Fly Motion Vision

The Role of Landscapes and Landmarks in Bee Navigation: A Review

The ability of animals to explore landmarks in their environment is essential to their fitness. Landmarks are widely recognized to play a key role in navigation by providing information in multiple sensory modalities. However, what is a landmark? We propose that animals use a hierarchy of information based upon its utility and salience when an animal is in a given motivational state. Focusing on honeybees, we suggest that foragers choose landmarks based upon their relative uniqueness, conspicuousness, stability, and context. We also propose that it is useful to distinguish between landmarks that provide sensory input that changes ("near") or does not change ("far") as the receiver uses these landmarks to navigate. However, we recognize that this distinction occurs on a continuum and is not a clear-cut dichotomy. We review the rich literature on landmarks, focusing on recent studies that have illuminated our understanding of the kinds of information that bees use, how they use it, potential mechanisms, and future research directions.

Differential Tuning to Visual Motion Allows Robust Encoding of Optic Flow in the Dragonfly

Visual cues provide an important means for aerial creatures to ascertain their self-motion through the environment. In many insects, including flies, moths, and bees, wide-field motion-sensitive neurons in the third optic ganglion are thought to underlie such motion encoding; however, these neurons can only respond robustly over limited speed ranges. The task is more complicated for some species of dragonflies that switch between extended periods of hovering flight and fast-moving pursuit of prey and conspecifics, requiring motion detection over a broad range of velocities. Since little is known about motion processing in these insects, we performed intracellular recordings from hawking, emerald dragonflies (Hemicordulia spp.) and identified a diverse group of motion-sensitive neurons that we named lobula tangential cells (LTCs). Following prolonged visual stimulation with drifting gratings, we observed significant differences in both temporal and spatial tuning of LTCs. Cluster analysis of these changes confirmed several groups of LTCs with distinctive spatiotemporal tuning. These differences were associated with variation in velocity tuning in response to translated, natural scenes. LTCs with differences in velocity tuning ranges and optima may underlie how a broad range of motion velocities are encoded. In the hawking dragonfly, changes in LTC tuning over time are therefore likely to support their extensive range of behaviors, from hovering to fast-speed pursuits.SIGNIFICANCE STATEMENT Understanding how animals navigate the world is an inherently difficult and interesting problem. Insects are useful models for understanding neuronal mechanisms underlying these activities, with neurons that encode wide-field motion previously identified in insects, such as flies, hawkmoths, and butterflies. Like some Dipteran flies, dragonflies exhibit complex aerobatic behaviors, such as hovering, patrolling, and aerial combat. However, dragonflies lack halteres that support such diverse behavior in flies. To understand how dragonflies might address this problem using only visual cues, we recorded from their wide-field motion-sensitive neurons. We found these differ strongly in the ways they respond to sustained motion, allowing them collectively to encode the very broad range of velocities experienced during diverse behavior.

Feeling fooled: Texture contaminates the neural code for tactile speed

Motion is an essential component of everyday tactile experience: most manual interactions involve relative movement between the skin and objects. Much of the research on the neural basis of tactile motion perception has focused on how direction is encoded, but less is known about how speed is. Perceived speed has been shown to be dependent on surface texture, but previous studies used only coarse textures, which span a restricted range of tangible spatial scales and provide a limited window into tactile coding. To fill this gap, we measured the ability of human observers to report the speed of natural textures—which span the range of tactile experience and engage all the known mechanisms of texture coding—scanned across the skin. In parallel experiments, we recorded the responses of single units in the nerve and in the somatosensory cortex of primates to the same textures scanned at different speeds. We found that the perception of speed is heavily influenced by texture: some textures are systematically perceived as moving faster than are others, and some textures provide a more informative signal about speed than do others. Similarly, the responses of neurons in the nerve and in cortex are strongly dependent on texture. In the nerve, although all fibers exhibit speed-dependent responses, the responses of Pacinian corpuscle–associated (PC) fibers are most strongly modulated by speed and can best account for human judgments. In cortex, approximately half of the neurons exhibit speed-dependent responses, and this subpopulation receives strong input from PC fibers. However, speed judgments seem to reflect an integration of speed-dependent and speed-independent responses such that the latter help to partially compensate for the strong texture dependence of the former.

Our ability to sense the speed at which a surface moves across our skin is highly unreliable and depends on the texture of the surface. This study shows that speed illusions can be predicted from the responses of a specific population of nerve fibers and of their downstream targets; because the skin is too sparsely innervated to compute tactile speed accurately, the nervous system relies on a heuristic to estimate it.

Landing maneuvers of houseflies on vertical and inverted surfaces

Landing maneuvers of flies are complex behaviors which can be conceptually decomposed into sequences of modular actions, including body-deceleration, leg-extension, and body rotations. These behavioral ‘modules’ must be coordinated to ensure well-controlled landing. The composite nature of these behaviors induces kinematic variability, making it difficult to identify the central rules that govern landing. Many previous studies have relied on tethered preparations to study landing behaviors, but tethering induces experimental artefacts by forcing some behaviors to operate in open-feedback control loop while others remain closed-loop. On the other hand, it is harder for the experimenter to control the stimuli experienced by freely-flying insects. One approach towards understanding general mechanisms of landing is to determine the common elements of their kinematics on surfaces of different orientations. We conducted a series of experiments in which the houseflies, Musca domestica, were lured to land on vertical (wall landings) or inverted (ceiling landings) substrates, while their flight was recorded with multiple high-speed cameras. We observed that, in both cases, well-controlled landings occurred when the distance at which flies initiated deceleration was proportional to flight velocity component in the direction of substrate. The ratio of substrate distance and velocity at onset of deceleration (tau) was conserved, despite substantial differences in mechanics of vertical vs. ceiling landings. When these conditions were not satisfied, their landing performance was compromised, causing their heads to collide into the substrate. Unlike body-deceleration, leg-extension in flies was independent of substrate distance or approach velocity. Thus, the robust reflexive visual initiation of deceleration is independent of substrate orientation, and combines with a more variable initiation of leg-extension which depends on surface orientation. Together, these combinations of behaviors enable flies to land in a versatile manner on substrates of various orientations.

Mechanical spectroscopy of insect swarms

Social animals routinely form groups, which are thought to display emergent, collective behavior. This hypothesis suggests that animal groups should have properties at the group scale that are not directly linked to the individuals, much as bulk materials have properties distinct from those of their constituent atoms. Materials are often probed by measuring their response to controlled perturbations, but these experiments are difficult to conduct on animal groups, particularly in the wild. Here, we show that laboratory midge swarms have emergent continuum mechanical properties, displaying a collective viscoelastic response to applied oscillatory visual stimuli that allows us to extract storage and loss moduli for the swarm. We find that the swarms strongly damp perturbations, both viscously and inertially. Thus, unlike bird flocks, which appear to use collective behavior to promote lossless information flow through the group, our results suggest that midge swarms use it to stabilize themselves against environmental perturbations.

Novel Strategies for the Generation of Neuronal Diversity: Lessons From the Fly Visual System

Among all organs of an adult animal, the central nervous system stands out because of its vast complexity and morphological diversity. During early development, the entire central nervous system develops from an apparently homogenous group of progenitors that differentiate into all neural cell types. Therefore, understanding the molecular and genetic mechanisms that give rise to the cellular and anatomical diversity of the brain is a key goal of the developmental neurobiology field. With this aim in mind, the development of the central nervous system of model organisms has been extensively studied. From more than a century, the mechanisms of neurogenesis have been studied in the fruit fly Drosophila melanogaster. The visual system comprises one of the major structures of the Drosophila brain. The visual information is collected by the eye-retina photoreceptors and then processed by the four optic lobe ganglia: the lamina, medulla, lobula and lobula plate. The molecular mechanisms that originate neuronal diversity in the optic lobe have been unveiled in the past decade. In this article, we describe the early development and differentiation of the lobula plate ganglion, from the formation of the optic placode and the inner proliferation center to the specification of motion detection neurons. We focused specifically on how the precise combination of signaling pathways and cell-specific transcription factors patterns the pool of neural stem cells that generates the different neurons of the motion detection system.

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.

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.

The Neuronal Basis of an Illusory Motion Percept Is Explained by Decorrelation of Parallel Motion Pathways

Both vertebrates and invertebrates perceive illusory motion, known as "reverse-phi," in visual stimuli that contain sequential luminance increments and decrements. However, increment (ON) and decrement (OFF) signals are initially processed by separate visual neurons, and parallel elementary motion detectors downstream respond selectively to the motion of light or dark edges, often termed ON- and OFF-edges. It remains unknown how and where ON and OFF signals combine to generate reverse-phi motion signals. Here, we show that each of Drosophila's elementary motion detectors encodes motion by combining both ON and OFF signals. Their pattern of responses reflects combinations of increments and decrements that co-occur in natural motion, serving to decorrelate their outputs. These results suggest that the general principle of signal decorrelation drives the functional specialization of parallel motion detection channels, including their selectivity for moving light or dark edges.

Blowfly yaw control via electrical stimulation of the H1 lobula plate tangential cell

Eliciting predictable flight responses in insects via exogenous stimulation of the nervous system is an area of both scientific and engineering interest. Blowflies in particular possess an excellent biological flight control system, making them an ideal system for characterising responses to stimulation. Here we demonstrate a means of electrically controlling Calliphoridae-Protophormia terranovae wing behaviour, generating a repeatable yaw response via biphasic electrical stimulation of the H1 lobula plate tangential cell (LPTC). We found that a 10 mA current pulse at a frequency of 30-270 Hz produces a yaw response in the preferred direction of H1 in a tethered blowfly preparation, and the magnitude of the yaw response is proportional to the frequency of the stimulus. This result suggests that these LPTCs, which encode optic flow, may be a viable interface for controlling fly flight behaviour. This platform could find application not only for micro air vehicles (MAVs), but also in developing flight models or for studying neurological control of fly flight behaviour.

The transcription factor SoxD controls neuronal guidance in the Drosophila visual system

Precise control of neurite guidance during development is essential to ensure proper formation of neuronal networks and correct function of the central nervous system (CNS). How neuronal projections find their targets to generate appropriate synapses is not entirely understood. Although transcription factors are key molecules during neurogenesis, we do not know their entire function during the formation of networks in the CNS. Here, we used the Drosophila melanogaster optic lobe as a model for understanding neurite guidance during development. We assessed the function of Sox102F/SoxD, the unique Drosophila orthologue of the vertebrate SoxD family of transcription factors. SoxD is expressed in immature and mature neurons in the larval and adult lobula plate ganglia (one of the optic lobe neuropils), but is absent from glial cells, neural stem cells and progenitors of the lobula plate. SoxD RNAi knockdown in all neurons results in a reduction of the lobula plate neuropil, without affecting neuronal fate. This morphological defect is associated with an impaired optomotor response of adult flies. Moreover, knocking down SoxD only in T4/T5 neuronal types, which control motion vision, affects proper neurite guidance into the medulla and lobula. Our findings suggest that SoxD regulates neurite guidance, without affecting neuronal fate.

Apparent Motion Perception in the Praying Mantis: Psychophysics and Modelling

Simple Summary

Computer monitors, smart phone screens, and other forms of digital displays present a series of still images (frames) in which objects are displaced in small steps, tricking us into perceiving smooth motion. This illusion is referred to as “apparent motion”. For motion to be perceived, the magnitude of each displacement step must be smaller than a certain limit, referred to as Dmax. Previous studies have investigated the relationship between this limit and object size in humans and found that the maximum displacement is larger for larger objects than for smaller ones. In this work, we investigated the same relationship in the praying mantis Sphodromantis lineola by presenting them with moving random chequerboard patterns on a computer monitor. Even though motion perception in humans and insects are believed to be explained equally well by the same underlying model, we found that Dmax scales differently with object size in mantids. These results suggest that there may be qualitative differences in how mantids perceive apparent motion compared to humans.

Abstract

Apparent motion is the perception of motion created by rapidly presenting still frames in which objects are displaced in space. Observers can reliably discriminate the direction of apparent motion when inter-frame object displacement is below a certain limit, Dmax. Earlier studies of motion perception in humans found that Dmax is lower-bounded at around 15 arcmin, and thereafter scales with the size of the spatial elements in the images. Here, we run corresponding experiments in the praying mantis Sphodromantis lineola to investigate how Dmax scales with the element size. We use random moving chequerboard patterns of varying element and displacement step sizes to elicit the optomotor response, a postural stabilization mechanism that causes mantids to lean in the direction of large-field motion. Subsequently, we calculate Dmax as the displacement step size corresponding to a 50% probability of detecting an optomotor response in the same direction as the stimulus. Our main findings are that the mantis Dmax scales roughly as a square-root of element size and that, in contrast to humans, it is not lower-bounded. We present two models to explain these observations: a simple high-level model based on motion energy in the Fourier domain and a more-detailed one based on the Reichardt Detector. The models present complementary intuitive and physiologically-realistic accounts of how Dmax scales with the element size in insects. We conclude that insect motion perception is limited by only a single stage of spatial filtering, reflecting the optics of the compound eye. In contrast, human motion perception reflects a second stage of spatial filtering, at coarser scales than imposed by human optics, likely corresponding to the magnocellular pathway. After this spatial filtering, mantis and human motion perception and Dmax are qualitatively very similar.

Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation

Shaping the collision selectivity in vision-based artificial collision-detecting systems is still an open challenge. This paper presents a novel neuron model of a locust looming detector, i.e. the lobula giant movement detector (LGMD1), in order to provide effective solutions to enhance the collision selectivity of looming objects over other visual challenges. We propose an approach to model the biologically plausible mechanisms of ON and OFF pathways and a biophysical mechanism of spike frequency adaptation (SFA) in the proposed LGMD1 visual neural network. The ON and OFF pathways can separate both dark and light looming features for parallel spatiotemporal computations. This works effectively on perceiving a potential collision from dark or light objects that approach; such a bio-plausible structure can also separate LGMD1's collision selectivity to its neighbouring looming detector - the LGMD2. The SFA mechanism can enhance the LGMD1's collision selectivity to approaching objects rather than receding and translating stimuli, which is a significant improvement compared with similar LGMD1 neuron models. The proposed framework has been tested using off-line tests of synthetic and real-world stimuli, as well as on-line bio-robotic tests. The enhanced collision selectivity of the proposed model has been validated in systematic experiments. The computational simplicity and robustness of this work have also been verified by the bio-robotic tests, which demonstrates potential in building neuromorphic sensors for collision detection in both a fast and reliable manner.

The functional organization of descending sensory-motor pathways in Drosophila

In most animals, the brain controls the body via a set of descending neurons (DNs) that traverse the neck. DN activity activates, maintains or modulates locomotion and other behaviors. Individual DNs have been well-studied in species from insects to primates, but little is known about overall connectivity patterns across the DN population. We systematically investigated DN anatomy in Drosophila melanogaster and created over 100 transgenic lines targeting individual cell types. We identified roughly half of all Drosophila DNs and comprehensively map connectivity between sensory and motor neuropils in the brain and nerve cord, respectively. We find the nerve cord is a layered system of neuropils reflecting the fly's capability for two largely independent means of locomotion -- walking and flight -- using distinct sets of appendages. Our results reveal the basic functional map of descending pathways in flies and provide tools for systematic interrogation of neural circuits.

Non-uniform weighting of local motion inputs underlies dendritic computation in the fly visual system

The fly visual system offers a unique opportunity to explore computations performed by single neurons. Two previous studies characterized, in vivo, the receptive field (RF) of the vertical system (VS) cells of the blowfly (calliphora vicina), both intracellularly in the axon, and, independently using Ca²⁺ imaging, in hundreds of distal dendritic branchlets. We integrated this information into detailed passive cable and compartmental models of 3D reconstructed VS cells. Within a given VS cell type, the transfer resistance (TR) from different branchlets to the axon differs substantially, suggesting that they contribute unequally to the shaping of the axonal RF. Weighting the local RFs of all dendritic branchlets by their respective TR yielded a faithful reproduction of the axonal RF. The model also predicted that the various dendritic branchlets are electrically decoupled from each other, thus acting as independent local functional subunits. The study suggests that single neurons in the fly visual system filter dendritic noise and compute the weighted average of their inputs.

Representation of the stomatopod's retinal midband in the optic lobes: Putative neural substrates for integrating chromatic, achromatic and polarization information

Stomatopods have an elaborate visual system served by a retina that is unique to this class of pancrustaceans. Its upper and lower eye hemispheres encode luminance and linear polarization while an equatorial band of photoreceptors termed the midband detects color, circularly polarized light and linear polarization in the ultraviolet. In common with many malacostracan crustaceans, stomatopods have stalked eyes, but they can move these independently within three degrees of rotational freedom. Both eyes separately use saccadic and scanning movements but they can also move in a coordinated fashion to track selected targets or maintain a forward eyestalk posture during swimming. Visual information is initially processed in the first two optic neuropils, the lamina and the medulla, where the eye's midband is represented by enlarged regions within each neuropil that contain populations of neurons, the axons of which are segregated from the neuropil regions subtending the hemispheres. Neuronal channels representing the midband extend from the medulla to the lobula where populations of putative inhibitory glutamic acid decarboxylase-positive neurons and tyrosine hydroxylase-positive neurons intrinsic to the lobula have specific associations with the midband. Here we investigate the organization of the midband representation in the medulla and the lobula in the context of their overall architecture. We discuss the implications of observed arrangements, in which midband inputs to the lobula send out collaterals that extend across the retinotopic mosaic pertaining to the hemispheres. This organization suggests an integrative design that diverges from the eumalacostracan ground pattern and, for the stomatopod, enables color and polarization information to be integrated with luminance information that presumably encodes shape and motion.

Parameters of motion vision in low-light in the hawkmoth, Manduca sexta

The hawkmoth Manduca sexta, is nocturnally active, beginning its flight activity at sunset, and executing rapid controlled maneuvers to search for food and mates in dim light conditions. This moth's visual system has been shown to trade off spatial and temporal resolution for increased sensitivity in these conditions. The study presented here uses tethered flying moths to characterize the flight performance envelope of M. sexta's wide-field-motion-triggered steering response in low light conditions by measuring attempted turning in response to wide-field visual motion. Moths were challenged with a horizontally oscillating sinusoidal grating at a range of luminance, from daylight to starlight conditions. The impact of luminance on response to a range of temporal frequencies and spatial wavelengths was assessed across a range of pattern contrasts. The optomotor response decreased as a function of decreasing luminance, and the lower limit of the moth's contrast sensitivity was found to be between 1% to 5%. The preferred spatial frequency for M. sexta increased from 0.06 to 0.3 cycles/degree as the luminance decreased, but the preferred temporal frequency remained stable at 4.5 Hz across all conditions. The relationship between the optomotor response time to the temporal frequency of the pattern movement did not vary significantly with luminance levels. Taken together, these results suggest that the behavioral response to wide-field visual input in M. sexta is adapted to operate during crepuscular to nocturnal luminance levels, and the decreasing light levels experienced during that period changes visual acuity and does not affect their response time significantly.

Head movements quadruple the range of speeds encoded by the insect motion vision system in hawkmoths

Flying insects use compensatory head movements to stabilize gaze. Like other optokinetic responses, these movements can reduce image displacement, motion and misalignment, and simplify the optic flow field. Because gaze is imperfectly stabilized in insects, we hypothesized that compensatory head movements serve to extend the range of velocities of self-motion that the visual system encodes. We tested this by measuring head movements in hawkmoths Hyles lineata responding to full-field visual stimuli of differing oscillation amplitudes, oscillation frequencies and spatial frequencies. We used frequency-domain system identification techniques to characterize the head's roll response, and simulated how this would have affected the output of the motion vision system, modelled as a computational array of Reichardt detectors. The moths' head movements were modulated to allow encoding of both fast and slow self-motion, effectively quadrupling the working range of the visual system for flight control. By using its own output to drive compensatory head movements, the motion vision system thereby works as an adaptive sensor, which will be especially beneficial in nocturnal species with inherently slow vision. Studies of the ecology of motion vision must therefore consider the tuning of motion-sensitive interneurons in the context of the closed-loop systems in which they function.

An Anatomically Constrained Model for Path Integration in the Bee Brain

Path integration is a widespread navigational strategy in which directional changes and distance covered are continuously integrated on an outward journey, enabling a straight-line return to home. Bees use vision for this task-a celestial-cue-based visual compass and an optic-flow-based visual odometer-but the underlying neural integration mechanisms are unknown. Using intracellular electrophysiology, we show that polarized-light-based compass neurons and optic-flow-based speed-encoding neurons converge in the central complex of the bee brain, and through block-face electron microscopy, we identify potential integrator cells. Based on plausible output targets for these cells, we propose a complete circuit for path integration and steering in the central complex, with anatomically identified neurons suggested for each processing step. The resulting model circuit is thus fully constrained biologically and provides a functional interpretation for many previously unexplained architectural features of the central complex. Moreover, we show that the receptive fields of the newly discovered speed neurons can support path integration for the holonomic motion (i.e., a ground velocity that is not precisely aligned with body orientation) typical of bee flight, a feature not captured in any previously proposed model of path integration. In a broader context, the model circuit presented provides a general mechanism for producing steering signals by comparing current and desired headings-suggesting a more basic function for central complex connectivity, from which path integration may have evolved.

A crustacean lobula plate: Morphology, connections, and retinotopic organization

The lobula plate is part of the lobula complex, the third optic neuropil, in the optic lobes of insects. It has been extensively studied in dipterous insects, where its role in processing flow-field motion information used for controlling optomotor responses was discovered early. Recently, a lobula plate was also found in malacostracan crustaceans. Here, we provide the first detailed description of the neuroarchitecture, the input and output connections and the retinotopic organization of the lobula plate in a crustacean, the crab Neohelice granulata using a variety of histological methods that include silver reduced staining and mass staining with dextran-conjugated dyes. The lobula plate of this crab is a small elongated neuropil. It receives separated retinotopic inputs from columnar neurons of the medulla and the lobula. In the anteroposterior plane, the neuropil possesses four layers defined by the arborizations of such columnar inputs. Medulla projecting neurons arborize mainly in two of these layers, one on each side, while input neurons arriving from the lobula branch only in one. The neuropil contains at least two classes of tangential elements, one connecting with the lateral protocerebrum and the other that exits the optic lobes toward the supraesophageal ganglion. The number of layers in the crab's lobula plate, the retinotopic connections received from the medulla and from the lobula, and the presence of large tangential neurons exiting the neuropil, reflect the general structure of the insect lobula plate and, hence, provide support to the notion of an evolutionary conserved function for this neuropil.

The comprehensive connectome of a neural substrate for 'ON' motion detection in Drosophila

Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in vision, how the eye detects image motion, has long been analysed using Hassenstein-Reichardt (HR) detector or Barlow-Levick (BL) models. These both simulate motion detection well, but the exact neuronal circuits undertaking these tasks remain elusive. We reconstructed a comprehensive connectome of the circuits of Drosophila's motion-sensing T4 cells using a novel EM technique. We uncover complex T4 inputs and reveal that putative excitatory inputs cluster at T4's dendrite shafts, while inhibitory inputs localize to the bases. Consistent with our previous study, we reveal that Mi1 and Tm3 cells provide most synaptic contacts onto T4. We are, however, unable to reproduce the spatial offset between these cells reported previously. Our comprehensive connectome reveals complex circuits that include candidate anatomical substrates for both HR and BL types of motion detectors.

Anatomy of the lobula complex in the brain of the praying mantis compared to the lobula complexes of the locust and cockroach

The praying mantis is an insect which relies on vision for capturing prey, avoiding being eaten and for spatial orientation. It is well known for its ability to use stereopsis for estimating the distance of objects. The neuronal substrate mediating visually driven behaviors, however, is not very well investigated. To provide a basis for future functional studies, we analyzed the anatomical organization of visual neuropils in the brain of the praying mantis Hierodula membranacea and provide supporting evidence from a second species, Rhombodera basalis, with particular focus on the lobula complex (LOX). Neuropils were three‐dimensionally reconstructed from synapsin‐immunostained whole mount brains. The neuropil organization and the pattern of γ‐aminobutyric acid immunostaining of the medulla and LOX were compared between the praying mantis and two related polyneopteran species, the Madeira cockroach and the desert locust. The investigated visual neuropils of the praying mantis are highly structured. Unlike in most insects the LOX of the praying mantis consists of five nested neuropils with at least one neuropil not present in the cockroach or locust. Overall, the mantis LOX is more similar to the LOX of the locust than the more closely related cockroach suggesting that the sensory ecology plays a stronger role than the phylogenetic distance of the three species in structuring this center of visual information processing.

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response

Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data.

The glia of the adult Drosophila nervous system

Glia play crucial roles in the development and homeostasis of the nervous system. While the GLIA in the Drosophila embryo have been well characterized, their study in the adult nervous system has been limited. Here, we present a detailed description of the glia in the adult nervous system, based on the analysis of some 500 glial drivers we identified within a collection of synthetic GAL4 lines. We find that glia make up ∼10% of the cells in the nervous system and envelop all compartments of neurons (soma, dendrites, axons) as well as the nervous system as a whole. Our morphological analysis suggests a set of simple rules governing the morphogenesis of glia and their interactions with other cells. All glial subtypes minimize contact with their glial neighbors but maximize their contact with neurons and adapt their macromorphology and micromorphology to the neuronal entities they envelop. Finally, glial cells show no obvious spatial organization or registration with neuronal entities. Our detailed description of all glial subtypes and their regional specializations, together with the powerful genetic toolkit we provide, will facilitate the functional analysis of glia in the mature nervous system. GLIA 2017 GLIA 2017;65:606–638

Neural control and precision of flight muscle activation in Drosophila

Precision of motor commands is highly relevant in a large context of various locomotor behaviors, including stabilization of body posture, heading control and directed escape responses. While posture stability and heading control in walking and swimming animals benefit from high friction via ground reaction forces and elevated viscosity of water, respectively, flying animals have to cope with comparatively little aerodynamic friction on body and wings. Although low frictional damping in flight is the key to the extraordinary aerial performance and agility of flying birds, bats and insects, it challenges these animals with extraordinary demands on sensory integration and motor precision. Our review focuses on the dynamic precision with which Drosophila activates its flight muscular system during maneuvering flight, considering relevant studies on neural and muscular mechanisms of thoracic propulsion. In particular, we tackle the precision with which flies adjust power output of asynchronous power muscles and synchronous flight control muscles by monitoring muscle calcium and spike timing within the stroke cycle. A substantial proportion of the review is engaged in the significance of visual and proprioceptive feedback loops for wing motion control including sensory integration at the cellular level. We highlight that sensory feedback is the basis for precise heading control and body stability in flies.

Visual projection neurons in the Drosophila lobula link feature detection to distinct behavioral programs

Visual projection neurons (VPNs) provide an anatomical connection between early visual processing and higher brain regions. Here we characterize lobula columnar (LC) cells, a class of Drosophila VPNs that project to distinct central brain structures called optic glomeruli. We anatomically describe 22 different LC types and show that, for several types, optogenetic activation in freely moving flies evokes specific behaviors. The activation phenotypes of two LC types closely resemble natural avoidance behaviors triggered by a visual loom. In vivo two-photon calcium imaging reveals that these LC types respond to looming stimuli, while another type does not, but instead responds to the motion of a small object. Activation of LC neurons on only one side of the brain can result in attractive or aversive turning behaviors depending on the cell type. Our results indicate that LC neurons convey information on the presence and location of visual features relevant for specific behaviors.

Fly Stampede 2.0: A Next Generation Optomotor Assay for Walking Behavior in Drosophila Melanogaster

Optomotor behavior represents a stereotyped locomotor response to visual motion that is found in both vertebrate and invertebrate models. The Fly Stampede assay was developed to study an optomotor response in freely walking populations of Drosophila. Here we share optimized assay designs and software for production of a modified stampede assay that can be used for genetic screens, and improved tracking outputs for understanding behavioral parameters of visual-motion responses and arousal state of individual animals. Arousal state influences behavioral performance in the stampede assay. As proof of principle experiments we show parametric modulation of visual stimuli and startle stimuli in both wildtype and mutant flies for the type I family dopamine receptor Dop1R1 (DopR). DopR mutants are hyperactive and perform poorly in the stampede assay, suggesting a potential role in visual perception and/or arousal. The stampede assay creates an efficient platform for rapid screening of mutant animals or circuit manipulations for investigating attentional processes in Drosophila.

A faithful internal representation of walking movements in the Drosophila visual system

The integration of sensorimotor signals to internally estimate self-movement is critical for spatial perception and motor control. However, which neural circuits accurately track body motion and how these circuits control movement remain unknown. We found that a population of Drosophila neurons that were sensitive to visual flow patterns typically generated during locomotion, the horizontal system (HS) cells, encoded unambiguous quantitative information about the fly's walking behavior independently of vision. Angular and translational velocity signals were integrated with a behavioral-state signal and generated direction-selective and speed-sensitive graded changes in the membrane potential of these non-spiking cells. The nonvisual direction selectivity of HS cells cooperated with their visual selectivity only when the visual input matched that expected from the fly's movements, thereby revealing a circuit for internally monitoring voluntary walking. Furthermore, given that HS cells promoted leg-based turning, the activity of these cells could be used to control forward walking.

Peripheral Processing Facilitates Optic Flow-Based Depth Perception

Flying insects, such as flies or bees, rely on consistent information regarding the depth structure of the environment when performing their flight maneuvers in cluttered natural environments. These behaviors include avoiding collisions, approaching targets or spatial navigation. Insects are thought to obtain depth information visually from the retinal image displacements ("optic flow") during translational ego-motion. Optic flow in the insect visual system is processed by a mechanism that can be modeled by correlation-type elementary motion detectors (EMDs). However, it is still an open question how spatial information can be extracted reliably from the responses of the highly contrast- and pattern-dependent EMD responses, especially if the vast range of light intensities encountered in natural environments is taken into account. This question will be addressed here by systematically modeling the peripheral visual system of flies, including various adaptive mechanisms. Different model variants of the peripheral visual system were stimulated with image sequences that mimic the panoramic visual input during translational ego-motion in various natural environments, and the resulting peripheral signals were fed into an array of EMDs. We characterized the influence of each peripheral computational unit on the representation of spatial information in the EMD responses. Our model simulations reveal that information about the overall light level needs to be eliminated from the EMD input as is accomplished under light-adapted conditions in the insect peripheral visual system. The response characteristics of large monopolar cells (LMCs) resemble that of a band-pass filter, which reduces the contrast dependency of EMDs strongly, effectively enhancing the representation of the nearness of objects and, especially, of their contours. We furthermore show that local brightness adaptation of photoreceptors allows for spatial vision under a wide range of dynamic light conditions.

In Vivo Imaging Reveals Composite Coding for Diagonal Motion in the Drosophila Visual System

Understanding information coding is important for resolving the functions of visual neural circuits. The motion vision system is a classic model for studying information coding as it contains a concise and complete information-processing circuit. In Drosophila, the axon terminals of motion-detection neurons (T4 and T5) project to the lobula plate, which comprises four regions that respond to the four cardinal directions of motion. The lobula plate thus represents a topographic map on a transverse plane. This enables us to study the coding of diagonal motion by investigating its response pattern. By using in vivo two-photon calcium imaging, we found that the axon terminals of T4 and T5 cells in the lobula plate were activated during diagonal motion. Further experiments showed that the response to diagonal motion is distributed over the following two regions compared to the cardinal directions of motion—a diagonal motion selective response region and a non-selective response region—which overlap with the response regions of the two vector-correlated cardinal directions of motion. Interestingly, the sizes of the non-selective response regions are linearly correlated with the angle of the diagonal motion. These results revealed that the Drosophila visual system employs a composite coding for diagonal motion that includes both independent coding and vector decomposition coding.

Direction Selectivity in Drosophila Emerges from Preferred-Direction Enhancement and Null-Direction Suppression

Across animal phyla, motion vision relies on neurons that respond preferentially to stimuli moving in one, preferred direction over the opposite, null direction. In the elementary motion detector of Drosophila, direction selectivity emerges in two neuron types, T4 and T5, but the computational algorithm underlying this selectivity remains unknown. We find that the receptive fields of both T4 and T5 exhibit spatiotemporally offset light-preferring and dark-preferring subfields, each obliquely oriented in spacetime. In a linear-nonlinear modeling framework, the spatiotemporal organization of the T5 receptive field predicts the activity of T5 in response to motion stimuli. These findings demonstrate that direction selectivity emerges from the enhancement of responses to motion in the preferred direction, as well as the suppression of responses to motion in the null direction. Thus, remarkably, T5 incorporates the essential algorithmic strategies used by the Hassenstein-Reichardt correlator and the Barlow-Levick detector. Our model for T5 also provides an algorithmic explanation for the selectivity of T5 for moving dark edges: our model captures all two- and three-point spacetime correlations relevant to motion in this stimulus class. More broadly, our findings reveal the contribution of input pathway visual processing, specifically center-surround, temporally biphasic receptive fields, to the generation of direction selectivity in T5. As the spatiotemporal receptive field of T5 in Drosophila is common to the simple cell in vertebrate visual cortex, our stimulus-response model of T5 will inform efforts in an experimentally tractable context to identify more detailed, mechanistic models of a prevalent computation.

SIGNIFICANCE STATEMENT

Feature selective neurons respond preferentially to astonishingly specific stimuli, providing the neurobiological basis for perception. Direction selectivity serves as a paradigmatic model of feature selectivity that has been examined in many species. While insect elementary motion detectors have served as premiere experimental models of direction selectivity for 60 years, the central question of their underlying algorithm remains unanswered. Using in vivo two-photon imaging of intracellular calcium signals, we measure the receptive fields of the first direction-selective cells in the Drosophila visual system, and define the algorithm used to compute the direction of motion. Computational modeling of these receptive fields predicts responses to motion and reveals how this circuit efficiently captures many useful correlations intrinsic to moving dark edges.

Mechanisms underpinning aggregation and collective movement by insect groups

Collective movement behaviours range from temporary aggregations to coordinated mass migrations with synchronous movement. Models show how complex collective patterns can arise from simple rules regulating local interactions between individuals. However, it is not always clear how these rules are implemented by real insects: even simple abstract rules contain non-trivial assumptions about the perceptual abilities of individuals. Here we review the underlying mechanisms of simple forms of aggregation and collective movement focussing on locusts using them as an example of system in which analyses of the underlying molecular and neural mechanisms have become feasible. These and similar studies promise to reveal the physiological rules governing these behaviours.

Neural mechanisms of insect navigation

We know more about the ethology of insect navigation than the neural substrates. Few studies have shown direct effects of brain manipulation on navigational behaviour; or measure brain responses that clearly relate to the animal's current location or spatial target, independently of specific sensory cues. This is partly due to the methodological problems of obtaining neural data in a naturally behaving animal. However, substantial indirect evidence, such as comparative anatomy and knowledge of the neural circuits that provide relevant sensory inputs provide converging arguments for the role of some specific brain areas: the mushroom bodies; and the central complex. Finally, modelling can help bridge the gap by relating the computational requirements of a given navigational task to the type of computation offered by different brain areas.

A Motion Detection Algorithm Using Local Phase Information

Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using only local phase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector; it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm.

Classification of Parkinson's Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision

Electrophysiological studies indicate altered contrast processing in some Parkinson's Disease (PD) patients. We recently demonstrated that vision is altered in Drosophila PD models and hypothesised that different types of genetic and idiopathic PD may affect dopaminergic visual signalling pathways differently. Here we asked whether visual responses in Drosophila could be used to identify PD mutations. To mimic a clinical setting a range of flies was used. Young flies from four control lines were compared to three early-onset PD mutations (PINK1, DJ-1α and DJ-1β), and to two other neurodegenerative mutations, one in the fly LRRK2 orthologue (dLRRK) the other in eggroll, a model of general neurodegeneration in Drosophila. Stimuli were contrast reversing gratings spanning 64 spatiotemporal frequency combinations. We recorded the steady-state visually-evoked response amplitude across all combinations. We found that the pattern of neuronal responses differed between genotypes. Wild-type and early-onset PD flies formed separate clusters; the late-onset mutation is an outlier. Neuronal responses in early-onset PD flies were stronger than in wild-types. Multivariate pattern analysis grouped flies by PD/non-PD genotype with an accuracy >85%. We propose that machine learning algorithms may be useful in increasing the diagnostic specificity of human electrophysiological measurements in both animal models and PD patients.

High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor

Genetically encoded voltage indicators (GEVIs) are a promising technology for fluorescence readout of millisecond-scale neuronal dynamics. Previous GEVIs had insufficient signaling speed and dynamic range to resolve action potentials in live animals. We coupled fast voltage-sensing domains from a rhodopsin protein to bright fluorophores through resonance energy transfer. The resulting GEVIs are sufficiently bright and fast to report neuronal action potentials and membrane voltage dynamics in awake mice and flies, resolving fast spike trains with 0.2-millisecond timing precision at spike detection error rates orders of magnitude better than previous GEVIs. In vivo imaging revealed sensory-evoked responses, including somatic spiking, dendritic dynamics, and intracellular voltage propagation. These results empower in vivo optical studies of neuronal electrophysiology and coding and motivate further advancements in high-speed microscopy.

Synaptic circuits and their variations within different columns in the visual system of Drosophila

We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly's compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla's neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall, <1% of contacts are not part of a consensus circuit, and we classify those contacts that supplement (E+) or are missing from it (E-). Autapses, in which the same cell is both presynaptic and postsynaptic at the same synapse, are occasionally seen; two cells in particular, Dm9 and Mi1, form ≥ 20-fold more autapses than do other neurons. These results delimit the accuracy of developmental events that establish and normally maintain synaptic circuits with such precision, and thereby address the operation of such circuits. They also establish a precedent for error rates that will be required in the new science of connectomics.

Neural Circuit to Integrate Opposing Motions in the Visual Field

When navigating in their environment, animals use visual motion cues as feedback signals that are elicited by their own motion. Such signals are provided by wide-field neurons sampling motion directions at multiple image points as the animal maneuvers. Each one of these neurons responds selectively to a specific optic flow-field representing the spatial distribution of motion vectors on the retina. Here, we describe the discovery of a group of local, inhibitory interneurons in the fruit fly Drosophila key for filtering these cues. Using anatomy, molecular characterization, activity manipulation, and physiological recordings, we demonstrate that these interneurons convey direction-selective inhibition to wide-field neurons with opposite preferred direction and provide evidence for how their connectivity enables the computation required for integrating opposing motions. Our results indicate that, rather than sharpening directional selectivity per se, these circuit elements reduce noise by eliminating non-specific responses to complex visual information.

A higher order visual neuron tuned to the spatial amplitude spectra of natural scenes

Animal sensory systems are optimally adapted to those features typically encountered in natural surrounds, thus allowing neurons with limited bandwidth to encode challengingly large input ranges. Natural scenes are not random, and peripheral visual systems in vertebrates and insects have evolved to respond efficiently to their typical spatial statistics. The mammalian visual cortex is also tuned to natural spatial statistics, but less is known about coding in higher order neurons in insects. To redress this we here record intracellularly from a higher order visual neuron in the hoverfly. We show that the cSIFE neuron, which is inhibited by stationary images, is maximally inhibited when the slope constant of the amplitude spectrum is close to the mean in natural scenes. The behavioural optomotor response is also strongest to images with naturalistic image statistics. Our results thus reveal a close coupling between the inherent statistics of natural scenes and higher order visual processing in insects.

Adaptation-induced modification of motion selectivity tuning in visual tectal neurons of adult zebrafish

In the developing brain, training-induced emergence of direction selectivity and plasticity of orientation tuning appear to be widespread phenomena. These are found in the visual pathway across different classes of vertebrates. Moreover, short-term plasticity of orientation tuning in the adult brain has been demonstrated in several species of mammals. However, it is unclear whether neuronal orientation and direction selectivity in nonmammalian species remains modifiable through short-term plasticity in the fully developed brain. To address this question, we analyzed motion tuning of neurons in the optic tectum of adult zebrafish by calcium imaging. In total, orientation and direction selectivity was enhanced by adaptation, responses of previously orientation-selective neurons were sharpened, and even adaptation-induced emergence of selectivity in previously nonselective neurons was observed in some cases. The different observed effects are mainly based on the relative distance between the previously preferred and the adaptation direction. In those neurons in which a shift of the preferred orientation or direction was induced by adaptation, repulsive shifts (i.e., away from the adapter) were more prevalent than attractive shifts. A further novel finding for visually induced adaptation that emerged from our study was that repulsive and attractive shifts can occur within one brain area, even with uniform stimuli. The type of shift being induced also depends on the difference between the adapting and the initially preferred stimulus direction. Our data indicate that, even within the fully developed optic tectum, short-term plasticity might have an important role in adjusting neuronal tuning functions to current stimulus conditions.

Cockroach optomotor responses below single photon level

Reliable vision in dim light depends on the efficient capture of photons. Moreover, visually guided behaviour requires reliable signals from the photoreceptors to generate appropriate motor reactions. Here, we show that at behavioural low-light threshold, cockroach photoreceptors respond to moving gratings with single-photon absorption events known as 'quantum bumps' at or below the rate of 0.1 s(-1). By performing behavioural experiments and intracellular recordings from photoreceptors under identical stimulus conditions, we demonstrate that continuous modulation of the photoreceptor membrane potential is not necessary to elicit visually guided behaviour. The results indicate that in cockroach motion detection, massive temporal and spatial pooling takes place throughout the eye under dim conditions, involving currently unknown neural processing algorithms. The extremely high night-vision capability of the cockroach visual system provides a roadmap for bio-mimetic imaging design.

A common evolutionary origin for the ON- and OFF-edge motion detection pathways of the Drosophila visual system

Synaptic circuits for identified behaviors in the Drosophila brain have typically been considered from either a developmental or functional perspective without reference to how the circuits might have been inherited from ancestral forms. For example, two candidate pathways for ON- and OFF-edge motion detection in the visual system act via circuits that use respectively either T4 or T5, two cell types of the fourth neuropil, or lobula plate (LOP), that exhibit narrow-field direction-selective responses and provide input to wide-field tangential neurons. T4 or T5 both have four subtypes that terminate one each in the four strata of the LOP. Representatives are reported in a wide range of Diptera, and both cell types exhibit various similarities in: (1) the morphology of their dendritic arbors; (2) their four morphological and functional subtypes; (3) their cholinergic profile in Drosophila; (4) their input from the pathways of L3 cells in the first neuropil, or lamina (LA), and by one of a pair of LA cells, L1 (to the T4 pathway) and L2 (to the T5 pathway); and (5) their innervation by a single, wide-field contralateral tangential neuron from the central brain. Progenitors of both also express the gene atonal early in their proliferation from the inner anlage of the developing optic lobe, being alone among many other cell type progeny to do so. Yet T4 receives input in the second neuropil, or medulla (ME), and T5 in the third neuropil or lobula (LO). Here we suggest that these two cell types were originally one, that their ancestral cell population duplicated and split to innervate separate ME and LO neuropils, and that a fiber crossing—the internal chiasma—arose between the two neuropils. The split most plausibly occurred, we suggest, with the formation of the LO as a new neuropil that formed when it separated from its ancestral neuropil to leave the ME, suggesting additionally that ME input neurons to T4 and T5 may also have had a common origin.

Learning-guided automatic three dimensional synapse quantification for drosophila neurons

BACKGROUND

The subcellular distribution of synapses is fundamentally important for the assembly, function, and plasticity of the nervous system. Automated and effective quantification tools are a prerequisite to large-scale studies of the molecular mechanisms of subcellular synapse distribution. Common practices for synapse quantification in neuroscience labs remain largely manual or semi-manual. This is mainly due to computational challenges in automatic quantification of synapses, including large volume, high dimensions and staining artifacts. In the case of confocal imaging, optical limit and xy-z resolution disparity also require special considerations to achieve the necessary robustness.

RESULTS

A novel algorithm is presented in the paper for learning-guided automatic recognition and quantification of synaptic markers in 3D confocal images. The method developed a discriminative model based on 3D feature descriptors that detected the centers of synaptic markers. It made use of adaptive thresholding and multi-channel co-localization to improve the robustness. The detected markers then guided the splitting of synapse clumps, which further improved the precision and recall of the detected synapses. Algorithms were tested on lobula plate tangential cells (LPTCs) in the brain of Drosophila melanogaster, for GABAergic synaptic markers on axon terminals as well as dendrites.

CONCLUSIONS

The presented method was able to overcome the staining artifacts and the fuzzy boundaries of synapse clumps in 3D confocal image, and automatically quantify synaptic markers in a complex neuron such as LPTC. Comparison with some existing tools used in automatic 3D synapse quantification also proved the effectiveness of the proposed method.

Noise-robust recognition of wide-field motion direction and the underlying neural mechanisms in Drosophila melanogaster

Appropriate and robust behavioral control in a noisy environment is important for the survival of most organisms. Understanding such robust behavioral control has been an attractive subject in neuroscience research. Here, we investigated the processing of wide-field motion with random dot noise at both the behavioral and neuronal level in Drosophila melanogaster. We measured the head yaw optomotor response (OMR) and the activity of motion-sensitive neurons, horizontal system (HS) cells, with in vivo whole-cell patch clamp recordings at various levels of noise intensity. We found that flies had a robust sensation of motion direction under noisy conditions, while membrane potential changes of HS cells were not correlated with behavioral responses. By applying signal classification theory to the distributions of HS cell responses, however, we found that motion direction under noise can be clearly discriminated by HS cells, and that this discrimination performance was quantitatively similar to that of OMR. Furthermore, we successfully reproduced HS cell activity in response to noisy motion stimuli with a local motion detector model including a spatial filter and threshold function. This study provides evidence for the physiological basis of noise-robust behavior in a tiny insect brain.