Idiothetic Path Integration in the Fruit Fly Drosophila melanogaster

Nutrient homeostasis - translating internal states to behavior

Behavioral neuroscience aims to describe a causal relationship between neuronal processes and behavior. Animals' ever-changing physiological needs alter their internal states. Internal states then alter neuronal processes to adapt the behavior of the animal enabling it to meet its needs. Here, we describe nutrient-specific appetites as an attractive framework to study how internal states shape complex neuronal processes and resulting behavioral outcomes. Understanding how neurons detect nutrient states and how these are integrated at the level of neuronal circuits will provide a multilevel description of the mechanisms underlying complex feeding and foraging decisions.

Neuronal processing of amino acids in Drosophila: from taste sensing to behavioural regulation

Finding and feeding on appropriate food are crucial for all animals. Carbohydrates and amino acids are both essential nutrients, albeit with distinct roles: the former are the main energy source whereas the latter are the building blocks of proteins and are used as neurotransmitters. Despite their crucial role, neither the sensing nor the neuronal processing of amino acids is well understood. Studies in Drosophila melanogaster have only recently gained momentum in shedding new light on the molecular and neuronal mechanisms of peripheral and internal amino acid sensing, as well as the organization of amino acid feeding behaviour. Furthermore, amino acids have been shown to act as rewards in associative learning. Focusing on recent studies in Drosophila, we summarize what is known so far about the perception of, and the behavioural responses to, amino acids in insects, and try to identify key questions for future research.

The prandial process in flies

Feeding is fundamental to any heterotroph organism; in its role to quell hunger it overrides most other motivational states. But feeding also literally opens the door to harmful risks, especially for a saprophagous animal like Drosophila; ingestion of poisonous substrate can lead to irreversible damage. Thus feeding incorporates a series of steps with several checkpoints to guarantee that the ingestion remains beneficial and provides a balanced diet, or the feeding process is interrupted. Subsequently, we will summarize and describe the feeding process in Drosophila in a comprehensive manner. We propose eleven distinct steps for feeding, grouped into four categories, to address our current knowledge of prandial regulatory mechanisms in Drosophila.

Principles of Insect Path Integration

Continuously monitoring its position in space relative to a goal is one of the most essential tasks for an animal that moves through its environment. Species as diverse as rats, bees, and crabs achieve this by integrating all changes of direction with the distance covered during their foraging trips, a process called path integration. They generate an estimate of their current position relative to a starting point, enabling a straight-line return, following what is known as a home vector. While in theory path integration always leads the animal precisely back home, in the real world noise limits the usefulness of this strategy when operating in isolation. Noise results from stochastic processes in the nervous system and from unreliable sensory information, particularly when obtaining heading estimates. Path integration, during which angular self-motion provides the sole input for encoding heading (idiothetic path integration), results in accumulating errors that render this strategy useless over long distances. In contrast, when using an external compass this limitation is avoided (allothetic path integration). Many navigating insects indeed rely on external compass cues for estimating body orientation, whereas they obtain distance information by integration of steps or optic-flow-based speed signals. In the insect brain, a region called the central complex plays a key role for path integration. Not only does the central complex house a ring-attractor network that encodes head directions, neurons responding to optic flow also converge with this circuit. A neural substrate for integrating direction and distance into a memorized home vector has therefore been proposed in the central complex. We discuss how behavioral data and the theoretical framework of path integration can be aligned with these neural data.

Random Walk Revisited: Quantification and Comparative Analysis of Drosophila Walking Trajectories

Walking trajectory is frequently measured to assess animal behavior. Air-supported spherical treadmills have been developed for real-time monitoring of animal walking trajectories. However, current systems for mice mainly employ computer mouse microcameras (chip-on-board sensors) to monitor ball motion, and these detectors exhibit technical issues with focus and rotation scale. In addition, computational methods to analyze and quantify the “random walk” of organisms are under-developed. In this work, we overcame the hurdle of frame-to-signal translation to develop a treadmill system with camera-based detection. Moreover, we generated a package of mathematical methods to quantify distinct aspects of Drosophila walking trajectories. By extracting and quantifying certain features of walking dynamics with high temporal resolution, we found that depending on their internal state, flies employ different walking strategies to approach environmental cues. This camera-based treadmill system and method package may also be applicable to monitor the walking trajectories of other diverse animal species.

•

A camera-mode treadmill system was built to track Drosophila walking trajectories

•

Four key features were identified to describe walking strategies

•

Ball rotation is indispensable for full characterization of trajectories

•

Fed and starved control flies show no obvious differences in their random walk

Analysis of aligning active local searchers orbiting around their common home position

We discuss effects of pairwise aligning interactions in an ensemble of central place foragers or of searchers that are connected to a common home. In a wider sense, we also consider self-moving entities that are attracted to a central place such as, for instance, the zooplankton Daphnia being attracted to a beam of light. Single foragers move with constant speed due to some propulsive mechanism. They explore at random loops the space around and return rhytmically to their home. In the ensemble, the direction of the velocity of a searcher is aligned to the motion of its neighbors. At first, we perform simulations of this ensemble and find a cooperative behavior of the entities. Above an overcritical interaction strength the trajectories of the searcher qualitatively changes and searchers start to move along circles around the home position. Thereby, all searchers rotate either clockwise or anticlockwise around the central home position as it was reported for the zooplankton Daphnia. At second, the computational findings are analytically explained by the formulation of transport equations outgoing from the nonlinear mean field Fokker-Planck equation of the considered situation. In the asymptotic stationary limit, we find expressions for the critical interaction strength, the mean radial and orbital velocities of the searchers and their velocity variances. We also obtain the marginal spatial and angular densities in the undercritical regime where the foragers behave like individuals as well as in the overcritical regime where they rotate collectively around the considered home. We additionally elaborate the overdamped Smoluchowski-limit for the ensemble.

Learning a Spatial Task by Trial and Error in Drosophila

The ability to use memory to return to specific locations for foraging is advantageous for survival. Although recent reports have demonstrated that the fruit flies Drosophila melanogaster are capable of visual cue-driven place learning and idiothetic path integration [1-4], the depth and flexibility of Drosophila's ability to solve spatial tasks and the underlying neural substrate and genetic basis have not been extensively explored. Here, we show that Drosophila can remember a reward-baited location through reinforcement learning and do so quickly and without requiring vision. After gaining genetic access to neurons (through 0273-GAL4) with properties reminiscent of the vertebrate medial forebrain bundle (MFB) and developing a high-throughput closed-loop stimulation system, we found that both sighted and blind flies can learn-by trial and error-to repeatedly return to an unmarked location (in a rectangularly shaped arena) where a brief stimulation of the 0273-GAL4 neurons was available for each visit. We found that optogenetic stimulation of these neurons enabled learning by employing both a cholinergic pathway that promoted self-stimulation and a dopaminergic pathway that likely promoted association of relevant cues with reward. Lastly, inhibiting activities of specific neurons time-locked with stimulation of 0273-GAL4 neurons showed that mushroom bodies (MB) and central complex (CX) both play a role in promoting learning of our task. Our work uncovered new depth in flies' ability to learn a spatial task and established an assay with a level of throughput that permits a systematic genetic interrogation of flies' ability to learn spatial tasks.

Spatial Cognition: Allowing Natural Behaviours to Flourish in the Lab

Understanding the computational basis of spatial cognition requires observations of natural behaviour and the underlying neural circuits, which are difficult to do simultaneously: however, recent studies show how we might achieve this, combining rich virtual reality set-ups and the use of optogenetics in freely moving animals.

Path integration: how details of the honeybee waggle dance and the foraging strategies of desert ants might help in understanding its mechanisms

Path integration is a navigational strategy that gives an animal an estimate of its position relative to some starting point. For many decades, ingenious and probing behavioural experiments have been the only window onto the operation of path integration in arthropods. New methods have now made it possible to visualise the activity of neural circuits in Drosophila while they fly or walk in virtual reality. Studies of this kind, as well as electrophysiological recordings from single neurons in the brains of other insects, are revealing details of the neural mechanisms that control an insect's direction of travel and other aspects of path integration. The aim here is first to review the major features of path integration in foraging desert ants and honeybees, the current champion path integrators of the insect world, and second consider how the elaborate behaviour of these insects might be accommodated within the framework of the newly understood neural circuits. The discussion focuses particularly on the ability of ants and honeybees to use a celestial compass to give direction in Earth-based coordinates, and of honeybees to use a landscape panorama to provide directional guidance for path integration. The possibility is raised that well-ordered behaviour might in some cases substitute for complex circuitry.

Representation of Haltere Oscillations and Integration with Visual Inputs in the Fly Central Complex

The reduced hindwings of flies, known as halteres, are specialized mechanosensory organs that detect body rotations during flight. Primary afferents of the haltere encode its oscillation frequency linearly over a wide bandwidth and with precise phase-dependent spiking. However, it is not currently known whether information from haltere primary afferent neurons is sent to higher brain centers where sensory information about body position could be used in decision making, or whether precise spike timing is useful beyond the peripheral circuits that drive wing movements. We show that in cells in the central brain, the timing and rates of neural spiking can be modulated by sensory input from experimental haltere movements (driven by a servomotor). Using multichannel extracellular recording in restrained flesh flies (Sarcophaga bullata of both sexes), we examined responses of central complex cells to a range of haltere oscillation frequencies alone, and in combination with visual motion speeds and directions. Haltere-responsive units fell into multiple response classes, including those responding to any haltere motion and others with firing rates linearly related to the haltere frequency. Cells with multisensory responses showed higher firing rates than the sum of the unisensory responses at higher haltere frequencies. They also maintained visual properties, such as directional selectivity, while increasing response gain nonlinearly with haltere frequency. Although haltere inputs have been described extensively in the context of rapid locomotion control, we find haltere sensory information in a brain region known to be involved in slower, higher-order behaviors, such as navigation.SIGNIFICANCE STATEMENT Many animals use vision for navigation; however, these cues must be interpreted in the context of the body's position. In mammalian brains, hippocampal cells combine visual and vestibular information to encode head direction. A region of the arthropod brain, known as the central complex (CX), similarly encodes heading information, but it is unknown whether proprioceptive information is integrated here as well. We show that CX neurons respond to input from halteres, specialized proprioceptors in flies that detect body rotations. These neurons also respond to visual input, providing one of the few examples of multiple sensory modalities represented in individual CX cells. Haltere stimulation modifies neural responses to visual signals, providing a mechanism for integrating vision with proprioception.

Diverse Food-Sensing Neurons Trigger Idiothetic Local Search in Drosophila

Foraging animals may benefit from remembering the location of a newly discovered food patch while continuing to explore nearby [1, 2]. For example, after encountering a drop of yeast or sugar, hungry flies often perform a local search [3, 4]. That is, rather than remaining on the food or simply walking away, flies execute a series of exploratory excursions during which they repeatedly depart and return to the resource. Fruit flies, Drosophila melanogaster, can perform this food-centered search behavior in the absence of external landmarks, instead relying on internal (idiothetic) cues [5]. This path-integration behavior may represent a deeply conserved navigational capacity in insects [6, 7], but its underlying neural basis remains unknown. Here, we used optogenetic activation to screen candidate cell classes and found that local searches can be initiated by diverse sensory neurons. Optogenetically induced searches resemble those triggered by actual food, are modulated by starvation state, and exhibit key features of path integration. Flies perform tightly centered searches around the fictive food site, even within a constrained maze, and they can return to the fictive food site after long excursions. Together, these results suggest that flies enact local searches in response to a wide variety of food-associated cues and that these sensory pathways may converge upon a common neural system for navigation. Using a virtual reality system, we demonstrate that local searches can be optogenetically induced in tethered flies walking on a spherical treadmill, laying the groundwork for future studies to image the brain during path integration. VIDEO ABSTRACT.

The Central Complex as a Potential Substrate for Vector Based Navigation

Insects use path integration (PI) to maintain a home vector, but can also store and recall vector-memories that take them from home to a food location, and even allow them to take novel shortcuts between food locations. The neural circuit of the Central Complex (a brain area that receives compass and optic flow information) forms a plausible substrate for these behaviors. A recent model, grounded in neurophysiological and neuroanatomical data, can account for PI during outbound exploratory routes and the control of steering to return home. Here, we show that minor, hypothetical but neurally plausible, extensions of this model can additionally explain how insects could store and recall PI vectors to follow food-ward paths, take shortcuts, search at the feeder and re-calibrate their vector-memories with experience. In addition, a simple assumption about how one of multiple vector-memories might be chosen at any point in time can produce the development and maintenance of efficient routes between multiple locations, as observed in bees. The central complex circuitry is therefore well-suited to allow for a rich vector-based navigational repertoire.

Sound localization behavior in Drosophila melanogaster depends on inter-antenna vibration amplitude comparisons

Drosophila melanogaster hear with their antennae: sound evokes vibration of the distal antennal segment, and this vibration is transduced by specialized mechanoreceptor cells. The left and right antennae vibrate preferentially in response to sounds arising from different azimuthal angles. Therefore, by comparing signals from the two antennae, it should be possible to obtain information about the azimuthal angle of a sound source. However, behavioral evidence of sound localization has not been reported in Drosophila Here, we show that walking D. melanogaster do indeed turn in response to lateralized sounds. We confirm that this behavior is evoked by vibrations of the distal antennal segment. The rule for turning is different for sounds arriving from different locations: flies turn toward sounds in their front hemifield, but they turn away from sounds in their rear hemifield, and they do not turn at all in response to sounds from 90 or -90 deg. All of these findings can be explained by a simple rule: the fly steers away from the antenna with the larger vibration amplitude. Finally, we show that these behaviors generalize to sound stimuli with diverse spectro-temporal features, and that these behaviors are found in both sexes. Our findings demonstrate the behavioral relevance of the antenna's directional tuning properties. They also pave the way for investigating the neural implementation of sound localization, as well as the potential roles of sound-guided steering in courtship and exploration.

The insect central complex and the neural basis of navigational strategies

Oriented behaviour is present in almost all animals, indicating that it is an ancient feature that has emerged from animal brains hundreds of millions of years ago. Although many complex navigation strategies have been described, each strategy can be broken down into a series of elementary navigational decisions. In each moment in time, an animal has to compare its current heading with its desired direction and compensate for any mismatch by producing a steering response either to the right or to the left. Different from reflex-driven movements, target-directed navigation is not only initiated in response to sensory input, but also takes into account previous experience and motivational state. Once a series of elementary decisions are chained together to form one of many coherent navigation strategies, the animal can pursue a navigational target, e.g. a food source, a nest entrance or a constant flight direction during migrations. Insects show a great variety of complex navigation behaviours and, owing to their small brains, the pursuit of the neural circuits controlling navigation has made substantial progress over the last years. A brain region as ancient as insects themselves, called the central complex, has emerged as the likely navigation centre of the brain. Research across many species has shown that the central complex contains the circuitry that might comprise the neural substrate of elementary navigational decisions. Although this region is also involved in a wide range of other functions, we hypothesize in this Review that its role in mediating the animal's next move during target-directed behaviour is its ancestral function, around which other functions have been layered over the course of evolution.

Statistical structure of locomotion and its modulation by odors

Most behaviors such as making tea are not stereotypical but have an obvious structure. However, analytical methods to objectively extract structure from non-stereotyped behaviors are immature. In this study, we analyze the locomotion of fruit flies and show that this non-stereotyped behavior is well-described by a Hierarchical Hidden Markov Model (HHMM). HHMM shows that a fly's locomotion can be decomposed into a few locomotor features, and odors modulate locomotion by altering the time a fly spends performing different locomotor features. Importantly, although all flies in our dataset use the same set of locomotor features, individual flies vary considerably in how often they employ a given locomotor feature, and how this usage is modulated by odor. This variation is so large that the behavior of individual flies is best understood as being grouped into at least three to five distinct clusters, rather than variations around an average fly.

Children's low-level pesticide exposure and associations with autism and ADHD: a review

Pesticides are chemicals that are designed specifically for the purpose of killing or suppressing another living organism. Human toxicity is possible with any pesticide, and a growing body of literature has investigated possible associations with neurodevelopmental disorders. Attention deficit disorder with or without hyperactivity (ADHD) and autism spectrum disorder (ASD) are two of these specific disorders that have garnered particular interest. Exposure to toxic chemicals during critical windows of brain development is a biologically plausible mechanism. This review describes the basic laboratory science including controlled pesticide dosing experiments in animals that supports a mechanistic relationship in the development of ADHD and/or ASD. Epidemiological relationships are also described for low-level pesticide exposure and ADHD and/or ASD. The available evidence supports the hypothesis that pesticide exposure at levels that do not cause acute toxicity may be among the multifactorial causes of ADHD and ASD, though further study is needed, especially for some of the newer pesticides.

Social foraging extends associative odor-food memory expression in an automated learning assay for Drosophila

Animals socially interact during foraging and share information about the quality and location of food sources. The mechanisms of social information transfer during foraging have been mostly studied at the behavioral level, and its underlying neural mechanisms are largely unknown. Fruit flies have become a model for studying the neural bases of social information transfer, because they provide a large genetic toolbox to monitor and manipulate neuronal activity, and they show a rich repertoire of social behaviors. Fruit flies aggregate, they use social information for choosing a suitable mating partner and oviposition site, and they show better aversive learning when in groups. However, the effects of social interactions on associative odor-food learning have not yet been investigated. Here we present an automated learning and memory assay for walking flies that allows studying the effect of group size on social interactions and on the formation and expression of associative odor-food memories. We found that both inter-fly attraction and the duration of odor-food memory expression increase with group size. We discuss possible behavioral and neural mechanisms of this social effect on odor-food memory expression. This study opens up opportunities to investigate how social interactions during foraging are relayed in the neural circuitry of learning and memory expression.

Sugar Intake Elicits Intelligent Searching Behavior in Flies and Honey Bees

We present a comparison of the sugar-elicited search behavior in Drosophila melanogaster and Apis mellifera. In both species, intake of sugar-water elicits a complex of searching responses. The most obvious response was an increase in turning frequency. However, we also found that flies and honey bees returned to the location of the sugar drop. They even returned to the food location when we prevented them from using visual and chemosensory cues. Analyses of the recorded trajectories indicated that flies and bees use two mechanisms, a locomotor pattern involving an increased turning frequency and path integration to increase the probability to stay close or even return to the sugar drop location. However, evidence for the use of path integration in honey bees was less clear. In general, walking trajectories of honey bees showed a higher degree of curvature and were more spacious; two characters which likely masked evidence for the use of path integration in our experiments. Visual cues, i.e., a black dot, presented underneath the sugar drop made flies and honey bees stay closer to the starting point of the search. In honey bees, vertical black columns close to the sugar drop increased the probability to visit similar cues in the vicinity. An additional one trial learning experiment suggested that the intake of sugar-water likely has the potential to initiate an associative learning process. Together, our experiments indicate that the sugar-elicited local search is more complex than previously assumed. Most importantly, this local search behavior appeared to exhibit major behavioral capabilities of large-scale navigation. Thus, we propose that sugar-elicited search behavior has the potential to become a fruitful behavioral paradigm to identify neural and molecular mechanisms involved in general mechanisms of navigation.

Distinct activity-gated pathways mediate attraction and aversion to CO2 in Drosophila

Carbon dioxide is produced by many organic processes, and is a convenient volatile cue for insects¹ searching for blood hosts², flowers³, communal nests⁴, fruit⁵, and wildfires⁶. Curiously, although Drosophila melanogaster feed on yeast that produce CO₂ and ethanol during fermentation, laboratory experiments suggest that walking flies avoid CO₂^7–12. Here, we resolve this paradox by showing that both flying and walking Drosophila find CO₂ attractive, but only when in an active state associated with foraging. Aversion at low activity levels may be an adaptation to avoid CO₂-seeking-parasites, or succumbing to respiratory acidosis in the presence of high concentrations of CO₂ that exist in nature^13,14. In contrast to CO₂, flies are attracted to ethanol in all behavioral states, and invest twice the time searching near ethanol compared to CO₂. These behavioral differences reflect the fact that whereas CO₂ is generated by many natural processes, ethanol is a unique signature of yeast fermentation. Using genetic tools, we determined that the evolutionarily ancient ionotropic co-receptor IR25a is required for CO₂ attraction, and that the receptors necessary for CO₂ avoidance are not involved. Our study lays the foundation for future research to determine the neural circuits underlying both state- and odorant- dependent decision making in Drosophila.

Neuroarchitecture of the dung beetle central complex

Despite their tiny brains, insects show impressive abilities when navigating over short distances during path integration or during migration over thousands of kilometers across entire continents. Celestial compass cues often play an important role as references during navigation. In contrast to many other insects, South African dung beetles rely exclusively on celestial cues for visual reference during orientation. After finding a dung pile, these animals cut off a piece of dung from the pat, shape it into a ball and roll it away along a straight path until a suitable place for underground consumption is found. To maintain a constant bearing, a brain region in the beetle's brain, called the central complex, is crucially involved in the processing of skylight cues, similar to what has already been shown for path-integrating and migrating insects. In this study, we characterized the neuroanatomy of the sky-compass network and the central complex in the dung beetle brain in detail. Using tracer injections, combined with imaging and 3D modeling, we describe the anatomy of the possible sky-compass network in the central brain. We used a quantitative approach to study the central-complex network and found that several types of neuron exhibit a highly organized connectivity pattern. The architecture of the sky-compass network and central complex is similar to that described in insects that perform path integration or are migratory. This suggests that, despite their different orientation behaviors, this neural circuitry for compass orientation is highly conserved among the insects.

Search and return model for stochastic path integrators

We extend a recently introduced prototypical stochastic model describing uniformly the search and return of objects looking for new food sources around a given home. The model describes the kinematic motion of the object with constant speed in two dimensions. The angular dynamics is driven by noise and describes a "pursuit" and "escape" behavior of the heading and the position vectors. Pursuit behavior ensures the return to the home and the escaping between the two vectors realizes exploration of space in the vicinity of the given home. Noise is originated by environmental influences and during decision making of the object. We take symmetric α -stable noise since such noise is observed in experiments. We now investigate for the simplest possible case, the consequences of limited knowledge of the position angle of the home. We find that both noise type and noise strength can significantly increase the probability of returning to the home. First, we review shortly main findings of the model presented in the former manuscript. These are the stationary distance distribution of the noise driven conservative dynamics and the observation of an optimal noise for finding new food sources. Afterwards, we generalize the model by adding a constant shift γ within the interaction rule between the two vectors. The latter might be created by a permanent uncertainty of the correct home position. Nonvanishing shifts transform the kinematics of the searcher to a dissipative dynamics. For the latter, we discuss the novel deterministic properties and calculate the stationary spatial distribution around the home.

Optimal noise in a stochastic model for local search

We develop a prototypical stochastic model for a local search around a given home. The stochastic dynamic model is motivated by experimental findings of the motion of a fruit fly around a given spot of food but will generally describe the local search behavior. The local search consists of a sequence of two epochs. In the first the searcher explores new space around the home, whereas it returns to the home during the second epoch. In the proposed two-dimensional model both tasks are described by the same stochastic dynamics. The searcher moves with constant speed and its angular dynamics is driven by a symmetric α-stable noise source. The latter stands for the uncertainty to decide the new direction of motion. The main ingredient of the model is the nonlinear interaction dynamics of the searcher with its home. In order to determine the new heading direction, the searcher has to know the actual angles of its position to the home and of the heading vector. A bound state to the home is realized by a permanent switch of a repulsive and attractive forcing of the heading direction from the position direction corresponding to search and return epochs. Our investigation elucidates the analytic tractability of the deterministic and stochastic dynamics. Noise transforms the conservative deterministic dynamics into a dissipative one of the moments. The noise enables a faster finding of a target distinct from the home with optimal intensity. This optimal situation is related to the noise-dependent relaxation time. It is uniquely defined for all α and distinguishes between the stochastic dynamics before and after its value. For times large compared to this, we derive the corresponding Smoluchowski equation and find diffusive spreading of the searcher in the space. We report on the qualitative agreement with the experimentally observed spatial distribution, noisy oscillatory return times, and spatial autocorrelation function of the fruit fly. However, as a result of its simplicity, the model aims to reproduce the local search behavior of other units during their exploration of surrounding space and their quasiperiodic return to a home.

Density-functional fluctuation theory of crowds

A primary goal of collective population behavior studies is to determine the rules governing crowd distributions in order to predict future behaviors in new environments. Current top-down modeling approaches describe, instead of predict, specific emergent behaviors, whereas bottom-up approaches must postulate, instead of directly determine, rules for individual behaviors. Here, we employ classical density functional theory (DFT) to quantify, directly from observations of local crowd density, the rules that predict mass behaviors under new circumstances. To demonstrate our theory-based, data-driven approach, we use a model crowd consisting of walking fruit flies and extract two functions that separately describe spatial and social preferences. The resulting theory accurately predicts experimental fly distributions in new environments and provides quantification of the crowd “mood”. Should this approach generalize beyond milling crowds, it may find powerful applications in fields ranging from spatial ecology and active matter to demography and economics.

Tools from statistical physics can be used to investigate a large variety of fields ranging from economics to biology. Here the authors first adapt density-functional theory to predict the distributions of crowds in new environments and then validate their approach using groups of fruit flies.

Elementary sensory-motor transformations underlying olfactory navigation in walking fruit-flies

Odor attraction in walking Drosophila melanogaster is commonly used to relate neural function to behavior, but the algorithms underlying attraction are unclear. Here, we develop a high-throughput assay to measure olfactory behavior in response to well-controlled sensory stimuli. We show that odor evokes two behaviors: an upwind run during odor (ON response), and a local search at odor offset (OFF response). Wind orientation requires antennal mechanoreceptors, but search is driven solely by odor. Using dynamic odor stimuli, we measure the dependence of these two behaviors on odor intensity and history. Based on these data, we develop a navigation model that recapitulates the behavior of flies in our apparatus, and generates realistic trajectories when run in a turbulent boundary layer plume. The ability to parse olfactory navigation into quantifiable elementary sensori-motor transformations provides a foundation for dissecting neural circuits that govern olfactory behavior.

Building a heading signal from anatomically defined neuron types in the Drosophila central complex

A network of a few hundred neurons in the Drosophila central complex carries an estimate of the fly's heading in the world, akin to the mammalian head-direction system. Here we describe how anatomically defined neuronal classes in this network are poised to implement specific sub-processes for building and updating this population-level heading signal. The computations we describe in the fly central complex strongly resemble those posited to exist in the mammalian brain, in computational models for building head-direction signals. By linking circuit anatomy to navigational physiology, the Drosophila central complex should provide a detailed example of how a heading signal is built.

Satiation state-dependent dopaminergic control of foraging in Drosophila

Hunger evokes stereotypic behaviors that favor the discovery of nutrients. The neural pathways that coordinate internal and external cues to motivate foraging behaviors are only partly known. Drosophila that are food deprived increase locomotor activity, are more efficient in locating a discrete source of nutrition, and are willing to overcome adversity to obtain food. We developed a simple open field assay that allows flies to freely perform multiple steps of the foraging sequence, and we show that two distinct dopaminergic neural circuits regulate measures of foraging behaviors. One group, the PAM neurons, functions in food deprived flies while the other functions in well fed flies, and both promote foraging. These satiation state-dependent circuits converge on dopamine D1 receptor-expressing Kenyon cells of the mushroom body, where neural activity promotes foraging independent of satiation state. These findings provide evidence for active foraging in well-fed flies that is separable from hunger-driven foraging.

The adult foraging assay (AFA) detects strain and food-deprivation effects in feeding-related traits of Drosophila melanogaster

We introduce a high-resolution adult foraging assay (AFA) that relates pre- and post-ingestive walking behavior to individual instances of food consumption. We explore the utility of the AFA by taking advantage of established rover and sitter strains known to differ in a number of feeding-related traits. The AFA allows us to effectively distinguish locomotor behavior in Fed and Food-Deprived (FD) rover and sitter foragers. We found that rovers exhibit more exploratory behavior into the center of an arena containing sucrose drops compared to sitters who hug the edges of the arena and exhibit thigmotaxic behavior. Rovers also discover and ingest more sucrose drops than sitters. Sitters become more exploratory with increasing durations of food deprivation and the number of ingestion events also increases progressively with prolonged fasting for both strains. AFA results are matched by strain differences in sucrose responsiveness, starvation resistance, and lipid levels, suggesting that under the same feeding condition, rovers are more motivated to forage than sitters. These findings demonstrate the AFA's ability to effectively discriminate movement and food ingestion patterns of different strains and feeding treatments.

Adiabatic elimination of inertia of the stochastic microswimmer driven by α-stable noise

We consider a microswimmer that moves in two dimensions at a constant speed and changes the direction of its motion due to a torque consisting of a constant and a fluctuating component. The latter will be modeled by a symmetric Lévy-stable (α-stable) noise. The purpose is to develop a kinetic approach to eliminate the angular component of the dynamics to find a coarse-grained description in the coordinate space. By defining the joint probability density function of the position and of the orientation of the particle through the Fokker-Planck equation, we derive transport equations for the position-dependent marginal density, the particle's mean velocity, and the velocity's variance. At time scales larger than the relaxation time of the torque τ_{ϕ}, the two higher moments follow the marginal density and can be adiabatically eliminated. As a result, a closed equation for the marginal density follows. This equation, which gives a coarse-grained description of the microswimmer's positions at time scales t≫τ_{ϕ}, is a diffusion equation with a constant diffusion coefficient depending on the properties of the noise. Hence, the long-time dynamics of a microswimmer can be described as a normal, diffusive, Brownian motion with Gaussian increments.

The evolution of honey bee dance communication: a mechanistic perspective

Honey bee dance has been intensively studied as a communication system, and yet we still know very little about the neurobiological mechanisms supporting how dances are produced and interpreted. Here, we discuss how new information on the functions of the central complex (CX) of the insect brain might shed some light on possible neural mechanisms of dance behaviour. We summarise the features of dance communication across the species of the genus Apis. We then propose that neural mechanisms of orientation and spatial processing found to be supported by the CX may function in dance communication also, and that this mechanistic link could explain some specific features of the dance form. This is purely a hypothesis, but in proposing this hypothesis, and how it might be investigated, we hope to stimulate new mechanistic analyses of dance communication.

Unraveling the neural basis of insect navigation

One of the defining features of animals is their ability to navigate their environment. Using behavioral experiments this topic has been under intense investigation for nearly a century. In insects, this work has largely focused on the remarkable homing abilities of ants and bees. More recently, the neural basis of navigation shifted into the focus of attention. Starting with revealing the neurons that process the sensory signals used for navigation, in particular polarized skylight, migratory locusts became the key species for delineating navigation-relevant regions of the insect brain. Over the last years, this work was used as a basis for research in the fruit fly Drosophila and extraordinary progress has been made in illuminating the neural underpinnings of navigational processes. With increasingly detailed understanding of navigation circuits, we can begin to ask whether there is a fundamentally shared concept underlying all navigation behavior across insects. This review highlights recent advances and puts them into the context of the behavioral work on ants and bees, as well as the circuits involved in polarized-light processing. A region of the insect brain called the central complex emerges as the common substrate for guiding navigation and its highly organized neuroarchitecture provides a framework for future investigations potentially suited to explain all insect navigation behavior at the level of identified neurons.

Insect Navigation: How Flies Keep Track of Their Snack

A new study provides evidence that fruit flies use path integration to maintain proximity to a food source during their local searches.