Fly Motion Vision

Motor neurons generate pose-targeted movements via proprioceptive sculpting

Motor neurons are the final common pathway1 through which the brain controls movement of the body, forming the basic elements from which all movement is composed. Yet how a single motor neuron contributes to control during natural movement remains unclear. Here we anatomically and functionally characterize the individual roles of the motor neurons that control head movement in the fly, Drosophila melanogaster. Counterintuitively, we find that activity in a single motor neuron rotates the head in different directions, depending on the starting posture of the head, such that the head converges towards a pose determined by the identity of the stimulated motor neuron. A feedback model predicts that this convergent behaviour results from motor neuron drive interacting with proprioceptive feedback. We identify and genetically2 suppress a single class of proprioceptive neuron3 that changes the motor neuron-induced convergence as predicted by the feedback model. These data suggest a framework for how the brain controls movements: instead of directly generating movement in a given direction by activating a fixed set of motor neurons, the brain controls movements by adding bias to a continuing proprioceptive-motor loop.

GABAergic Inhibition Controls Receptive Field Size, Sensitivity, and Contrast Preference of Direction Selective Retinal Ganglion Cells Near the Threshold of Vision

Information about motion is encoded by direction-selective retinal ganglion cells (DSGCs). These cells reliably transmit this information across a broad range of light levels, spanning moonlight to sunlight. Previous work indicates that adaptation to low light levels causes heterogeneous changes to the direction tuning of ON-OFF (oo)DSGCs and suggests that superior-preferring ON-OFF DSGCs (s-DSGCs) are biased toward detecting stimuli rather than precisely signaling direction. Using a large-scale multielectrode array, we measured the absolute sensitivity of ooDSGCs and found that s-DSGCs are 10-fold more sensitive to dim flashes of light than other ooDSGCs. We measured their receptive field (RF) sizes and found that s-DSGCs also have larger receptive fields than other ooDSGCs; however, the size difference does not fully explain the sensitivity difference. Using a conditional knock-out of gap junctions and pharmacological manipulations, we demonstrate that GABA-mediated inhibition contributes to the difference in absolute sensitivity and receptive field size at low light levels, while the connexin36-mediated gap junction coupling plays a minor role. We further show that under scotopic conditions, ooDSGCs exhibit only an ON response, but pharmacologically removing GABA-mediated inhibition unmasks an OFF response. These results reveal that GABAergic inhibition controls and differentially modulates the responses of ooDSGCs under scotopic conditions.

Re-framing bio-plausible collision detection: identifying shared meta-properties through strategic prototyping

Insects exhibit remarkable abilities in navigating complex natural environments, whether it be evading predators, capturing prey, or seeking out con-specifics, all of which rely on their compact yet reliable neural systems. We explore the field of bio-inspired robotic vision systems, focusing on the locust inspired Lobula Giant Movement Detector (LGMD) models. The existing LGMD models are thoroughly evaluated, identifying their common meta-properties that are essential for their functionality. This article reveals a common framework, characterized by layered structures and computational strategies, which is crucial for enhancing the capability of bio-inspired models for diverse applications. The result of this analysis is the Strategic Prototype, which embodies the identified meta-properties. It represents a modular and more flexible method for developing more responsive and adaptable robotic visual systems. The perspective highlights the potential of the Strategic Prototype: LGMD-Universally Prototype (LGMD-UP), the key to re-framing LGMD models and advancing our understanding and implementation of bio-inspired visual systems in robotics. It might open up more flexible and adaptable avenues for research and practical applications.

Drosophila flying in augmented reality reveals the vision-based control autonomy of the optomotor response

For walking, swimming, and flying animals, the optomotor response is essential to stabilize gaze. How flexible is the optomotor response? Classic work in Drosophila has argued that flies adapt flight control under augmented visual feedback conditions during goal-directed bar fixation. However, whether the lower-level, reflexive optomotor response can similarly adapt to augmented visual feedback (partially autonomous) or not (autonomous) over long timescales is poorly understood. To address this question, we developed an augmented reality paradigm to study the vision-based control autonomy of the yaw optomotor response of flying fruit flies (Drosophila). Flies were placed in a flight simulator, which permitted free body rotation about the yaw axis. By feeding back body movements in real time to a visual display, we augmented and inverted visual feedback. Thus, this experimental paradigm caused a constant visual error between expected and actual visual feedback to study potential adaptive visuomotor control. By combining experiments with control theory, we demonstrate that the optomotor response is autonomous during augmented reality flight bouts of up to 30 min, which exceeds the reported learning epoch during bar fixation. Agreement between predictions from linear systems theory and experimental data supports the notion that the optomotor response is approximately linear and time invariant within our experimental assay. Even under positive visual feedback, which revealed the stability limit of flies in augmented reality, the optomotor response was autonomous. Our results support a hierarchical motor control architecture in flies with fast and autonomous reflexes at the bottom and more flexible behavior at higher levels.

A look into feedback neural computation upon collision selectivity

Physiological studies have shown that a group of locust's lobula giant movement detectors (LGMDs) has a diversity of collision selectivity to approaching objects, relatively darker or brighter than their backgrounds in cluttered environments. Such diversity of collision selectivity can serve locusts to escape from attack by natural enemies, and migrate in swarm free of collision. For computational studies, endeavours have been made to realize the diverse selectivity which, however, is still one of the most challenging tasks especially in complex and dynamic real world scenarios. The existing models are mainly formulated as multi-layered neural networks with merely feed-forward information processing, and do not take into account the effect of re-entrant signals in feedback loop, which is an essential regulatory loop for motion perception, yet never been explored in looming perception. In this paper, we inaugurate feedback neural computation for constructing a new LGMD-based model, named F-LGMD to look into the efficacy upon implementing different collision selectivity. Accordingly, the proposed neural network model features both feed-forward processing and feedback loop. The feedback control propagates output signals of parallel ON/OFF channels back into their starting neurons, thus makes part of the feed-forward neural network, i.e. the ON/OFF channels and the feedback loop form an iterative cycle system. Moreover, the feedback control is instantaneous, which leads to the existence of a fixed point whereby the fixed point theorem is applied to rigorously derive valid range of feedback coefficients. To verify the effectiveness of the proposed method, we conduct systematic experiments covering synthetic and natural collision datasets, and also online robotic tests. The experimental results show that the F-LGMD, with a unified network, can fulfil the diverse collision selectivity revealed in physiology, which not only reduces considerably the handcrafted parameters compared to previous studies, but also offers a both efficient and robust scheme for collision perception through feedback neural computation.

Motion perception based on ON/OFF channels: A survey

Motion perception is an essential ability for animals and artificially intelligent systems interacting effectively, safely with surrounding objects and environments. Biological visual systems, that have naturally evolved over hundreds-million years, are quite efficient and robust for motion perception, whereas artificial vision systems are far from such capability. This paper argues that the gap can be significantly reduced by formulation of ON/OFF channels in motion perception models encoding luminance increment (ON) and decrement (OFF) responses within receptive field, separately. Such signal-bifurcating structure has been found in neural systems of many animal species articulating early motion is split and processed in segregated pathways. However, the corresponding biological substrates, and the necessity for artificial vision systems have never been elucidated together, leaving concerns on uniqueness and advantages of ON/OFF channels upon building dynamic vision systems to address real world challenges. This paper highlights the importance of ON/OFF channels in motion perception through surveying current progress covering both neuroscience and computationally modelling works with applications. Compared to related literature, this paper for the first time provides insights into implementation of different selectivity to directional motion of looming, translating, and small-sized target movement based on ON/OFF channels in keeping with soundness and robustness of biological principles. Existing challenges and future trends of such bio-plausible computational structure for visual perception in connection with hotspots of machine learning, advanced vision sensors like event-driven camera finally are discussed.

Threat gates visual aversion via theta activity in Tachykinergic neurons

Animals must adapt sensory responses to an ever-changing environment for survival. Such sensory modulation is especially critical in a threatening situation, in which animals often promote aversive responses to, among others, visual stimuli. Recently, threatened Drosophila has been shown to exhibit a defensive internal state. Whether and how threatened Drosophila promotes visual aversion, however, remains elusive. Here we report that mechanical threats to Drosophila transiently gate aversion from an otherwise neutral visual object. We further identified the neuropeptide tachykinin, and a single cluster of neurons expressing it ("Tk-GAL42 ∩ Vglut neurons"), that are responsible for gating visual aversion. Calcium imaging analysis revealed that mechanical threats are encoded in Tk-GAL42 ∩ Vglut neurons as elevated activity. Remarkably, we also discovered that a visual object is encoded in Tk-GAL42 ∩ Vglut neurons as θ oscillation, which is causally linked to visual aversion. Our data reveal how a single cluster of neurons adapt organismal sensory response to a threatening situation through a neuropeptide and a combination of rate/temporal coding schemes.

Robust encoding of natural stimuli by neuronal response sequences in monkey visual cortex

Parallel multisite recordings in the visual cortex of trained monkeys revealed that the responses of spatially distributed neurons to natural scenes are ordered in sequences. The rank order of these sequences is stimulus-specific and maintained even if the absolute timing of the responses is modified by manipulating stimulus parameters. The stimulus specificity of these sequences was highest when they were evoked by natural stimuli and deteriorated for stimulus versions in which certain statistical regularities were removed. This suggests that the response sequences result from a matching operation between sensory evidence and priors stored in the cortical network. Decoders trained on sequence order performed as well as decoders trained on rate vectors but the former could decode stimulus identity from considerably shorter response intervals than the latter. A simulated recurrent network reproduced similarly structured stimulus-specific response sequences, particularly once it was familiarized with the stimuli through non-supervised Hebbian learning. We propose that recurrent processing transforms signals from stationary visual scenes into sequential responses whose rank order is the result of a Bayesian matching operation. If this temporal code were used by the visual system it would allow for ultrafast processing of visual scenes.

Bioinspired figure-ground discrimination via visual motion smoothing

Flies detect and track moving targets among visual clutter, and this process mainly relies on visual motion. Visual motion is analyzed or computed with the pathway from the retina to T4/T5 cells. The computation of local directional motion was formulated as an elementary movement detector (EMD) model more than half a century ago. Solving target detection or figure-ground discrimination problems can be equivalent to extracting boundaries between a target and the background based on the motion discontinuities in the output of a retinotopic array of EMDs. Individual EMDs cannot measure true velocities, however, due to their sensitivity to pattern properties such as luminance contrast and spatial frequency content. It remains unclear how local directional motion signals are further integrated to enable figure-ground discrimination. Here, we present a computational model inspired by fly motion vision. Simulations suggest that the heavily fluctuating output of an EMD array is naturally surmounted by a lobula network, which is hypothesized to be downstream of the local motion detectors and have parallel pathways with distinct directional selectivity. The lobula network carries out a spatiotemporal smoothing operation for visual motion, especially across time, enabling the segmentation of moving figures from the background. The model qualitatively reproduces experimental observations in the visually evoked response characteristics of one type of lobula columnar (LC) cell. The model is further shown to be robust to natural scene variability. Our results suggest that the lobula is involved in local motion-based target detection.

Optic flow based spatial vision in insects

The optic flow, i.e., the displacement of retinal images of objects in the environment induced by self-motion, is an important source of spatial information, especially for fast-flying insects. Spatial information over a wide range of distances, from the animal's immediate surroundings over several hundred metres to kilometres, is necessary for mediating behaviours, such as landing manoeuvres, collision avoidance in spatially complex environments, learning environmental object constellations and path integration in spatial navigation. To facilitate the processing of spatial information, the complexity of the optic flow is often reduced by active vision strategies. These result in translations and rotations being largely separated by a saccadic flight and gaze mode. Only the translational components of the optic flow contain spatial information. In the first step of optic flow processing, an array of local motion detectors provides a retinotopic spatial proximity map of the environment. This local motion information is then processed in parallel neural pathways in a task-specific manner and used to control the different components of spatial behaviour. A particular challenge here is that the distance information extracted from the optic flow does not represent the distances unambiguously, but these are scaled by the animal's speed of locomotion. Possible ways of coping with this ambiguity are discussed.

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

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

Zebrafish - The Neurobehavioural Model in Trend

Zebrafish (Danio rerio) is currently in vogue as a prevalently used experimental model for studies concerning neurobehavioural disorders and associated fields. Since the 1960s, this model has succeeded in breaking most barriers faced in the hunt for an experimental model. From its appearance to its high parity with human beings genetically, this model renders itself as an advantageous experimental lab animal. Neurobehavioural disorders have always posed an arduous task in terms of their detection as well as in determining their exact etiology. They are still, in most cases, diseases of interest for inventing or discovering novel pharmacological interventions. Thus, the need for a harbinger experimental model for studying neurobehaviours is escalating. Ensuring the same model is used for studying several neuro-studies conserves the results from inter-species variations. For this, we need a model that satisfies all the pre-requisite conditions to be made the final choice of model for neurobehavioural studies. This review recapitulates the progress of zebrafish as an experimental model with its most up-to-the-minute advances in the area. Various tests, assays, and responses employed using zebrafish in screening neuroactive drugs have been tabulated effectively. The tools, techniques, protocols, and apparatuses that bolster zebrafish studies are discussed. The probable research that can be done using zebrafish has also been briefly outlined. The various breeding and maintenance methods employed, along with the information on various strains available and most commonly used, are also elaborated upon, supplementing Zebrafish's use in neuroscience.

Exploration of motion inhibition for the suppression of false positives in biologically inspired small target detection algorithms from a moving platform

Detecting small moving targets against a cluttered background in visual data is a challenging task. The main problems include spatio-temporal target contrast enhancement, background suppression and accurate target segmentation. When targets are at great distances from a non-stationary camera, the difficulty of these challenges increases. In such cases the moving camera can introduce large spatial changes between frames which may cause issues in temporal algorithms; furthermore targets can approach a single pixel, thereby affecting spatial methods. Previous literature has shown that biologically inspired methods, based on the vision systems of insects, are robust to such conditions. It has also been shown that the use of divisive optic-flow inhibition with these methods enhances the detectability of small targets. However, the location within the visual pathway the inhibition should be applied was ambiguous. In this paper, we investigated the tunings of some of the optic-flow filters and use of a nonlinear transform on the optic-flow signal to modify motion responses for the purpose of suppressing false positives and enhancing small target detection. Additionally, we looked at multiple locations within the biologically inspired vision (BIV) algorithm where inhibition could further enhance detection performance, and look at driving the nonlinear transform with a global motion estimate. To get a better understanding of how the BIV algorithm performs, we compared to other state-of-the-art target detection algorithms, and look at how their performance can be enhanced with the optic-flow inhibition. Our explicit use of the nonlinear inhibition allows for the incorporation of a wider dynamic range of inhibiting signals, along with spatio-temporal filter refinement, which further increases target-background discrimination in the presence of camera motion. Extensive experiments shows that our proposed approach achieves an improvement of 25% over linearly conditioned inhibition schemes and 2.33 times the detection performance of the BIV model without inhibition. Moreover, our approach achieves between 10 and 104 times better detection performance compared to any conventional state-of-the-art moving object detection algorithm applied to the same, highly cluttered and moving scenes. Applying the nonlinear inhibition to other algorithms showed that their performance can be increased by up to 22 times. These findings show that the application of optic-flow- based signal suppression should be applied to enhance target detection from moving platforms. Furthermore, they indicate where best to look for evidence of such signals within the insect brain.

Contrast independent biologically inspired translational optic flow estimation

The visual systems of insects are relatively simple compared to humans. However, they enable navigation through complex environments where insects perform exceptional levels of obstacle avoidance. Biology uses two separable modes of optic flow to achieve this: rapid gaze fixation (rotational motion known as saccades); and the inter-saccadic translational motion. While the fundamental process of insect optic flow has been known since the 1950's, so too has its dependence on contrast. The surrounding visual pathways used to overcome environmental dependencies are less well known. Previous work has shown promise for low-speed rotational motion estimation, but a gap remained in the estimation of translational motion, in particular the estimation of the time to impact. To consistently estimate the time to impact during inter-saccadic translatory motion, the fundamental limitation of contrast dependence must be overcome. By adapting an elaborated rotational velocity estimator from literature to work for translational motion, this paper proposes a novel algorithm for overcoming the contrast dependence of time to impact estimation using nonlinear spatio-temporal feedforward filtering. By applying bioinspired processes, approximately 15 points per decade of statistical discrimination were achieved when estimating the time to impact to a target across 360 background, distance, and velocity combinations: a 17-fold increase over the fundamental process. These results show the contrast dependence of time to impact estimation can be overcome in a biologically plausible manner. This, combined with previous results for low-speed rotational motion estimation, allows for contrast invariant computational models designed on the principles found in the biological visual system, paving the way for future visually guided systems.

How Do Neural Networks Estimate Optical Flow? A Neuropsychology-Inspired Study

End-to-end trained convolutional neural networks have led to a breakthrough in optical flow estimation. The most recent advances focus on improving the optical flow estimation by improving the architecture and setting a new benchmark on the publicly available MPI-Sintel dataset. Instead, in this article, we investigate how deep neural networks estimate optical flow. A better understanding of how these networks function is important for (i) assessing their generalization capabilities to unseen inputs, and (ii) suggesting changes to improve their performance. For our investigation, we focus on FlowNetS, as it is the prototype of an encoder-decoder neural network for optical flow estimation. Furthermore, we use a filter identification method that has played a major role in uncovering the motion filters present in animal brains in neuropsychological research. The method shows that the filters in the deepest layer of FlowNetS are sensitive to a variety of motion patterns. Not only do we find translation filters, as demonstrated in animal brains, but thanks to the easier measurements in artificial neural networks, we even unveil dilation, rotation, and occlusion filters. Furthermore, we find similarities in the refinement part of the network and the perceptual filling-in process which occurs in the mammal primary visual cortex.

Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila

Natural vision is dynamic: as an animal moves, its visual input changes dramatically. How can the visual system reliably extract local features from an input dominated by self-generated signals? In Drosophila, diverse local visual features are represented by a group of projection neurons with distinct tuning properties. Here, we describe a connectome-based volumetric imaging strategy to measure visually evoked neural activity across this population. We show that local visual features are jointly represented across the population, and a shared gain factor improves trial-to-trial coding fidelity. A subset of these neurons, tuned to small objects, is modulated by two independent signals associated with self-movement, a motor-related signal, and a visual motion signal associated with rotation of the animal. These two inputs adjust the sensitivity of these feature detectors across the locomotor cycle, selectively reducing their gain during saccades and restoring it during intersaccadic intervals. This work reveals a strategy for reliable feature detection during locomotion.

Accommodating unobservability to control flight attitude with optic flow

Attitude control is an essential flight capability. Whereas flying robots commonly rely on accelerometers¹ for estimating attitude, flying insects lack an unambiguous sense of gravity^2,3. Despite the established role of several sense organs in attitude stabilization^3-5, the dependence of flying insects on an internal gravity direction estimate remains unclear. Here we show how attitude can be extracted from optic flow when combined with a motion model that relates attitude to acceleration direction. Although there are conditions such as hover in which the attitude is unobservable, we prove that the ensuing control system is still stable, continuously moving into and out of these conditions. Flying robot experiments confirm that accommodating unobservability in this manner leads to stable, but slightly oscillatory, attitude control. Moreover, experiments with a bio-inspired flapping-wing robot show that residual, high-frequency attitude oscillations from flapping motion improve observability. The presented approach holds a promise for robotics, with accelerometer-less autopilots paving the road for insect-scale autonomous flying robots⁶. Finally, it forms a hypothesis on insect attitude estimation and control, with the potential to provide further insight into known biological phenomena^5,7,8 and to generate new predictions such as reduced head and body attitude variance at higher flight speeds⁹.

Neuronal circuits integrating visual motion information in Drosophila melanogaster

The detection of visual motion enables sophisticated animal navigation, and studies on flies have provided profound insights into the cellular and circuit bases of this neural computation. The fly's directionally selective T4 and T5 neurons encode ON and OFF motion, respectively. Their axons terminate in one of the four retinotopic layers in the lobula plate, where each layer encodes one of the four directions of motion. Although the input circuitry of the directionally selective neurons has been studied in detail, the synaptic connectivity of circuits integrating T4/T5 motion signals is largely unknown. Here, we report a 3D electron microscopy reconstruction, wherein we comprehensively identified T4/T5's synaptic partners in the lobula plate, revealing a diverse set of new cell types and attributing new connectivity patterns to the known cell types. Our reconstruction explains how the ON- and OFF-motion pathways converge. T4 and T5 cells that project to the same layer connect to common synaptic partners and comprise a core motif together with bilayer interneurons, detailing the circuit basis for computing motion opponency. We discovered pathways that likely encode new directions of motion by integrating vertical and horizontal motion signals from upstream T4/T5 neurons. Finally, we identify substantial projections into the lobula, extending the known motion pathways and suggesting that directionally selective signals shape feature detection there. The circuits we describe enrich the anatomical basis for experimental and computations analyses of motion vision and bring us closer to understanding complete sensory-motor pathways.

Generating spatiotemporal patterns of linearly polarised light at high frame rates for insect vision research

Polarisation vision is commonplace among invertebrates; however, most experiments focus on determining behavioural and/or neurophysiological responses to static polarised light sources rather than moving patterns of polarised light. To address the latter, we designed a polarisation stimulation device based on superimposing polarised and non-polarised images from two projectors, which can display moving patterns at frame rates exceeding invertebrate flicker fusion frequencies. A linear polariser fitted to one projector enables moving patterns of polarised light to be displayed, whilst the other projector contributes arbitrary intensities of non-polarised light to yield moving patterns with a defined polarisation and intensity contrast. To test the device, we measured receptive fields of polarisation-sensitive Argynnis paphia butterfly photoreceptors for both non-polarised and polarised light. We then measured local motion sensitivities of the optic flow-sensitive lobula plate tangential cell H1 in Calliphora vicina blowflies under both polarised and non-polarised light, finding no polarisation sensitivity in this neuron.

Summary: Design of a versatile visual stimulation device for presenting moving patterns of polarised light, and demonstration of its use to characterise polarisation sensitivity in butterfly photoreceptors and blowfly motion-sensitive interneurons.

An Artificial Visual System for Motion Direction Detection Based on the Hassenstein–Reichardt Correlator Model

The perception of motion direction is essential for the survival of visual animals. Despite various theoretical and biophysical investigations that have been conducted to elucidate directional selectivity at the neural level, the systemic mechanism of motion direction detection remains elusive. Here, we develop an artificial visual system (AVS) based on the core computation of the Hassenstein–Reichardt correlator (HRC) model for global motion direction detection. With reference to the biological investigations of Drosophila, we first describe a local motion-sensitive, directionally detective neuron that only responds to ON motion signals with high pattern contrast in a particular direction. Then, we use the full-neurons scheme motion direction detection mechanism to detect the global motion direction based on our previous research. The mechanism enables our AVS to detect multiple directions in a two-dimensional view, and the global motion direction is inferred from the outputs of all local motion-sensitive directionally detective neurons. To verify the reliability of our AVS, we conduct a series of experiments and compare its performance with the time-considered convolution neural network (CNN) and the EfficientNetB0 under the same conditions. The experimental results demonstrated that our system is reliable in detecting the direction of motion, and among the three models, our AVS has better motion direction detection capabilities.

Multiple mechanisms of photoreceptor spectral tuning in Heliconius butterflies

The evolution of color vision is often studied through the lens of receptor gain relative to an ancestor with fewer spectral classes of photoreceptor. For instance, in Heliconius butterflies, a genus-specific UVRh opsin duplication led to the evolution of UV color discrimination in Heliconius erato females, a rare trait among butterflies. However, color vision evolution is not well understood in the context of loss. In Heliconius melpomene and Heliconius ismenius lineages, the UV2 receptor subtype has been lost, which limits female color vision in shorter wavelengths. Here, we compare the visual systems of butterflies that have either retained or lost the UV2 photoreceptor using intracellular recordings, ATAC-seq, and antibody staining. We identify several ways these butterflies modulate their color vision. In H. melpomene, chromatin reorganization has downregulated an otherwise intact UVRh2 gene, whereas in H. ismenius, pseudogenization has led to the truncation of UVRh2. In species that lack the UV2 receptor, the peak sensitivity of the remaining UV1 photoreceptor cell is shifted to longer wavelengths. Across Heliconius, we identify the widespread use of filtering pigments and co-expression of two opsins in the same photoreceptor cells. Multiple mechanisms of spectral tuning, including the molecular evolution of blue opsins, have led to the divergence of receptor sensitivities between species. The diversity of photoreceptor and ommatidial subtypes between species suggests that Heliconius visual systems are under varying selection pressures for color discrimination. Modulating the wavelengths of peak sensitivities of both the blue- and remaining UV-sensitive photoreceptor cells suggests that Heliconius species may have compensated for UV receptor loss.

Optocollic responses in adult barn owls (Tyto furcata)

Barn owls, like primates, have frontally oriented eyes, which allow for a large binocular overlap. While owls have similar binocular vision and visual-search strategies as primates, it is less clear whether reflexive visual behavior also resembles that of primates or is more similar to that of closer related, but lateral-eyed bird species. Test cases are visual responses driven by wide-field movement: the optokinetic, optocollic, and optomotor responses, mediated by eye, head and body movements, respectively. Adult primates have a so-called symmetric horizontal response: they show the same following behavior, if the stimulus, presented to one eye only, moves in the nasal-to-temporal direction or in the temporal-to-nasal direction. By contrast, lateral-eyed birds have an asymmetric response, responding better to temporal-to-nasal movement than to nasal-to-temporal movement. We show here that the horizontal optocollic response of adult barn owls is less asymmetric than that in the chicken for all velocities tested. Moreover, the response is symmetric for low velocities (< 20 deg/s), and similar to that of primates. The response becomes moderately asymmetric for middle-range velocities (20-40 deg/s). A definitive statement for the complex situation for higher velocities (> 40 deg/s) is not possible.

A biophysical account of multiplication by a single neuron

Nonlinear, multiplication-like operations carried out by individual nerve cells greatly enhance the computational power of a neural system1-3, but our understanding of their biophysical implementation is scant. Here we pursue this problem in the Drosophila melanogaster ON motion vision circuit4,5, in which we record the membrane potentials of direction-selective T4 neurons and of their columnar input elements6,7 in response to visual and pharmacological stimuli in vivo. Our electrophysiological measurements and conductance-based simulations provide evidence for a passive supralinear interaction between two distinct types of synapse on T4 dendrites. We show that this multiplication-like nonlinearity arises from the coincidence of cholinergic excitation and release from glutamatergic inhibition. The latter depends on the expression of the glutamate-gated chloride channel GluClα8,9 in T4 neurons, which sharpens the directional tuning of the cells and shapes the optomotor behaviour of the animals. Interacting pairs of shunting inhibitory and excitatory synapses have long been postulated as an analogue approximation of a multiplication, which is integral to theories of motion detection10,11, sound localization12 and sensorimotor control13.

Building 2D Model of Compound Eye Vision for Machine Learning

This paper presents a two-dimensional mathematical model of compound eye vision. Such a model is useful for solving navigation issues for autonomous mobile robots on the ground plane. The model is inspired by the insect compound eye that consists of ommatidia, which are tiny independent photoreception units, each of which combines a cornea, lens, and rhabdom. The model describes the planar binocular compound eye vision, focusing on measuring distance and azimuth to a circular feature with an arbitrary size. The model provides a necessary and sufficient condition for the visibility of a circular feature by each ommatidium. On this basis, an algorithm is built for generating a training data set to create two deep neural networks (DNN): the first detects the distance, and the second detects the azimuth to a circular feature. The hyperparameter tuning and the configurations of both networks are described. Experimental results showed that the proposed method could effectively and accurately detect the distance and azimuth to objects.

Optic Flow: A History

The concept of optic flow, a global pattern of visual motion that is both caused by and signals self-motion, is canonically ascribed to James Gibson's 1950 book "The Perception of the Visual World." There have, however, been several other developments of this concept, chiefly by Gwilym Grindley and Edward Calvert. Based on rarely referenced scientific literature and archival research, this article describes the development of the concept of optic flow by the aforementioned authors and several others. The article furthermore presents the available evidence for interactions between these authors, focusing on whether parts of Gibson's proposal were derived from the work of Grindley or Calvert. While Grindley's work may have made Gibson aware of the geometrical facts of optic flow, Gibson's work is not derivative of Grindley's. It is furthermore shown that Gibson only learned of Calvert's work in 1956, almost a decade after Gibson first published his proposal. In conclusion, the development of the concept of optic flow presents an intriguing example of convergent thought in the progress of science.

Elimination of vision-guided target attraction in Aedes aegypti using CRISPR

Blood-feeding insects, such as the mosquito, Aedes (Ae.) aegypti, use multiple senses to seek out and bite humans.^1,2 Upon exposure to the odor of CO₂, the attention of female mosquitoes to potential targets is greatly increased. Female mosquitoes are attracted to high-contrast visual cues and use skin olfactory cues to assist them in homing in on targets several meters away.^3-9 Within close range, convective heat from skin and additional skin odors further assist the mosquitoes' evaluation as to whether the object of interest might be a host.^10,11 Here, using CRISPR-Cas9, we mutated the gene encoding Op1, which is the most abundant of the five rhodopsins expressed in the eyes of Ae. aegypti. Using cage and wind-tunnel assays, we found that elimination of op1 did not impair CO₂-induced target seeking. We then mutated op2, which encodes the rhodopsin most similar to Op1, and also found that there was no impact on this behavior. Rather, mutation of both op1 and op2 was required for abolishing vision-guided target attraction. In contrast, the double mutants exhibited normal phototaxis and odor-tracking responses. By measuring the walking optomotor response, we found that the double mutants still perceived optic flow. In further support of the conclusion that the double mutant is not blind, the animals retained an electrophysiological response to light, although it was diminished. This represents the first genetic perturbation of vision in mosquitoes and indicates that vision-guided target attraction by Ae. aegypti depends on two highly related rhodopsins.

Single-cell transcriptomics in the Drosophila visual system: Advances and perspectives on cell identity regulation, connectivity, and neuronal diversity evolution

The Drosophila visual system supports complex behaviors and shares many of its anatomical and molecular features with the vertebrate brain. Yet, it contains a much more manageable number of neurons and neuronal types. In addition to the extensive Drosophila genetic toolbox, this relative simplicity has allowed decades of work to yield a detailed account of its neuronal type diversity, morphology, connectivity and specification mechanisms. In the past three years, numerous studies have applied large scale single-cell transcriptomic approaches to the Drosophila visual system and have provided access to the complete gene expression profile of most neuronal types throughout development. This makes the fly visual system particularly well suited to perform detailed studies of the genetic mechanisms underlying the evolution and development of neuronal systems. Here, we highlight how these transcriptomic resources allow exploring long-standing biological questions under a new light. We first present the efforts made to characterize neuronal diversity in the Drosophila visual system and suggest ways to further improve this description. We then discuss current advances allowed by the single-cell datasets, and envisage how these datasets can be further leveraged to address fundamental questions regarding the regulation of neuronal identity, neuronal circuit development and the evolution of neuronal diversity.

Autonomous Flying With Neuromorphic Sensing

Autonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges. In this paper, we argue for a bio-inspired approach to solve autonomous flying challenges, outline the frontier of sensing, data processing, and flight control within a neuromorphic paradigm, and chart directions of research needed to achieve operational capabilities comparable to those we observe in nature. One central problem of neuromorphic computing is learning. In biological systems, learning is achieved by adaptive and relativistic information acquisition characterized by near-continuous information retrieval with variable rates and sparsity. This results in both energy and computational resource savings being an inspiration for autonomous systems. We consider pertinent features of insect, bat and bird flight behavior as examples to address various vital aspects of autonomous flight. Insects exhibit sophisticated flight dynamics with comparatively reduced complexity of the brain. They represent excellent objects for the study of navigation and flight control. Bats and birds enable more complex models of attention and point to the importance of active sensing for conducting more complex missions. The implementation of neuromorphic paradigms for autonomous flight will require fundamental changes in both traditional hardware and software. We provide recommendations for sensor hardware and processing algorithm development to enable energy efficient and computationally effective flight control.

Polarization contrasts and their effect on the gaze stabilization of crustaceans

Many animals go to great lengths to stabilize their eyes relative to the visual scene and do so to enhance the localization of moving objects and to functionally partition the visual system relative to the outside world. An important cue that is used to control these stabilization movements is contrast within the visual surround. Previous studies on insects, spiders and fish have shown that gaze stabilization is achromatic (‘colour blind’), meaning that chromatic contrast alone (in the absence of apparent intensity contrasts) does not contribute to gaze stabilization. Following the assumption that polarization vision is analogous in many ways to colour vision, the present study shows that five different crustacean species do not use the polarization of light alone for gaze stabilization, despite being able to use this modality for detecting predator-like objects. This work therefore suggests that the gaze stabilization in many crustaceans cannot be elicited by the polarization of light alone.

Summary: Five different crustacean species do not use the polarization of light alone for gaze stabilization, despite being able to use this modality for detecting predator-like objects.

Artificial fly visual joint perception neural network inspired by multiple-regional collision detection

The biological visual system includes multiple types of motion sensitive neurons which preferentially respond to specific perceptual regions. However, it still keeps open how to borrow such neurons to construct bio-inspired computational models for multiple-regional collision detection. To fill this gap, this work proposes a visual joint perception neural network with two subnetworks - presynaptic and postsynaptic neural networks, inspired by the preferentialperception characteristics of three horizontal and vertical motion sensitive neurons. Related to the neural network and three hazard detection mechanisms, an artificial fly visual synthesized collision detection model for multiple-regional collision detection is originally developed to monitor possible danger occurrence in the case where one or more moving objects appear in the whole field of view. The experiments can clearly draw two conclusions: (i) the acquired neural network can effectively display the characteristics of visual movement, and (ii) the collision detection model, which outperforms the compared models, can effectively perform multiple-regional collision detection at a high success rate, and only takes about 0.24s to complete the process of collision detection for each virtual or actual image frame with resolution 110×60.

Modelling Drosophila motion vision pathways for decoding the direction of translating objects against cluttered moving backgrounds

Decoding the direction of translating objects in front of cluttered moving backgrounds, accurately and efficiently, is still a challenging problem. In nature, lightweight and low-powered flying insects apply motion vision to detect a moving target in highly variable environments during flight, which are excellent paradigms to learn motion perception strategies. This paper investigates the fruit fly Drosophila motion vision pathways and presents computational modelling based on cutting-edge physiological researches. The proposed visual system model features bio-plausible ON and OFF pathways, wide-field horizontal-sensitive (HS) and vertical-sensitive (VS) systems. The main contributions of this research are on two aspects: (1) the proposed model articulates the forming of both direction-selective and direction-opponent responses, revealed as principal features of motion perception neural circuits, in a feed-forward manner; (2) it also shows robust direction selectivity to translating objects in front of cluttered moving backgrounds, via the modelling of spatiotemporal dynamics including combination of motion pre-filtering mechanisms and ensembles of local correlators inside both the ON and OFF pathways, which works effectively to suppress irrelevant background motion or distractors, and to improve the dynamic response. Accordingly, the direction of translating objects is decoded as global responses of both the HS and VS systems with positive or negative output indicating preferred-direction or null-direction translation. The experiments have verified the effectiveness of the proposed neural system model, and demonstrated its responsive preference to faster-moving, higher-contrast and larger-size targets embedded in cluttered moving backgrounds.

A novel setup for simultaneous two-photon functional imaging and precise spectral and spatial visual stimulation in Drosophila

Motion vision has been extensively characterised in Drosophila melanogaster, but substantially less is known about how flies process colour, or how spectral information affects other visual modalities. To accurately dissect the components of the early visual system responsible for processing colour, we developed a versatile visual stimulation setup to probe combined spatial, temporal and spectral response properties. Using flies expressing neural activity indicators, we tracked visual responses in the medulla, the second visual neuropil, to a projected colour stimulus. The introduction of custom bandpass optical filters enables simultaneous two-photon imaging and visual stimulation over a large range of wavelengths without compromising the temporal stimulation rate. With monochromator-produced light, any spectral bandwidth and centre wavelength from 390 to 730 nm can be selected to produce a narrow spectral hue. A specialised screen material scatters each band of light across the visible spectrum equally at all locations of the screen, thus enabling presentation of spatially structured stimuli. We show layer-specific shifts of spectral response properties in the medulla correlating with projection regions of photoreceptor terminals.

Active vision shapes and coordinates flight motor responses in flies

Animals use active sensing to respond to sensory inputs and guide future motor decisions. In flight, flies generate a pattern of head and body movements to stabilize gaze. How the brain relays visual information to control head and body movements and how active head movements influence downstream motor control remains elusive. Using a control theoretic framework, we studied the optomotor gaze stabilization reflex in tethered flight and quantified how head movements stabilize visual motion and shape wing steering efforts in fruit flies (Drosophila). By shaping visual inputs, head movements increased the gain of wing steering responses and coordination between stimulus and wings, pointing to a tight coupling between head and wing movements. Head movements followed the visual stimulus in as little as 10 ms-a delay similar to the human vestibulo-ocular reflex-whereas wing steering responses lagged by more than 40 ms. This timing difference suggests a temporal order in the flow of visual information such that the head filters visual information eliciting downstream wing steering responses. Head fixation significantly decreased the mechanical power generated by the flight motor by reducing wingbeat frequency and overall thrust. By simulating an elementary motion detector array, we show that head movements shift the effective visual input dynamic range onto the sensitivity optimum of the motion vision pathway. Taken together, our results reveal a transformative influence of active vision on flight motor responses in flies. Our work provides a framework for understanding how to coordinate moving sensors on a moving body.

Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception

The combination of spiking neural networks and event-based vision sensors holds the potential of highly efficient and high-bandwidth optical flow estimation. This paper presents the first hierarchical spiking architecture in which motion (direction and speed) selectivity emerges in an unsupervised fashion from the raw stimuli generated with an event-based camera. A novel adaptive neuron model and stable spike-timing-dependent plasticity formulation are at the core of this neural network governing its spike-based processing and learning, respectively. After convergence, the neural architecture exhibits the main properties of biological visual motion systems, namely feature extraction and local and global motion perception. Convolutional layers with input synapses characterized by single and multiple transmission delays are employed for feature and local motion perception, respectively; while global motion selectivity emerges in a final fully-connected layer. The proposed solution is validated using synthetic and real event sequences. Along with this paper, we provide the cuSNN library, a framework that enables GPU-accelerated simulations of large-scale spiking neural networks. Source code and samples are available at https://github.com/tudelft/cuSNN.

Non-canonical Receptive Field Properties and Neuromodulation of Feature-Detecting Neurons in Flies

Several fundamental aspects of motion vision circuitry are prevalent across flies and mice. Both taxa segregate ON and OFF signals. For any given spatial pattern, motion detectors in both taxa are tuned to speed, selective for one of four cardinal directions, and modulated by catecholamine neurotransmitters. These similarities represent conserved, canonical properties of the functional circuits and computational algorithms for motion vision. Less is known about feature detectors, including how receptive field properties differ from the motion pathway or whether they are under neuromodulatory control to impart functional plasticity for the detection of salient objects from a moving background. Here, we investigated 19 types of putative feature selective lobula columnar (LC) neurons in the optic lobe of the fruit fly Drosophila melanogaster to characterize divergent properties of feature selection. We identified LC12 and LC15 as feature detectors. LC15 encodes moving bars, whereas LC12 is selective for the motion of discrete objects, mostly independent of size. Neither is selective for contrast polarity, speed, or direction, highlighting key differences in the underlying algorithms for feature detection and motion vision. We show that the onset of background motion suppresses object responses by LC12 and LC15. Surprisingly, the application of octopamine, which is released during flight, reverses the suppressive influence of background motion, rendering both LCs able to track moving objects superimposed against background motion. Our results provide a comparative framework for the function and modulation of feature detectors and new insights into the underlying neuronal mechanisms involved in visual feature detection.

Retinal slip compensation of pitch-constrained blue bottle flies flying in a flight mill

In the presence of wind or background image motion, flies are able to maintain a constant retinal slip velocity by regulating flight speed to the extent permitted by their locomotor capacity. Here we investigated the retinal slip compensation of tethered blue bottle flies (Calliphora vomitoria) flying semi-freely along an annular corridor in a magnetically levitated flight mill enclosed by two motorized cylindrical walls. We perturbed the flies' retinal slip by spinning the cylindrical walls, generating bilaterally averaged retinal slip perturbations from -0.3 to 0.3 m s-1 (or -116.4 to 116.4 deg s-1). When the perturbation was less than ∼0.1 m s-1 (38.4 deg s-1), the flies successfully compensated the perturbations and maintained a retinal slip velocity by adjusting their airspeed up to 20%. However, with greater retinal slip perturbation, the flies' compensation became saturated as their airspeed plateaued, indicating that they were unable to further maintain a constant retinal slip velocity. The compensation gain, i.e. the ratio of airspeed compensation and retinal slip perturbation, depended on the spatial frequency of the grating patterns, being the largest at 12 m-1 (0.04 deg-1).

Event-Based Eccentric Motion Detection Exploiting Time Difference Encoding

Attentional selectivity tends to follow events considered as interesting stimuli. Indeed, the motion of visual stimuli present in the environment attract our attention and allow us to react and interact with our surroundings. Extracting relevant motion information from the environment presents a challenge with regards to the high information content of the visual input. In this work we propose a novel integration between an eccentric down-sampling of the visual field, taking inspiration from the varying size of receptive fields (RFs) in the mammalian retina, and the Spiking Elementary Motion Detector (sEMD) model. We characterize the system functionality with simulated data and real world data collected with bio-inspired event driven cameras, successfully implementing motion detection along the four cardinal directions and diagonally.

Ventral motion parallax enhances fruit fly steering to visual sideslip

Flies and other insects use incoherent motion (parallax) to the front and sides to measure distances and identify obstacles during translation. Although additional depth information could be drawn from below, there is no experimental proof that they use it. The finding that blowflies encode motion disparities in their ventral visual fields suggests this may be an important region for depth information. We used a virtual flight arena to measure fruit fly responses to optic flow. The stimuli appeared below (n = 51) or above the fly (n = 44), at different speeds, with or without parallax cues. Dorsal parallax does not affect responses, and similar motion disparities in rotation have no effect anywhere in the visual field. But responses to strong ventral sideslip (206° s⁻¹) change drastically depending on the presence or absence of parallax. Ventral parallax could help resolve ambiguities in cluttered motion fields, and enhance corrective responses to nearby objects.

A Neural Representation of Naturalistic Motion-Guided Behavior in the Zebrafish Brain

All animals must transform ambiguous sensory data into successful behavior. This requires sensory representations that accurately reflect the statistics of natural stimuli and behavior. Multiple studies show that visual motion processing is tuned for accuracy under naturalistic conditions, but the sensorimotor circuits extracting these cues and implementing motion-guided behavior remain unclear. Here we show that the larval zebrafish retina extracts a diversity of naturalistic motion cues, and the retinorecipient pretectum organizes these cues around the elements of behavior. We find that higher-order motion stimuli, gliders, induce optomotor behavior matching expectations from natural scene analyses. We then image activity of retinal ganglion cell terminals and pretectal neurons. The retina exhibits direction-selective responses across glider stimuli, and anatomically clustered pretectal neurons respond with magnitudes matching behavior. Peripheral computations thus reflect natural input statistics, whereas central brain activity precisely codes information needed for behavior. This general principle could organize sensorimotor transformations across animal species.

The physiology of movement

Movement, from foraging to migration, is known to be under the influence of the environment. The translation of environmental cues to individual movement decision making is determined by an individual's internal state and anticipated to balance costs and benefits. General body condition, metabolic and hormonal physiology mechanistically underpin this internal state. These physiological determinants are tightly, and often genetically linked with each other and hence central to a mechanistic understanding of movement. We here synthesise the available evidence of the physiological drivers and signatures of movement and review (1) how physiological state as measured in its most coarse way by body condition correlates with movement decisions during foraging, migration and dispersal, (2) how hormonal changes underlie changes in these movement strategies and (3) how these can be linked to molecular pathways. We reveale that a high body condition facilitates the efficiency of routine foraging, dispersal and migration. Dispersal decision making is, however, in some cases stimulated by a decreased individual condition. Many of the biotic and abiotic stressors that induce movement initiate a physiological cascade in vertebrates through the production of stress hormones. Movement is therefore associated with hormone levels in vertebrates but also insects, often in interaction with factors related to body or social condition. The underlying molecular and physiological mechanisms are currently studied in few model species, and show -in congruence with our insights on the role of body condition- a central role of energy metabolism during glycolysis, and the coupling with timing processes during migration. Molecular insights into the physiological basis of movement remain, however, highly refractory. We finalise this review with a critical reflection on the importance of these physiological feedbacks for a better mechanistic understanding of movement and its effects on ecological dynamics at all levels of biological organization.

Color vision in insects: insights from Drosophila

Color vision is an important sensory capability that enhances the detection of contrast in retinal images. Monochromatic animals exclusively detect temporal and spatial changes in luminance, whereas two or more types of photoreceptors and neuronal circuitries for the comparison of their responses enable animals to differentiate spectral information independent of intensity. Much of what we know about the cellular and physiological mechanisms underlying color vision comes from research on vertebrates including primates. In insects, many important discoveries have been made, but direct insights into the physiology and circuit implementation of color vision are still limited. Recent advances in Drosophila systems neuroscience suggest that a complete insect color vision circuitry, from photoreceptors to behavior, including all elements and computations, can be revealed in future. Here, we review fundamental concepts in color vision alongside our current understanding of the neuronal basis of color vision in Drosophila, including side views to selected other insects.

Candidates for photic entrainment pathways to the circadian clock via optic lobe neuropils in the Madeira cockroach

The compound eye of cockroaches is obligatory for entrainment of the Madeira cockroach's circadian clock, but the cellular nature of its entrainment pathways is enigmatic. Employing multiple-label immunocytochemistry, histochemistry, and backfills, we searched for photic entrainment pathways to the accessory medulla (AME), the circadian clock of the Madeira cockroach. We wanted to know whether photoreceptor terminals could directly contact pigment-dispersing factor-immunoreactive (PDF-ir) circadian pacemaker neurons with somata in the lamina (PDFLAs) or somata next to the AME (PDFMEs). Short green-sensitive photoreceptor neurons of the compound eye terminated in lamina layers LA1 and LA2, adjacent to PDFLAs and PDFMEs that branched in LA3. Long UV-sensitive compound eye photoreceptor neurons terminated in medulla layer ME2 without direct contact to ipsilateral PDFMEs that arborized in ME4. Multiple neuropeptide-ir interneurons branched in ME4, connecting the AME to ME2. Before, extraocular photoreceptors of the lamina organ were suggested to send terminals to accessory laminae. There, they overlapped with PDFLAs that mostly colocalized PDF, FMRFamide, and 5-HT immunoreactivities, and with terminals of ipsi- and contralateral PDFMEs. We hypothesize that during the day cholinergic activation of the largest PDFME via lamina organ photoreceptors maintains PDF release orchestrating phases of sleep-wake cycles. As ipsilateral PDFMEs express excitatory and contralateral PDFMEs inhibitory PDF autoreceptors, diurnal PDF release keeps both PDF-dependent clock circuits in antiphase. Future experiments will test whether ipsilateral PDFMEs are sleep-promoting morning cells, while contralateral PDFMEs are activity-promoting evening cells, maintaining stable antiphase via the largest PDFME entrained by extraocular photoreceptors of the lamina organ.

Chromatic information processing in the first optic ganglion of the butterfly Papilio xuthus

The butterfly Papilio xuthus has acute tetrachromatic color vision. Its eyes are furnished with eight spectral classes of photoreceptors, situated in three types of ommatidia, randomly distributed in the retinal mosaic. Here, we investigated early chromatic information processing by recording spectral, angular, and polarization sensitivities of photoreceptors and lamina monopolar cells (LMCs). We identified three spectral classes of LMCs whose spectral sensitivities corresponded to weighted linear sums of the spectral sensitivities of the photoreceptors present in the three ommatidial types. In ~ 25% of the photoreceptor axons, the spectral sensitivities differed from those recorded at the photoreceptor cell bodies. These axons showed spectral opponency, most likely mediated by chloride ion currents through histaminergic interphotoreceptor synapses. The opponency was most prominent in the processes of the long visual fibers in the medulla. We recalculated the wavelength discrimination function using the noise-limited opponency model to reflect the new spectral sensitivity data and found that it matched well with the behaviorally determined function. Our results reveal opponency at the first stage of Papilio’s visual system, indicating that spectral information is preprocessed with signals from photoreceptors within each ommatidium in the lamina, before being conveyed downstream by the long visual fibers and the LMCs.

Mechanisms of vision in the fruit fly

Vision is essential to maximize the efficiency of daily tasks such as feeding, avoiding predators or finding mating partners. An advantageous model is Drosophila melanogaster, since it offers tools that allow genetic and neuronal manipulation with high spatial and temporal resolution, which can be combined with behavioral, anatomical and physiological assays. Recent advances have expanded our knowledge on the neural circuitry underlying such important behaviors as color vision (role of reciprocal inhibition to enhance color signal at the level of the ommatidia); motion vision (motion-detection neurones receive both excitatory and inhibitory input), and sensory processing (role of the central complex in spatial navigation, and in orchestrating the information from other senses and the inner state). Research on synergies between pathways is shaping the field.

Sensory processing by motoneurons: a numerical model for low-level flight control in flies

Rhythmic locomotor behaviour in animals requires exact timing of muscle activation within the locomotor cycle. In rapidly oscillating motor systems, conventional control strategies may be affected by neural delays, making these strategies inappropriate for precise timing control. In flies, wing control thus requires sensory processing within the peripheral nervous system, circumventing the central brain. The underlying mechanism, with which flies integrate graded depolarization of visual interneurons and spiking proprioceptive feedback for precise muscle activation, is under debate. Based on physiological parameters, we developed a numerical model of spike initiation in flight muscles of a blowfly. The simulated Hodgkin–Huxley neuron reproduces multiple experimental findings and explains on the cellular level how vision might control wing kinematics. Sensory processing by single motoneurons appears to be sufficient for control of muscle power during flight in flies and potentially other flying insects, reducing computational load on the central brain during body posture reflexes and manoeuvring flight.

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.