in an outgroup species. Combined, our results show that the ancestral potential of a socially-tuned key circuit node to engage the wing motor circuit facilitates the expression of a new female behavior in appropriate sensory and motivational contexts. More broadly, our work provides insights into the evolution of social behaviors, particularly female behaviors, and the underlying neural mechanisms.

Serotonergic modulation of swallowing in a complete fly vagus nerve connectome

How the body interacts with the brain to perform vital life functions, such as feeding, is a fundamental issue in physiology and neuroscience. Here, we use a whole-animal scanning transmission electron microscopy volume of Drosophila to map the neuronal circuits that connect the entire enteric nervous system to the brain via the insect vagus nerve at synaptic resolution. We identify a gut-brain feedback loop in which Piezo-expressing mechanosensory neurons in the esophagus convey food passage information to a cluster of six serotonergic neurons in the brain. Together with information on food value, these central serotonergic neurons enhance the activity of serotonin receptor 7-expressing motor neurons that drive swallowing. This elemental circuit architecture includes an axo-axonic synaptic connection from the glutamatergic motor neurons innervating the esophageal muscles onto the mechanosensory neurons that signal to the serotonergic neurons. Our analysis elucidates a neuromodulatory sensory-motor system in which ongoing motor activity is strengthened through serotonin upon completion of a biologically meaningful action, and it may represent an ancient form of motor learning.

Connectomic reconstruction predicts visual features used for navigation

Many animals use visual information to navigate1-4, but how such information is encoded and integrated by the navigation system remains incompletely understood. In Drosophila melanogaster, EPG neurons in the central complex compute the heading direction5 by integrating visual input from ER neurons6-12, which are part of the anterior visual pathway (AVP)10,13-16. Here we densely reconstruct all neurons in the AVP using electron-microscopy data17. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons10,14,15, which connect the medulla in the optic lobe to the small unit of the anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons9,16, which connect the AOTUsu to the bulb neuropil; and ER neurons6-12, which connect the bulb to the EPG neurons. On the basis of morphologies, connectivity between neural classes and the locations of synapses, we identify distinct information channels that originate from four types of MeTu neurons, and we further divide these into ten subtypes according to the presynaptic connections in the medulla and the postsynaptic connections in the AOTUsu. Using the connectivity of the entire AVP and the dendritic fields of the MeTu neurons in the optic lobes, we infer potential visual features and the visual area from which any ER neuron receives input. We confirm some of these predictions physiologically. These results provide a strong foundation for understanding how distinct sensory features can be extracted and transformed across multiple processing stages to construct higher-order cognitive representations.

Brain bilateral asymmetry - insights from nematodes, zebrafish, and Drosophila

Chirality is a fundamental trait of living organisms, encompassing the homochirality of biological molecules and the left-right (LR) asymmetry of visceral organs and the brain. The nervous system in bilaterian organisms displays a lateralized organization characterized by the presence of asymmetrical neuronal circuits and brain functions that are predominantly localized within one hemisphere. Although body asymmetry is relatively well understood, and exhibits robust phenotypic expression and regulation via conserved molecular mechanisms across phyla, current findings indicate that the asymmetry of the nervous system displays greater phenotypic, genetic, and evolutionary variability. In this review we explore the use of nematode, zebrafish, and Drosophila genetic models to investigate neuronal circuit asymmetry. We discuss recent discoveries in the context of body-brain concordance and highlight the distinct characteristics of nervous system asymmetry and its cognitive correlates.

Predicting visual function by interpreting a neuronal wiring diagram

As connectomics advances, it will become commonplace to know far more about the structure of a nervous system than about its function. The starting point for many investigations will become neuronal wiring diagrams, which will be interpreted to make theoretical predictions about function. Here I demonstrate this emerging approach with the Drosophila optic lobe, analysing its structure to predict that three Dm3 (refs. 1-4) and three TmY (refs. 2,4) cell types are part of a circuit that serves the function of form vision. Receptive fields are predicted from connectivity, and suggest that the cell types encode the local orientation of a visual stimulus. Extraclassical5,6 receptive fields are also predicted, with implications for robust orientation tuning7, position invariance8,9 and completion of noisy or illusory contours10,11. The TmY types synapse onto neurons that project from the optic lobe to the central brain12,13, which are conjectured to compute conjunctions and disjunctions of oriented features. My predictions can be tested through neurophysiology, which would constrain the parameters and biophysical mechanisms in neural network models of fly vision14.

The fly connectome reveals a path to the effectome

A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome1-3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the 'effectome'. Specifically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons-thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly.

Network statistics of the whole-brain connectome of Drosophila

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections1-3, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations4,5, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex ( https://codex.flywire.ai ) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1-6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10-12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome-a map of projections between regions-from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.

Whole-brain annotation and multi-connectome cell typing of Drosophila

The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.

Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation

Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabilities, particularly considering the limited computational capacity. On the other hand, computational principles underlying these extraordinary feats are still only partially understood. The theoretical framework of reinforcement learning (RL) provides an ideal focal point to bring the two fields together for mutual benefit. In particular, we analyze and compare representations of space in robot and insect navigation models through the lens of RL, as the efficiency of insect navigation is likely rooted in an efficient and robust internal representation, linking retinotopic (egocentric) visual input with the geometry of the environment. While RL has long been at the core of robot navigation research, current computational theories of insect navigation are not commonly formulated within this framework, but largely as an associative learning process implemented in the insect brain, especially in the mushroom body (MB). Here we propose specific hypothetical components of the MB circuit that would enable the implementation of a certain class of relatively simple RL algorithms, capable of integrating distinct components of a navigation task, reminiscent of hierarchical RL models used in robot navigation. We discuss how current models of insect and robot navigation are exploring representations beyond classical, complete map-like representations, with spatial information being embedded in the respective latent representations to varying degrees.

Taste cells expressing Ionotropic Receptor 94e reciprocally impact feeding and egg laying in Drosophila

Chemosensory cells across the body of Drosophila melanogaster evaluate the environment to prioritize certain behaviors. Previous mapping of gustatory receptor neurons (GRNs) on the fly labellum identified a set of neurons in L-type sensilla that express Ionotropic Receptor 94e (IR94e), but the impact of IR94e GRNs on behavior remains unclear. We used optogenetics and chemogenetics to activate IR94e neurons and found that they drive mild feeding suppression but enhance egg laying. In vivo calcium imaging revealed that IR94e GRNs respond strongly to certain amino acids, including glutamate, and that IR94e plus co-receptors IR25a and IR76b are required for amino acid detection. Furthermore, IR94e mutants show behavioral changes to solutions containing amino acids, including increased consumption and decreased egg laying. Overall, our results suggest that IR94e GRNs on the fly labellum discourage feeding and encourage egg laying as part of an important behavioral switch in response to certain chemical cues.

A neural circuit architecture for rapid learning in goal-directed navigation

Anchoring goals to spatial representations enables flexible navigation but is challenging in novel environments when both representations must be acquired simultaneously. We propose a framework for how Drosophila uses internal representations of head direction (HD) to build goal representations upon selective thermal reinforcement. We show that flies use stochastically generated fixations and directed saccades to express heading preferences in an operant visual learning paradigm and that HD neurons are required to modify these preferences based on reinforcement. We used a symmetric visual setting to expose how flies' HD and goal representations co-evolve and how the reliability of these interacting representations impacts behavior. Finally, we describe how rapid learning of new goal headings may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in circuit architecture. Such evolutionarily structured architectures, which enable rapidly adaptive behavior driven by internal representations, may be relevant across species.

Neural Control of Naturalistic Behavior Choices

In the natural world, animals make decisions on an ongoing basis, continuously selecting which action to undertake next. In the lab, however, the neural bases of decision processes have mostly been studied using artificial trial structures. New experimental tools based on the genetic toolkit of model organisms now make it experimentally feasible to monitor and manipulate neural activity in small subsets of neurons during naturalistic behaviors. We thus propose a new approach to investigating decision processes, termed reverse neuroethology. In this approach, experimenters select animal models based on experimental accessibility and then utilize cutting-edge tools such as connectomes and genetically encoded reagents to analyze the flow of information through an animal's nervous system during naturalistic choice behaviors. We describe how the reverse neuroethology strategy has been applied to understand the neural underpinnings of innate, rapid decision making, with a focus on defensive behavioral choices in the vinegar fly Drosophila melanogaster.

Estimating orientation in natural scenes: A spiking neural network model of the insect central complex

The central complex of insects contains cells, organised as a ring attractor, that encode head direction. The 'bump' of activity in the ring can be updated by idiothetic cues and external sensory information. Plasticity at the synapses between these cells and the ring neurons, that are responsible for bringing sensory information into the central complex, has been proposed to form a mapping between visual cues and the heading estimate which allows for more accurate tracking of the current heading, than if only idiothetic information were used. In Drosophila, ring neurons have well characterised non-linear receptive fields. In this work we produce synthetic versions of these visual receptive fields using a combination of excitatory inputs and mutual inhibition between ring neurons. We use these receptive fields to bring visual information into a spiking neural network model of the insect central complex based on the recently published Drosophila connectome. Previous modelling work has focused on how this circuit functions as a ring attractor using the same type of simple visual cues commonly used experimentally. While we initially test the model on these simple stimuli, we then go on to apply the model to complex natural scenes containing multiple conflicting cues. We show that this simple visual filtering provided by the ring neurons is sufficient to form a mapping between heading and visual features and maintain the heading estimate in the absence of angular velocity input. The network is successful at tracking heading even when presented with videos of natural scenes containing conflicting information from environmental changes and translation of the camera.

Parallel vector memories in the brain of a bee as foundation for flexible navigation

Insects rely on path integration (vector-based navigation) and landmark guidance to perform sophisticated navigational feats, rivaling those seen in mammals. Bees in particular exhibit complex navigation behaviors including creating optimal routes and novel shortcuts between locations, an ability historically indicative of the presence of a cognitive map. A mammalian cognitive map has been widely accepted. However, in insects, the existence of a centralized cognitive map is highly contentious. Using a controlled laboratory assay that condenses foraging behaviors to short distances in walking bumblebees, we reveal that vectors learned during path integration can be transferred to long-term memory, that multiple such vectors can be stored in parallel, and that these vectors can be recalled at a familiar location and used for homeward navigation. These findings demonstrate that bees meet the two fundamental requirements of a vector-based analog of a decentralized cognitive map: Home vectors need to be stored in long-term memory and need to be recalled from remembered locations. Thus, our data demonstrate that bees possess the foundational elements for a vector-based map. By utilizing this relatively simple strategy for spatial organization, insects may achieve high-level navigation behaviors seen in vertebrates with the limited number of neurons in their brains, circumventing the computational requirements associated with the cognitive maps of mammals.