A neural circuit for wind-guided olfactory navigation

Bifurcation enhances temporal information encoding in the olfactory periphery

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.

Bifurcation enhances temporal information encoding in the olfactory periphery

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.

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.

Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster

High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.

Getting a glimpse into the sensory worlds of animals: the Editors' and Readers' Choice Awards 2024

The Editors' and Readers' Choice Awards were established in 2022 to celebrate some of the outstanding articles published every year in the Journal of Comparative Physiology A. The recipients of the 2024 Editors' Choice Awards were selected based on votes cast by the Editorial Board on articles published in 2023. In the category Original Paper, this distinction goes to 'Views from 'crabworld': the spatial distribution of light in a tropical mudflat' by Jochen Zeil (J Comp Physiol A 209:859-876, 2023); and in the category Review Article to 'Olfactory navigation in arthropods' by Theresa J. Steele and colleagues (J Comp Physiol A 209:467-488, 2023). The winners of the 2024 Readers' Choice Awards were determined by the number of online accesses of articles published in 2022. In the category Original Paper, the winner is 'Broadband 75-85 MHz radiofrequency fields disrupt magnetic compass orientation in night‑migratory songbirds consistent with a flavin‑based radical pair magnetoreceptor' by Bo Leberecht and colleagues (J Comp Physiol A 208:97-106, 2022). In the category Review Article, the winner is 'Magnetic maps in animal navigation' by Kenneth J. Lohmann and colleagues (J Comp Physiol A 208:41-67, 2022), which already won the Editors' Choice Award in 2023.

Skewing information flow through pre- and postsynaptic plasticity in the mushroom bodies of Drosophila

Animal brains need to store information to construct a representation of their environment. Knowledge of what happened in the past allows both vertebrates and invertebrates to predict future outcomes by recalling previous experience. Although invertebrate and vertebrate brains share common principles at the molecular, cellular, and circuit-architectural levels, there are also obvious differences as exemplified by the use of acetylcholine versus glutamate as the considered main excitatory neurotransmitters in the respective central nervous systems. Nonetheless, across central nervous systems, synaptic plasticity is thought to be a main substrate for memory storage. Therefore, how brain circuits and synaptic contacts change following learning is of fundamental interest for understanding brain computations tied to behavior in any animal. Recent progress has been made in understanding such plastic changes following olfactory associative learning in the mushroom bodies (MBs) of Drosophila A current framework of memory-guided behavioral selection is based on the MB skew model, in which antagonistic synaptic pathways are selectively changed in strength. Here, we review insights into plasticity at dedicated Drosophila MB output pathways and update what is known about the plasticity of both pre- and postsynaptic compartments of Drosophila MB neurons.

Q-Learning to navigate turbulence without a map

We consider the problem of olfactory searches in a turbulent environment. We focus on agents that respond solely to odor stimuli, with no access to spatial perception nor prior information about the odor location. We ask whether navigation strategies to a target can be learned robustly within a sequential decision making framework. We develop a reinforcement learning algorithm using a small set of interpretable olfactory states and train it with realistic turbulent odor cues. By introducing a temporal memory, we demonstrate that two salient features of odor traces, discretized in few olfactory states, are sufficient to learn navigation in a realistic odor plume. Performance is dictated by the sparse nature of turbulent plumes. An optimal memory exists which ignores blanks within the plume and activates a recovery strategy outside the plume. We obtain the best performance by letting agents learn their recovery strategy and show that it is mostly casting cross wind, similar to behavior observed in flying insects. The optimal strategy is robust to substantial changes in the odor plumes, suggesting minor parameter tuning may be sufficient to adapt to different environments.

Dynamic antennal positioning allows honeybee followers to decode the dance

The honeybee waggle dance has been widely studied as a communication system, yet we know little about how nestmates assimilate the information needed to navigate toward the signaled resource. They are required to detect the dancer's orientation relative to gravity and duration of the waggle phase and translate this into a flight vector with a direction relative to the sun1 and distance from the hive.2,3 Moreover, they appear capable of doing so from varied, dynamically changing positions around the dancer. Using high-speed, high-resolution video, we have uncovered a previously unremarked correlation between antennal position and the relative body axes of dancer and follower bees. Combined with new information about antennal inputs4,5 and spatial encoding in the insect central complex,6,7 we show how a neural circuit first proposed to underlie path integration could be adapted to decoding the dance and acquiring the signaled information as a flight vector that can be followed to the resource. This provides the first plausible account of how the bee brain could support the interpretation of its dance language.

Inhibitory control of 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 over time, for example switching between long-range dispersal behaviors and more localized search depending on the availability of resources. How pre-motor circuits regulate such locomotor statistics is not clear. Here we took advantage of the robust changes in locomotor statistics evoked by attractive odors in walking Drosophila to investigate their neural control. We began by analyzing the statistics of ground speed and angular velocity during three well-defined motor regimes: baseline walking, upwind running during odor, and search behavior following odor offset. We find that during search behavior, flies adopt higher angular velocities and slower ground speeds, and tend to turn for longer periods of time in one direction. We further find that flies spontaneously adopt periods of different mean ground speed, and that these changes in state influence the length of odor-evoked runs. We next developed a simple physiologically-inspired computational model of locomotor control that can recapitulate these statistical features of fly locomotion. Our model suggests that contralateral inhibition plays a key role both in regulating the difference between baseline and search behavior, and in modulating the response to odor with ground speed. As the fly connectome predicts decussating inhibitory neurons in the lateral accessory lobe (LAL), a pre-motor structure, we generated genetic tools to target these neurons and test their role in behavior. Consistent with our model, we found that activation of neurons labeled in one line increased curvature. In a second line labeling distinct neurons, activation and inactivation strongly and reciprocally regulated ground speed and altered the length of the odor-evoked run. Additional targeted light activation experiments argue that these effects arise from the brain rather than from neurons in the ventral nerve cord, while sparse activation experiments argue that speed control in the second line arises from both LAL neurons and a population of neurons in the dorsal superior medial protocerebrum (SMP). Together, our work develops a biologically plausible computational architecture that captures the statistical features of fly locomotion across behavioral states and identifies potential neural substrates of these computations.

Wind Gates Search States in Free Flight

For any organism tracking a chemical cue to its source, the motion of its surrounding fluid provides crucial information for success. For both swimming and flying animals engaged in olfaction driven search, turning into the direction of oncoming wind or water current is often a critical first step 1, 2 . However, in nature, wind and water currents may not always provide a reliable directional cue 3, 4, 5 . It is unclear how organisms adjust their search strategies accordingly due to the challenges of separately controlling flow and chemical encounters. Here, we use the genetic toolkit of Drosophila melanogaster , a model organism for olfaction 6 , to develop an optogenetic paradigm to deliver temporally precise "virtual" olfactory experiences in free-flying animals while independently manipulating the wind conditions. We show that in free flight, Drosophila melanogaster adopt distinct search routines that are gated by whether they are flying in laminar wind or in still air. We first confirm that in laminar wind flies turn upwind, and further, we show that they achieve this using a rapid turn. In still air, flies adopt remarkably stereotyped "sink and circle" search state characterized by ∼60°turns at 3-4 Hz, biased in a consistent direction. In both laminar wind and still air, immediately after odor onset, flies decelerate and often perform a rapid turn. Both maneuvers are consistent with predictions from recent control theoretic analyses for how insects may estimate properties of wind while in flight 7, 8 . We suggest that flies may use their deceleration and "anemometric" turn as active sensing maneuvers to rapidly gauge properties of their wind environment before initiating a proximal or upwind search routine.

The conserved RNA-binding protein Imp is required for the specification and function of olfactory navigation circuitry in Drosophila

Complex behaviors depend on the precise developmental specification of neuronal circuits, but the relationship between genetic programs for neural development, circuit structure, and behavioral output is often unclear. The central complex (CX) is a conserved sensory-motor integration center in insects, which governs many higher-order behaviors and largely derives from a small number of type II neural stem cells (NSCs). Here, we show that Imp, a conserved IGF-II mRNA-binding protein expressed in type II NSCs, plays a role in specifying essential components of CX olfactory navigation circuitry. We show the following: (1) that multiple components of olfactory navigation circuitry arise from type II NSCs. (2) Manipulating Imp expression in type II NSCs alters the number and morphology of many of these circuit elements, with the most potent effects on neurons targeting the ventral layers of the fan-shaped body (FB). (3) Imp regulates the specification of Tachykinin-expressing ventral FB input neurons. (4) Imp is required in type II NSCs for establishing proper morphology of the CX neuropil structures. (5) Loss of Imp in type II NSCs abolishes upwind orientation to attractive odor while leaving locomotion and odor-evoked regulation of movement intact. Taken together, our findings establish that a temporally expressed gene can regulate the expression of a complex behavior by developmentally regulating the specification of multiple circuit components and provides a first step toward a developmental dissection of the CX and its roles in behavior.

Transforming a head direction signal into a goal-oriented steering command

To navigate, we must continuously estimate the direction we are headed in, and we must correct deviations from our goal1. Direction estimation is accomplished by ring attractor networks in the head direction system2,3. However, we do not fully understand how the sense of direction is used to guide action. Drosophila connectome analyses4,5 reveal three cell populations (PFL3R, PFL3L and PFL2) that connect the head direction system to the locomotor system. Here we use imaging, electrophysiology and chemogenetic stimulation during navigation to show how these populations function. Each population receives a shifted copy of the head direction vector, such that their three reference frames are shifted approximately 120° relative to each other. Each cell type then compares its own head direction vector with a common goal vector; specifically, it evaluates the congruence of these vectors via a nonlinear transformation. The output of all three cell populations is then combined to generate locomotor commands. PFL3R cells are recruited when the fly is oriented to the left of its goal, and their activity drives rightward turning; the reverse is true for PFL3L. Meanwhile, PFL2 cells increase steering speed, and are recruited when the fly is oriented far from its goal. PFL2 cells adaptively increase the strength of steering as directional error increases, effectively managing the tradeoff between speed and accuracy. Together, our results show how a map of space in the brain can be combined with an internal goal to generate action commands, via a transformation from world-centric coordinates to body-centric coordinates.

Converting an allocentric goal into an egocentric steering signal

Neuronal signals that are relevant for spatial navigation have been described in many species1-10. However, a circuit-level understanding of how such signals interact to guide navigational behaviour is lacking. Here we characterize a neuronal circuit in the Drosophila central complex that compares internally generated estimates of the heading and goal angles of the fly-both of which are encoded in world-centred (allocentric) coordinates-to generate a body-centred (egocentric) steering signal. Past work has suggested that the activity of EPG neurons represents the fly's moment-to-moment angular orientation, or heading angle, during navigation2,11. An animal's moment-to-moment heading angle, however, is not always aligned with its goal angle-that is, the allocentric direction in which it wishes to progress forward. We describe FC2 cells12, a second set of neurons in the Drosophila brain with activity that correlates with the fly's goal angle. Focal optogenetic activation of FC2 neurons induces flies to orient along experimenter-defined directions as they walk forward. EPG and FC2 neurons connect monosynaptically to a third neuronal class, PFL3 cells12,13. We found that individual PFL3 cells show conjunctive, spike-rate tuning to both the heading angle and the goal angle during goal-directed navigation. Informed by the anatomy and physiology of these three cell classes, we develop a model that explains how this circuit compares allocentric heading and goal angles to build an egocentric steering signal in the PFL3 output terminals. Quantitative analyses and optogenetic manipulations of PFL3 activity support the model. Finally, using a new navigational memory task, we show that flies expressing disruptors of synaptic transmission in subsets of PFL3 cells have a reduced ability to orient along arbitrary goal directions, with an effect size in quantitative accordance with the prediction of our model. The biological circuit described here reveals how two population-level allocentric signals are compared in the brain to produce an egocentric output signal that is appropriate for motor control.

New genetic tools for mushroom body output neurons in Drosophila

How memories of past events influence behavior is a key question in neuroscience. The major associative learning center in Drosophila, the mushroom body (MB), communicates to the rest of the brain through mushroom body output neurons (MBONs). While 21 MBON cell types have their dendrites confined to small compartments of the MB lobes, analysis of EM connectomes revealed the presence of an additional 14 MBON cell types that are atypical in having dendritic input both within the MB lobes and in adjacent brain regions. Genetic reagents for manipulating atypical MBONs and experimental data on their functions have been lacking. In this report we describe new cell-type-specific GAL4 drivers for many MBONs, including the majority of atypical MBONs that extend the collection of MBON driver lines we have previously generated (Aso et al., 2014a; Aso et al., 2016; Aso et al., 2019). Using these genetic reagents, we conducted optogenetic activation screening to examine their ability to drive behaviors and learning. These reagents provide important new tools for the study of complex behaviors in Drosophila.

Monarch butterflies memorize the spatial location of a food source

Spatial memory helps animals to navigate familiar environments. In insects, spatial memory has extensively been studied in central place foragers such as ants and bees. However, if butterflies memorize a spatial location remains unclear. Here, we conducted behavioural experiments to test whether monarch butterflies (Danaus plexippus) can remember and retrieve the spatial location of a food source. We placed several visually identical feeders in a flight cage, with only one feeder providing sucrose solution. Across multiple days, individual butterflies predominantly visited the rewarding feeder. Next, we displaced a salient landmark close to the feeders to test which visual cue the butterflies used to relocate the rewarding feeder. While occasional landmark displacements were ignored by the butterflies and did not affect their decisions, systematic displacement of both the landmark and the rewarding feeder demonstrated that the butterflies associated the salient landmark with the feeder's position. Altogether, we show that butterflies consolidate and retrieve spatial memory in the context of foraging.

The neurobiology of the Monarch butterfly compass

Monarch butterflies (Danaus plexippus) have become a superb model system to unravel how the tiny insect brain controls an impressive navigation behavior, such as long-distance migration. Moreover, the ability to compare the neural substrate between migratory and nonmigratory Monarch butterflies provides us with an attractive model to specifically study how the insect brain is adapted for migration. We here review our current progress on the neural substrate of spatial orientation in Monarch butterflies and how their spectacular annual migration might be controlled by their brain. We also discuss open research questions, the answers to which will provide important missing pieces to obtain a full picture of insect migration - from the perception of orientation cues to the neural control of migration.

Emergent spatial goals in an integrative model of the insect central complex

The insect central complex appears to encode and process spatial information through vector manipulation. Here, we draw on recent insights into circuit structure to fuse previous models of sensory-guided navigation, path integration and vector memory. Specifically, we propose that the allocentric encoding of location provided by path integration creates a spatially stable anchor for converging sensory signals that is relevant in multiple behavioural contexts. The allocentric reference frame given by path integration transforms a goal direction into a goal location and we demonstrate through modelling that it can enhance approach of a sensory target in noisy, cluttered environments or with temporally sparse stimuli. We further show the same circuit can improve performance in the more complex navigational task of route following. The model suggests specific functional roles for circuit elements of the central complex that helps explain their high preservation across insect species.

Connectomic reconstruction predicts the functional organization of visual inputs to the navigation center of the Drosophila brain

Many animals, including humans, navigate their surroundings by visual input, yet we understand little about how visual information is transformed and integrated by the navigation system. In Drosophila melanogaster, compass neurons in the donut-shaped ellipsoid body of the central complex generate a sense of direction by integrating visual input from ring neurons, a part of the anterior visual pathway (AVP). Here, we densely reconstruct all neurons in the AVP using FlyWire, an AI-assisted tool for analyzing electron-microscopy data. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons, which connect the medulla in the optic lobe to the small unit of anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons, which connect the anterior optic tubercle to the bulb neuropil; and ring neurons, which connect the bulb to the ellipsoid body. Based on neuronal morphologies, connectivity between different neural classes, and the locations of synapses, we identified non-overlapping channels originating from four types of MeTu neurons, which we further divided into ten subtypes based on the presynaptic connections in medulla and postsynaptic connections in AOTUsu. To gain an objective measure of the natural variation within the pathway, we quantified the differences between anterior visual pathways from both hemispheres and between two electron-microscopy datasets. Furthermore, we infer potential visual features and the visual area from which any given ring neuron receives input by combining the connectivity of the entire AVP, the MeTu neurons' dendritic fields, and presynaptic connectivity in the optic lobes. These results provide a strong foundation for understanding how distinct visual features are extracted and transformed across multiple processing stages to provide critical information for computing the fly's sense of direction.

Neuronal calcium spikes enable vector inversion in the Drosophila brain

A typical neuron signals to downstream cells when it is depolarized and firing sodium spikes. Some neurons, however, also fire calcium spikes when hyperpolarized. The function of such bidirectional signaling remains unclear in most circuits. Here we show how a neuron class that participates in vector computation in the fly central complex employs hyperpolarization-elicited calcium spikes to invert two-dimensional mathematical vectors. When cells switch from firing sodium to calcium spikes, this leads to a ~180° realignment between the vector encoded in the neuronal population and the fly's internal heading signal, thus inverting the vector. We show that the calcium spikes rely on the T-type calcium channel Ca-α1T, and argue, via analytical and experimental approaches, that these spikes enable vector computations in portions of angular space that would otherwise be inaccessible. These results reveal a seamless interaction between molecular, cellular and circuit properties for implementing vector math in the brain.

Neural representation of goal direction in the monarch butterfly brain

Neural processing of a desired moving direction requires the continuous comparison between the current heading and the goal direction. While the neural basis underlying the current heading is well-studied, the coding of the goal direction remains unclear in insects. Here, we used tetrode recordings in tethered flying monarch butterflies to unravel how a goal direction is represented in the insect brain. While recording, the butterflies maintained robust goal directions relative to a virtual sun. By resetting their goal directions, we found neurons whose spatial tuning was tightly linked to the goal directions. Importantly, their tuning was unaffected when the butterflies changed their heading after compass perturbations, showing that these neurons specifically encode the goal direction. Overall, we here discovered invertebrate goal-direction neurons that share functional similarities to goal-direction cells reported in mammals. Our results give insights into the evolutionarily conserved principles of goal-directed spatial orientation in animals.

Neural circuit mechanisms for transforming learned olfactory valences into wind-oriented movement

How memories are used by the brain to guide future action is poorly understood. In olfactory associative learning in Drosophila, multiple compartments of the mushroom body act in parallel to assign a valence to a stimulus. Here, we show that appetitive memories stored in different compartments induce different levels of upwind locomotion. Using a photoactivation screen of a new collection of split-GAL4 drivers and EM connectomics, we identified a cluster of neurons postsynaptic to the mushroom body output neurons (MBONs) that can trigger robust upwind steering. These UpWind Neurons (UpWiNs) integrate inhibitory and excitatory synaptic inputs from MBONs of appetitive and aversive memory compartments, respectively. After formation of appetitive memory, UpWiNs acquire enhanced response to reward-predicting odors as the response of the inhibitory presynaptic MBON undergoes depression. Blocking UpWiNs impaired appetitive memory and reduced upwind locomotion during retrieval. Photoactivation of UpWiNs also increased the chance of returning to a location where activation was terminated, suggesting an additional role in olfactory navigation. Thus, our results provide insight into how learned abstract valences are gradually transformed into concrete memory-driven actions through divergent and convergent networks, a neuronal architecture that is commonly found in the vertebrate and invertebrate brains.

Olfactory search with finite-state controllers

Long-range olfactory search is an extremely difficult task in view of the sparsity of odor signals that are available to the searcher and the complex encoding of the information about the source location. Current algorithmic approaches typically require a continuous memory space, sometimes of large dimensionality, which may hamper their optimization and often obscure their interpretation. Here, we show how finite-state controllers with a small set of discrete memory states are expressive enough to display rich, time-extended behavioral modules that resemble the ones observed in living organisms. Finite-state controllers optimized for olfactory search have an immediate interpretation in terms of approximate clocks and coarse-grained spatial maps, suggesting connections with neural models of search behavior.

Continuous odor profile monitoring to study olfactory navigation in small animals

Olfactory navigation is observed across species and plays a crucial role in locating resources for survival. In the laboratory, understanding the behavioral strategies and neural circuits underlying odor-taxis requires a detailed understanding of the animal's sensory environment. For small model organisms like Caenorhabditis elegans and larval Drosophila melanogaster, controlling and measuring the odor environment experienced by the animal can be challenging, especially for airborne odors, which are subject to subtle effects from airflow, temperature variation, and from the odor's adhesion, adsorption, or reemission. Here, we present a method to control and measure airborne odor concentration in an arena compatible with an agar substrate. Our method allows continuous controlling and monitoring of the odor profile while imaging animal behavior. We construct stationary chemical landscapes in an odor flow chamber through spatially patterned odorized air. The odor concentration is measured with a spatially distributed array of digital gas sensors. Careful placement of the sensors allows the odor concentration across the arena to be continuously inferred in space and monitored through time. We use this approach to measure the odor concentration that each animal experiences as it undergoes chemotaxis behavior and report chemotaxis strategies for C. elegans and D. melanogaster larvae populations as they navigate spatial odor landscapes.

Neural Networks for Navigation: From Connections to Computations

Many animals can navigate toward a goal they cannot see based on an internal representation of that goal in the brain's spatial maps. These maps are organized around networks with stable fixed-point dynamics (attractors), anchored to landmarks, and reciprocally connected to motor control. This review summarizes recent progress in understanding these networks, focusing on studies in arthropods. One factor driving recent progress is the availability of the Drosophila connectome; however, it is increasingly clear that navigation depends on ongoing synaptic plasticity in these networks. Functional synapses appear to be continually reselected from the set of anatomical potential synapses based on the interaction of Hebbian learning rules, sensory feedback, attractor dynamics, and neuromodulation. This can explain how the brain's maps of space are rapidly updated; it may also explain how the brain can initialize goals as stable fixed points for navigation.

Olfactory navigation in arthropods

Using odors to find food and mates is one of the most ancient and highly conserved behaviors. Arthropods from flies to moths to crabs use broadly similar strategies to navigate toward odor sources-such as integrating flow information with odor information, comparing odor concentration across sensors, and integrating odor information over time. Because arthropods share many homologous brain structures-antennal lobes for processing olfactory information, mechanosensors for processing flow, mushroom bodies (or hemi-ellipsoid bodies) for associative learning, and central complexes for navigation, it is likely that these closely related behaviors are mediated by conserved neural circuits. However, differences in the types of odors they seek, the physics of odor dispersal, and the physics of locomotion in water, air, and on substrates mean that these circuits must have adapted to generate a wide diversity of odor-seeking behaviors. In this review, we discuss common strategies and specializations observed in olfactory navigation behavior across arthropods, and review our current knowledge about the neural circuits subserving this behavior. We propose that a comparative study of arthropod nervous systems may provide insight into how a set of basic circuit structures has diversified to generate behavior adapted to different environments.

The RNA-binding protein, Imp specifies olfactory navigation circuitry and behavior in Drosophila

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

Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments

To survive, insects must effectively navigate odors plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking Drosophila. We find that fly angular velocity depends on signal frequency and intermittency-the fraction of time signal can be detected-but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.

The influence of stimulus history on directional coding in the monarch butterfly brain

The central complex is a brain region in the insect brain that houses a neural network specialized to encode directional information. Directional coding has traditionally been investigated with compass cues that revolve in full rotations and at constant angular velocities around the insect's head. However, these stimulus conditions do not fully simulate an insect's sensory perception of compass cues during navigation. In nature, an insect flight is characterized by abrupt changes in moving direction as well as constant changes in velocity. The influence of such varying cue dynamics on compass coding remains unclear. We performed long-term tetrode recordings from the brain of monarch butterflies to study how central complex neurons respond to different stimulus velocities and directions. As these butterflies derive directional information from the sun during migration, we measured the neural response to a virtual sun. The virtual sun was either presented as a spot that appeared at random angular positions or was rotated around the butterfly at different angular velocities and directions. By specifically manipulating the stimulus velocity and trajectory, we dissociated the influence of angular velocity and direction on compass coding. While the angular velocity substantially affected the tuning directedness, the stimulus trajectory influenced the shape of the angular tuning curve. Taken together, our results suggest that the central complex flexibly adjusts its directional coding to the current stimulus dynamics ensuring a precise compass even under highly demanding conditions such as during rapid flight maneuvers.

Parallel motion vision pathways in the brain of a tropical bee

Spatial orientation is a prerequisite for most behaviors. In insects, the underlying neural computations take place in the central complex (CX), the brain's navigational center. In this region different streams of sensory information converge to enable context-dependent navigational decisions. Accordingly, a variety of CX input neurons deliver information about different navigation-relevant cues. In bees, direction encoding polarized light signals converge with translational optic flow signals that are suited to encode the flight speed of the animals. The continuous integration of speed and directions in the CX can be used to generate a vector memory of the bee's current position in space in relation to its nest, i.e., perform path integration. This process depends on specific, complex features of the optic flow encoding CX input neurons, but it is unknown how this information is derived from the visual periphery. Here, we thus aimed at gaining insight into how simple motion signals are reshaped upstream of the speed encoding CX input neurons to generate their complex features. Using electrophysiology and anatomical analyses of the halictic bees Megalopta genalis and Megalopta centralis, we identified a wide range of motion-sensitive neurons connecting the optic lobes with the central brain. While most neurons formed pathways with characteristics incompatible with CX speed neurons, we showed that one group of lobula projection neurons possess some physiological and anatomical features required to generate the visual responses of CX optic-flow encoding neurons. However, as these neurons cannot explain all features of CX speed cells, local interneurons of the central brain or alternative input cells from the optic lobe are additionally required to construct inputs with sufficient complexity to deliver speed signals suited for path integration in bees.

Lineages to circuits: the developmental and evolutionary architecture of information channels into the central complex

The representation and integration of internal and external cues is crucial for any organism to execute appropriate behaviors. In insects, a highly conserved region of the brain, the central complex (CX), functions in the representation of spatial information and behavioral states, as well as the transformation of this information into desired navigational commands. How does this relatively invariant structure enable the incorporation of information from the diversity of anatomical, behavioral, and ecological niches occupied by insects? Here, we examine the input channels to the CX in the context of their development and evolution. Insect brains develop from ~ 100 neuroblasts per hemisphere that divide systematically to form "lineages" of sister neurons, that project to their target neuropils along anatomically characteristic tracts. Overlaying this developmental tract information onto the recently generated Drosophila "hemibrain" connectome and integrating this information with the anatomical and physiological recording of neurons in other species, we observe neuropil and lineage-specific innervation, connectivity, and activity profiles in CX input channels. We posit that the proliferative potential of neuroblasts and the lineage-based architecture of information channels enable the modification of neural networks across existing, novel, and deprecated modalities in a species-specific manner, thus forming the substrate for the evolution and diversification of insect navigational circuits.

Active antennal movements in Drosophila can tune wind encoding

Insects use their antennae to smell odors,1,2 detect auditory cues,3,4 and sense mechanosensory stimuli such as wind5 and objects,6,7,8 frequently by combining sensory processing with active movements. Genetic access to antennal motor systems would therefore provide a powerful tool for dissecting the circuit mechanisms underlying active sensing, but little is known about how the most genetically tractable insect, Drosophila melanogaster, moves its antennae. Here, we use deep learning to measure how tethered Drosophila move their antennae in the presence of sensory stimuli and identify genetic reagents for controlling antennal movement. We find that flies perform both slow adaptive movements and fast flicking movements in response to wind-induced deflections, but not the attractive odor apple cider vinegar. Next, we describe four muscles in the first antennal segment that control antennal movements and identify genetic driver lines that provide access to two groups of antennal motor neurons and an antennal muscle. Through optogenetic inactivation, we provide evidence that antennal motor neurons contribute to active movements with different time courses. Finally, we show that activation of antennal motor neurons and muscles can adjust the gain and acuity of wind direction encoding by antennal displacement. Together, our experiments provide insight into the neural control of antennal movement and suggest that active antennal positioning in Drosophila may tune the precision of wind encoding.

Impact of central complex lesions on innate and learnt visual navigation in ants

Wood ants are excellent navigators, using a combination of innate and learnt navigational strategies to travel between their nest and feeding sites. Visual navigation in ants has been studied extensively, however, we have little direct evidence for the underlying neural mechanisms. Here, we perform lateralized mechanical lesions in the central complex (CX) of wood ants, a midline structure known to allow an insect to keep track of the direction of sensory cues relative to its own orientation and to control movement. We lesioned two groups of ants and observed their behaviour in an arena with a large visual landmark present. The first group of ants were naïve and when intact such ants show a clear innate attraction to the conspicuous landmark. The second group of ants were trained to aim to a food location to the side of the landmark. The general heading of naïve ants towards a visual cue was not altered by the lesions, but the heading of ants trained to a landmark adjacent food position was affected. Thus, CX lesions had a specific impact on learnt visual guidance. We also observed that lateralised lesions altered the fine details of turning with lesioned ants spending less time turning to the side ipsilateral of the lesion. The results confirm the role of the CX in turn control and highlight its important role in the implementation of learnt behaviours that rely on information from other brain regions.

Behavioral algorithms and neural mechanisms underlying odor-modulated locomotion in insects

Odors released from mates and resources such as a host and food are often the first sensory signals that an animal can detect. Changes in locomotion in response to odors are an important mechanism by which animals access resources important to their survival. Odor-modulated changes in locomotion in insects constitute a whole suite of flexible behaviors that allow insects to close in on these resources from long distances and perform local searches to locate and subsequently assess them. Here, we review changes in odor-mediated locomotion across many insect species. We emphasize that changes in locomotion induced by odors are diverse. In particular, the olfactory stimulus is sporadic at long distances and becomes more continuous at short distances. This distance-dependent change in temporal profile produces a corresponding change in an insect's locomotory strategy. We also discuss the neural circuits underlying odor modulation of locomotion.

Drosophila olfaction: past, present and future

Among the many wonders of nature, the sense of smell of the fly Drosophila melanogaster might seem, at first glance, of esoteric interest. Nevertheless, for over a century, the 'nose' of this insect has been an extraordinary system to explore questions in animal behaviour, ecology and evolution, neuroscience, physiology and molecular genetics. The insights gained are relevant for our understanding of the sensory biology of vertebrates, including humans, and other insect species, encompassing those detrimental to human health. Here, I present an overview of our current knowledge of D. melanogaster olfaction, from molecules to behaviours, with an emphasis on the historical motivations of studies and illustration of how technical innovations have enabled advances. I also highlight some of the pressing and long-term questions.

Experimental and theoretical probe on mechano- and chemosensory integration in the insect antennal lobe

In nature, olfactory signals are delivered to detectors—for example, insect antennae—by means of turbulent air, which exerts concurrent chemical and mechanical stimulation on the detectors. The antennal lobe, which is traditionally viewed as a chemosensory module, sits downstream of antennal inputs. We review experimental evidence showing that, in addition to being a chemosensory structure, antennal lobe neurons also respond to mechanosensory input in the form of wind speed. Benchmarked with empirical data, we constructed a dynamical model to simulate bimodal integration in the antennal lobe, with model dynamics yielding insights such as a positive correlation between the strength of mechanical input and the capacity to follow high frequency odor pulses, an important task in tracking odor sources. Furthermore, we combine experimental and theoretical results to develop a conceptual framework for viewing the functional significance of sensory integration within the antennal lobe. We formulate the testable hypothesis that the antennal lobe alternates between two distinct dynamical regimes, one which benefits odor plume tracking and one which promotes odor discrimination. We postulate that the strength of mechanical input, which correlates with behavioral contexts such being mid-flight versus hovering near a flower, triggers the transition from one regime to the other.