Nonlinear integration of binocular optic flow by DNOVS2, a descending neuron of the fly

A visuomotor circuit for evasive flight turns in Drosophila

Visual systems extract multiple features from a scene using parallel neural circuits. Ultimately, the separate neural signals must come together to coherently influence action. Here, we characterize a circuit in Drosophila that integrates multiple visual features related to imminent threats to drive evasive locomotor turns. We identified, using genetic perturbation methods, a pair of visual projection neurons (LPLC2) and descending neurons (DNp06) that underlie evasive flight turns in response to laterally moving or approaching visual objects. Using two-photon calcium imaging or whole-cell patch clamping, we show that these cells indeed respond to both translating and approaching visual patterns. Furthermore, by measuring visual responses of LPLC2 neurons after genetically silencing presynaptic motion-sensing neurons, we show that their visual properties emerge by integrating multiple visual features across two early visual structures: the lobula and the lobula plate. This study highlights a clear example of how distinct visual signals converge on a single class of visual neurons and then activate premotor neurons to drive action, revealing a concise visuomotor pathway for evasive flight maneuvers in Drosophila.

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.

Impact of walking speed and motion adaptation on optokinetic nystagmus-like head movements in the blowfly Calliphora

The optokinetic nystagmus is a gaze-stabilizing mechanism reducing motion blur by rapid eye rotations against the direction of visual motion, followed by slower syndirectional eye movements minimizing retinal slip speed. Flies control their gaze through head turns controlled by neck motor neurons receiving input directly, or via descending neurons, from well-characterized directional-selective interneurons sensitive to visual wide-field motion. Locomotion increases the gain and speed sensitivity of these interneurons, while visual motion adaptation in walking animals has the opposite effects. To find out whether flies perform an optokinetic nystagmus, and how it may be affected by locomotion and visual motion adaptation, we recorded head movements of blowflies on a trackball stimulated by progressive and rotational visual motion. Flies flexibly responded to rotational stimuli with optokinetic nystagmus-like head movements, independent of their locomotor state. The temporal frequency tuning of these movements, though matching that of the upstream directional-selective interneurons, was only mildly modulated by walking speed or visual motion adaptation. Our results suggest flies flexibly control their gaze to compensate for rotational wide-field motion by a mechanism similar to an optokinetic nystagmus. Surprisingly, the mechanism is less state-dependent than the response properties of directional-selective interneurons providing input to the neck motor system.

The impulse response of optic flow-sensitive descending neurons to roll m-sequences

When animals move through the world, their own movements generate widefield optic flow across their eyes. In insects, such widefield motion is encoded by optic lobe neurons. These lobula plate tangential cells (LPTCs) synapse with optic flow-sensitive descending neurons, which in turn project to areas that control neck, wing and leg movements. As the descending neurons play a role in sensorimotor transformation, it is important to understand their spatio-temporal response properties. Recent work shows that a relatively fast and efficient way to quantify such response properties is to use m-sequences or other white noise techniques. Therefore, here we used m-sequences to quantify the impulse responses of optic flow-sensitive descending neurons in male Eristalis tenax hoverflies. We focused on roll impulse responses as hoverflies perform exquisite head roll stabilizing reflexes, and the descending neurons respond particularly well to roll. We found that the roll impulse responses were fast, peaking after 16.5–18.0 ms. This is similar to the impulse response time to peak (18.3 ms) to widefield horizontal motion recorded in hoverfly LPTCs. We found that the roll impulse response amplitude scaled with the size of the stimulus impulse, and that its shape could be affected by the addition of constant velocity roll or lift. For example, the roll impulse response became faster and stronger with the addition of excitatory stimuli, and vice versa. We also found that the roll impulse response had a long return to baseline, which was significantly and substantially reduced by the addition of either roll or lift.

Summary: The impulse response of hoverfly optic flow-sensitive descending neurons to roll m-sequences reaches its time to peak within 20 ms and slowly returns to baseline over the next 100 ms.

Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers

The visual system must make predictions to compensate for inherent delays in its processing. Yet little is known, mechanistically, about how prediction aids natural behaviors. Here, we show that despite a 20-30ms intrinsic processing delay, the vertical motion sensitive (VS) network of the blowfly achieves maximally efficient prediction. This prediction enables the fly to fine-tune its complex, yet brief, evasive flight maneuvers according to its initial ego-rotation at the time of detection of the visual threat. Combining a rich database of behavioral recordings with detailed compartmental modeling of the VS network, we further show that the VS network has axonal gap junctions that are critical for optimal prediction. During evasive maneuvers, a VS subpopulation that directly innervates the neck motor center can convey predictive information about the fly’s future ego-rotation, potentially crucial for ongoing flight control. These results suggest a novel sensory-motor pathway that links sensory prediction to behavior.

Survival-critical behaviors shape neural circuits to translate sensory information into strikingly fast predictions, e.g. in escaping from a predator faster than the system’s processing delay. We show that the fly visual system implements fast and accurate prediction of its visual experience. This provides crucial information for directing fast evasive maneuvers that unfold over just 40ms. Our work shows how this fast prediction is implemented, mechanistically, and suggests the existence of a novel sensory-motor pathway from the fly visual system to a wing steering motor neuron. Echoing and amplifying previous work in the retina, our work hypothesizes that the efficient encoding of predictive information is a universal design principle supporting fast, natural behaviors.

Binocular responsiveness of projection neurons of the praying mantis optic lobe in the frontal visual field

Praying mantids are the only insects proven to have stereoscopic vision (stereopsis): the ability to perceive depth from the slightly shifted images seen by the two eyes. Recently, the first neurons likely to be involved in mantis stereopsis were described and a speculative neuronal circuit suggested. Here we further investigate classes of neurons in the lobula complex of the praying mantis brain and their tuning to stereoscopically-defined depth. We used sharp electrode recordings with tracer injections to identify visual projection neurons with input in the optic lobe and output in the central brain. In order to measure binocular response fields of the cells the animals watched a vertical bar stimulus in a 3D insect cinema during recordings. We describe the binocular tuning of 19 neurons projecting from the lobula complex and the medulla to central brain areas. The majority of neurons (12/19) were binocular and had receptive fields for both eyes that overlapped in the frontal region. Thus, these neurons could be involved in mantis stereopsis. We also find that neurons preferring different contrast polarity (bright vs dark) tend to be segregated in the mantis lobula complex, reminiscent of the segregation for small targets and widefield motion in mantids and other insects.

Visual motion sensitivity in descending neurons in the hoverfly

Many animals use motion vision information to control dynamic behaviors. For example, flying insects must decide whether to pursue a prey or not, to avoid a predator, to maintain their current flight trajectory, or to land. The neural mechanisms underlying the computation of visual motion have been particularly well investigated in the fly optic lobes. However, the descending neurons, which connect the optic lobes with the motor command centers of the ventral nerve cord, remain less studied. To address this deficiency, we describe motion vision sensitive descending neurons in the hoverfly Eristalis tenax. We describe how the neurons can be identified based on their receptive field properties, and how they respond to moving targets, looming stimuli and to widefield optic flow. We discuss their similarities with previously published visual neurons, in the optic lobes and ventral nerve cord, and suggest that they can be classified as target-selective, looming sensitive and optic flow sensitive, based on these similarities. Our results highlight the importance of using several visual stimuli as the neurons can rarely be identified based on only one response characteristic. In addition, they provide an understanding of the neurophysiology of visual neurons that are likely to affect behavior.

The diversity of lobula plate tangential cells (LPTCs) in the Drosophila motion vision system

To navigate through the environment, animals rely on visual feedback to control their movements relative to their surroundings. In dipteran flies, visual feedback is provided by the wide-field motion-sensitive neurons in the visual system called lobula plate tangential cells (LPTCs). Understanding the role of LPTCs in fly behaviors can address many fundamental questions on how sensory circuits guide behaviors. The blowfly was estimated to have ~ 60 LPTCs, but only a few have been identified in Drosophila. We conducted a Gal4 driver screen and identified five LPTC subtypes in Drosophila, based on their morphological characteristics: LPTCs have large arborizations in the lobula plate and project to the central brain. We compared their morphologies to the blowfly LPTCs and named them after the most similar blowfly cells: CH, H1, H2, FD1 and FD3, and V1. We further characterized their pre- and post-synaptic organizations, as well as their neurotransmitter profiles. These anatomical features largely agree with the anatomy and function of their likely blowfly counterparts. Nevertheless, several anatomical details indicate the Drosophila LPTCs may have more complex functions. Our characterization of these five LPTCs in Drosophila will facilitate further functional studies to understand their roles in the visual circuits that instruct fly behaviors.

Binocular Neuronal Processing of Object Motion in an Arthropod

Animals use binocular information to guide many behaviors. In highly visual arthropods, complex binocular computations involved in processing panoramic optic flow generated during self-motion occur in the optic neuropils. However, the extent to which binocular processing of object motion occurs in these neuropils remains unknown. We investigated this in a crab, where the distance between the eyes and the extensive overlapping of their visual fields advocate for the use of binocular processing. By performing in vivo intracellular recordings from the lobula (third optic neuropil) of male crabs, we assessed responses of object-motion-sensitive neurons to ipsilateral or contralateral moving objects under binocular and monocular conditions. Most recorded neurons responded to stimuli seen independently with either eye, proving that each lobula receives profuse visual information from both eyes. The contribution of each eye to the binocular response varies among neurons, from those receiving comparable inputs from both eyes to those with mainly ipsilateral or contralateral components, some including contralateral inhibition. Electrophysiological profiles indicated that a similar number of neurons were recorded from their input or their output side. In monocular conditions, the first group showed shorter response delays to ipsilateral than to contralateral stimulation, whereas the second group showed the opposite. These results fit well with neurons conveying centripetal and centrifugal information from and toward the lobula, respectively. Intracellular and massive stainings provided anatomical support for this and for direct connections between the two lobulae, but simultaneous recordings failed to reveal such connections. Simplified model circuits of interocular connections are discussed.SIGNIFICANCE STATEMENT Most active animals became equipped with two eyes, which contributes to functions like depth perception, objects spatial location, and motion processing, all used for guiding behaviors. In visually active arthropods, binocular neural processing of the panoramic optic flow generated during self-motion happens already in the optic neuropils. However, whether binocular processing of single-object motion occurs in these neuropils remained unknown. We investigated this in a crab, where motion-sensitive neurons from the lobula can be recorded in the intact animal. Here we demonstrate that different classes of neurons from the lobula compute binocular information. Our results provide new insight into where and how the visual information acquired by the two eyes is first combined in the brain of an arthropod.

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.

Evolution of Biological Image Stabilization

The use of vision to coordinate behavior requires an efficient control design that stabilizes the world on the retina or directs the gaze towards salient features in the surroundings. With a level gaze, visual processing tasks are simplified and behaviorally relevant features from the visual environment can be extracted. No matter how simple or sophisticated the eye design, mechanisms have evolved across phyla to stabilize gaze. In this review, we describe functional similarities in eyes and gaze stabilization reflexes, emphasizing their fundamental role in transforming sensory information into motor commands that support postural and locomotor control. We then focus on gaze stabilization design in flying insects and detail some of the underlying principles. Systems analysis reveals that gaze stabilization often involves several sensory modalities, including vision itself, and makes use of feedback as well as feedforward signals. Independent of phylogenetic distance, the physical interaction between an animal and its natural environment - its available senses and how it moves - appears to shape the adaptation of all aspects of gaze stabilization.

Efficient encoding of motion is mediated by gap junctions in the fly visual system

Understanding the computational implications of specific synaptic connectivity patterns is a fundamental goal in neuroscience. In particular, the computational role of ubiquitous electrical synapses operating via gap junctions remains elusive. In the fly visual system, the cells in the vertical-system network, which play a key role in visual processing, primarily connect to each other via axonal gap junctions. This network therefore provides a unique opportunity to explore the functional role of gap junctions in sensory information processing. Our information theoretical analysis of a realistic VS network model shows that within 10 ms following the onset of the visual input, the presence of axonal gap junctions enables the VS system to efficiently encode the axis of rotation, θ, of the fly’s ego motion. This encoding efficiency, measured in bits, is near-optimal with respect to the physical limits of performance determined by the statistical structure of the visual input itself. The VS network is known to be connected to downstream pathways via a subset of triplets of the vertical system cells; we found that because of the axonal gap junctions, the efficiency of this subpopulation in encoding θ is superior to that of the whole vertical system network and is robust to a wide range of signal to noise ratios. We further demonstrate that this efficient encoding of motion by this subpopulation is necessary for the fly's visually guided behavior, such as banked turns in evasive maneuvers. Because gap junctions are formed among the axons of the vertical system cells, they only impact the system’s readout, while maintaining the dendritic input intact, suggesting that the computational principles implemented by neural circuitries may be much richer than previously appreciated based on point neuron models. Our study provides new insights as to how specific network connectivity leads to efficient encoding of sensory stimuli.

Understanding sensory stimuli from the environment and deciding how best to respond to it behaviorally is essential for survival. What makes organisms efficient in encoding these sensory stimuli? This study provides a novel view on this unresolved issue using the visual system of the fly. We show that a specific synaptic connectivity manifested via gap junctions (GJs) among axons in the Vertical System (VS) network leads to particularly high encoding efficiency of the axis of rotation of the fly’s ego motion. Due to these GJs, triplets of VS neurons (the VS5-6-7 triplet), which connect to a downstream motor system, encode motion stimuli at an efficiency close to the physical limit; this efficient encoding is necessary for evasive maneuvers that are critical for the fly to escape predators. We then suggest why GJs in the VS network enable such high encoding efficiency.

Neural basis of forward flight control and landing in honeybees

The impressive repertoire of honeybee visually guided behaviors, and their ability to learn has made them an important tool for elucidating the visual basis of behavior. Like other insects, bees perform optomotor course correction to optic flow, a response that is dependent on the spatial structure of the visual environment. However, bees can also distinguish the speed of image motion during forward flight and landing, as well as estimate flight distances (odometry), irrespective of the visual scene. The neural pathways underlying these abilities are unknown. Here we report on a cluster of descending neurons (DNIIIs) that are shown to have the directional tuning properties necessary for detecting image motion during forward flight and landing on vertical surfaces. They have stable firing rates during prolonged periods of stimulation and respond to a wide range of image speeds, making them suitable to detect image flow during flight behaviors. While their responses are not strictly speed tuned, the shape and amplitudes of their speed tuning functions are resistant to large changes in spatial frequency. These cells are prime candidates not only for the control of flight speed and landing, but also the basis of a neural 'front end' of the honeybee's visual odometer.

An Array of Descending Visual Interneurons Encoding Self-Motion in Drosophila

The means by which brains transform sensory information into coherent motor actions is poorly understood. In flies, a relatively small set of descending interneurons are responsible for conveying sensory information and higher-order commands from the brain to motor circuits in the ventral nerve cord. Here, we describe three pairs of genetically identified descending interneurons that integrate information from wide-field visual interneurons and project directly to motor centers controlling flight behavior. We measured the physiological responses of these three cells during flight and found that they respond maximally to visual movement corresponding to rotation around three distinct body axes. After characterizing the tuning properties of an array of nine putative upstream visual interneurons, we show that simple linear combinations of their outputs can predict the responses of the three descending cells. Last, we developed a machine vision-tracking system that allows us to monitor multiple motor systems simultaneously and found that each visual descending interneuron class is correlated with a discrete set of motor programs.

SIGNIFICANCE STATEMENT

Most animals possess specialized sensory systems for encoding body rotation, which they use for stabilizing posture and regulating motor actions. In flies and other insects, the visual system contains an array of specialized neurons that integrate local optic flow to estimate body rotation during locomotion. However, the manner in which the output of these cells is transformed by the downstream neurons that innervate motor centers is poorly understood. We have identified a set of three visual descending neurons that integrate the output of nine large-field visual interneurons and project directly to flight motor centers. Our results provide new insight into how the sensory information that encodes body motion is transformed into a code that is appropriate for motor actions.

A Descending Neuron Correlated with the Rapid Steering Maneuvers of Flying Drosophila

To navigate through the world, animals must stabilize their path against disturbances and change direction to avoid obstacles and to search for resources [1, 2]. Locomotion is thus guided by sensory cues but also depends on intrinsic processes, such as motivation and physiological state. Flies, for example, turn with the direction of large-field rotatory motion, an optomotor reflex that is thought to help them fly straight [3-5]. Occasionally, however, they execute fast turns, called body saccades, either spontaneously or in response to patterns of visual motion such as expansion [6-8]. These turns can be measured in tethered flying Drosophila [3, 4, 9], which facilitates the study of underlying neural mechanisms. Whereas there is evidence for an efference copy input to visual interneurons during saccades [10], the circuits that control spontaneous and visually elicited saccades are not well known. Using two-photon calcium imaging and electrophysiological recordings in tethered flying Drosophila, we have identified a descending neuron whose activity is correlated with both spontaneous and visually elicited turns during tethered flight. The cell's activity in open- and closed-loop experiments suggests that it does not underlie slower compensatory responses to horizontal motion but rather controls rapid changes in flight path. The activity of this neuron can explain some of the behavioral variability observed in response to visual motion and appears sufficient for eliciting turns when artificially activated. This work provides an entry point into studying the circuits underlying the control of rapid steering maneuvers in the fly brain.

Organization of descending neurons in Drosophila melanogaster

Neural processing in the brain controls behavior through descending neurons (DNs) - neurons which carry signals from the brain to the spinal cord (or thoracic ganglia in insects). Because DNs arise from multiple circuits in the brain, the numerical simplicity and availability of genetic tools make Drosophila a tractable model for understanding descending motor control. As a first step towards a comprehensive study of descending motor control, here we estimate the number and distribution of DNs in the Drosophila brain. We labeled DNs by backfilling them with dextran dye applied to the neck connective and estimated that there are ~1100 DNs distributed in 6 clusters in Drosophila. To assess the distribution of DNs by neurotransmitters, we labeled DNs in flies in which neurons expressing the major neurotransmitters were also labeled. We found DNs belonging to every neurotransmitter class we tested: acetylcholine, GABA, glutamate, serotonin, dopamine and octopamine. Both the major excitatory neurotransmitter (acetylcholine) and the major inhibitory neurotransmitter (GABA) are employed equally; this stands in contrast to vertebrate DNs which are predominantly excitatory. By comparing the distribution of DNs in Drosophila to those reported previously in other insects, we conclude that the organization of DNs in insects is highly conserved.

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.

Complementary motion tuning in frontal nerve motor neurons of the blowfly

Flies actively turn their head during flight to stabilize their gaze and reduce motion blur. This optomotor response is triggered by wide-field motion indicating a deviation from a desired flight path. We focus on the neuronal circuit that underlies this behavior in the blowfly Calliphora, studying the integration of optic flow in neck motor neurons that innervate muscles controlling head rotations. Frontal nerve motor neurons (FNMNs) have been described anatomically and recorded from extracellularly before. Here, we assign for the first time to five anatomical classes of FNMNs their visual motion tuning. We measured their responses to optic flow, as produced by rotations around particular body axes, recording intracellularly from single axons. Simultaneous injection of Neurobiotin allowed for the anatomical characterization of the recorded cells and revealed coupling patterns with neighboring neurons. The five FNMN classes can be divided into two groups that complement each other, regarding their preferred axes of rotation. The tuning matches the pulling planes of their innervated neck muscles, serving to rotate the head around its longitudinal axis. Anatomical and physiological findings demonstrate a synaptic connection between one FNMN and a well-described descending neuron, elucidating one important step from visual motion integration to neck motor output.

Near-optimal decoding of transient stimuli from coupled neuronal subpopulations

Coupling between sensory neurons impacts their tuning properties and correlations in their responses. How such coupling affects sensory representations and ultimately behavior remains unclear. We investigated the role of neuronal coupling during visual processing using a realistic biophysical model of the vertical system (VS) cell network in the blow fly. These neurons are thought to encode the horizontal rotation axis during rapid free-flight maneuvers. Experimental findings suggest that neurons of the VS are strongly electrically coupled, and that several downstream neurons driving motor responses to ego-rotation receive inputs primarily from a small subset of VS cells. These downstream neurons must decode information about the axis of rotation from a partial readout of the VS population response. To investigate the role of coupling, we simulated the VS response to a variety of rotating visual scenes and computed optimal Bayesian estimates from the relevant subset of VS cells. Our analysis shows that coupling leads to near-optimal estimates from a subpopulation readout. In contrast, coupling between VS cells has no impact on the quality of encoding in the response of the full population. We conclude that coupling at one level of the fly visual system allows for near-optimal decoding from partial information at the subsequent, premotor level. Thus, electrical coupling may provide a general mechanism to achieve near-optimal information transfer from neuronal subpopulations across organisms and modalities.

Fly visual course control: behaviour, algorithms and circuits

Understanding how the brain controls behaviour is undisputedly one of the grand goals of neuroscience research, and the pursuit of this goal has a long tradition in insect neuroscience. However, appropriate techniques were lacking for a long time. Recent advances in genetic and recording techniques now allow the participation of identified neurons in the execution of specific behaviours to be interrogated. By focusing on fly visual course control, I highlight what has been learned about the neuronal circuit modules that control visual guidance in Drosophila melanogaster through the use of these techniques.

Motion-detecting circuits in flies: coming into view

Visual motion cues provide animals with critical information about their environment and guide a diverse array of behaviors. The neural circuits that carry out motion estimation provide a well-constrained model system for studying the logic of neural computation. Through a confluence of behavioral, physiological, and anatomical experiments, taking advantage of the powerful genetic tools available in the fruit fly Drosophila melanogaster, an outline of the neural pathways that compute visual motion has emerged. Here we describe these pathways, the evidence supporting them, and the challenges that remain in understanding the circuits and computations that link sensory inputs to behavior. Studies in flies and vertebrates have revealed a number of functional similarities between motion-processing pathways in different animals, despite profound differences in circuit anatomy and structure. The fact that different circuit mechanisms are used to achieve convergent computational outcomes sheds light on the evolution of the nervous system.

Spectral inputs and ocellar contributions to a pitch-sensitive descending neuron in the honeybee

By measuring insect compensatory optomotor reflexes to visual motion, researchers have examined the computational mechanisms of the motion processing system. However, establishing the spectral sensitivity of the neural pathways that underlie this motion behavior has been difficult, and the contribution of the simple eyes (ocelli) has been rarely examined. In this study we investigate the spectral response properties and ocellar inputs of an anatomically identified descending neuron (DNII(2)) in the honeybee optomotor pathway. Using a panoramic stimulus, we show that it responds selectively to optic flow associated with pitch rotations. The neuron is also stimulated with a custom-built light-emitting diode array that presented moving bars that were either all-green (spectrum 500-600 nm, peak 530 nm) or all-short wavelength (spectrum 350-430 nm, peak 380 nm). Although the optomotor response is thought to be dominated by green-sensitive inputs, we show that DNII(2) is equally responsive to, and direction selective to, both green- and short-wavelength stimuli. The color of the background image also influences the spontaneous spiking behavior of the cell: a green background produces significantly higher spontaneous spiking rates. Stimulating the ocelli produces strong modulatory effects on DNII(2), significantly increasing the amplitude of its responses in the preferred motion direction and decreasing the response latency by adding a directional, short-latency response component. Our results suggest that the spectral sensitivity of the optomotor response in honeybees may be more complicated than previously thought and that ocelli play a significant role in shaping the timing of motion signals.

Impact and sources of neuronal variability in the fly's motion vision pathway

Nervous systems encode information about dynamically changing sensory input by changes in neuronal activity. Neuronal activity changes, however, also arise from noise sources within and outside the nervous system or from changes of the animal's behavioral state. The resulting variability of neuronal responses in representing sensory stimuli limits the reliability with which animals can respond to stimuli and may thus even affect the chances for survival in certain situations. Relevant sources of noise arising at different stages along the motion vision pathway have been investigated from the sensory input to the initiation of behavioral reactions. Here, we concentrate on the reliability of processing visual motion information in flies. Flies rely on visual motion information to guide their locomotion. They are among the best established model systems for the processing of visual motion information allowing us to bridge the gap between behavioral performance and underlying neuronal computations. It has been possible to directly assess the consequences of noise at major stages of the fly's visual motion processing system on the reliability of neuronal signals. Responses of motion sensitive neurons and their variability have been related to optomotor movements as indicators for the overall performance of visual motion computation. We address whether and how noise already inherent in the stimulus, e.g. photon noise for the visual system, influences later processing stages and to what extent variability at the output level of the sensory system limits behavioral performance. Recent advances in circuit analysis and the progress in monitoring neuronal activity in behaving animals should now be applied to understand how the animal meets the requirements of fast and reliable manoeuvres in naturalistic situations.

Integration of binocular optic flow in cervical neck motor neurons of the fly

Global visual motion elicits an optomotor response of the eye that stabilizes the visual input on the retina. Here, we analyzed the neck motor system of the blowfly to understand binocular integration of visual motion information underlying a head optomotor response. We identified and characterized two cervical nerve motor neurons (called CNMN6 and CNMN7) tuned precisely to an optic flow corresponding to pitch movements of the head. By means of double recordings and dye coupling, we determined that these neurons are connected ipsilaterally to two vertical system cells (VS2 and VS3), and contralaterally to one horizontal system cell (HSS). In addition, CNMN7 turned out to be connected to the ipsilateral CNMN6 and to its contralateral counterpart. To analyze a potential function of this circuit, we performed behavioral experiments and found that the optomotor pitch response of the fly head was only observable when both eyes were intact. Thus, this neural circuit performs two visuomotor transformations: first, by integrating binocular visual information it enhances the tuning to the optic flow resulting from pitch movements of the head, and second it could assure an even head declination by coordinating the activity of the CNMN7 neurons on both sides.

Binocular interactions underlying the classic optomotor responses of flying flies

In response to imposed course deviations, the optomotor reactions of animals reduce motion blur and facilitate the maintenance of stable body posture. In flies, many anatomical and electrophysiological studies suggest that disparate motion cues stimulating the left and right eyes are not processed in isolation but rather are integrated in the brain to produce a cohesive panoramic percept. To investigate the strength of such inter-ocular interactions and their role in compensatory sensory–motor transformations, we utilize a virtual reality flight simulator to record wing and head optomotor reactions by tethered flying flies in response to imposed binocular rotation and monocular front-to-back and back-to-front motion. Within a narrow range of stimulus parameters that generates large contrast insensitive optomotor responses to binocular rotation, we find that responses to monocular front-to-back motion are larger than those to panoramic rotation, but are contrast sensitive. Conversely, responses to monocular back-to-front motion are slower than those to rotation and peak at the lowest tested contrast. Together our results suggest that optomotor responses to binocular rotation result from the influence of non-additive contralateral inhibitory as well as excitatory circuit interactions that serve to confer contrast insensitivity to flight behaviors influenced by rotatory optic flow.

Relating neuronal to behavioral performance: variability of optomotor responses in the blowfly

Behavioral responses of an animal vary even when they are elicited by the same stimulus. This variability is due to stochastic processes within the nervous system and to the changing internal states of the animal. To what extent does the variability of neuronal responses account for the overall variability at the behavioral level? To address this question we evaluate the neuronal variability at the output stage of the blowfly's (Calliphora vicina) visual system by recording from motion-sensitive interneurons mediating head optomotor responses. By means of a simple modelling approach representing the sensory-motor transformation, we predict head movements on the basis of the recorded responses of motion-sensitive neurons and compare the variability of the predicted head movements with that of the observed ones. Large gain changes of optomotor head movements have previously been shown to go along with changes in the animals' activity state. Our modelling approach substantiates that these gain changes are imposed downstream of the motion-sensitive neurons of the visual system. Moreover, since predicted head movements are clearly more reliable than those actually observed, we conclude that substantial variability is introduced downstream of the visual system.

Flight activity alters velocity tuning of fly motion-sensitive neurons

Sensory neurons are mostly studied in fixed animals, but their response properties might change when the animal is free to move. Indeed, recent studies found differences between responses of sensory neurons in resting versus moving insects. Since the dynamic range of visual motion stimuli strongly depends on the speed at which an animal is moving, we investigated whether the visual system adapts to such changes in stimulus dynamics as induced by self-motion. Lobula plate tangential cells of flies lend themselves well to study this question because they are known to code for ego-motion based on optic-flow. We recorded the responses of the lobula plate tangential cell H1 to a visual pattern moving at different velocities under three different conditions: fixed flies before and after application of the octopamine agonist chlordimeform (CDM) and tethered flying flies. CDM has been previously shown to induce arousal in flies. We found that flying as well as the application of CDM significantly broadens the velocity tuning of H1 toward higher velocities.

Candidate glutamatergic neurons in the visual system of Drosophila

The visual system of Drosophila contains approximately 60,000 neurons that are organized in parallel, retinotopically arranged columns. A large number of these neurons have been characterized in great anatomical detail. However, studies providing direct evidence for synaptic signaling and the neurotransmitter used by individual neurons are relatively sparse. Here we present a first layout of neurons in the Drosophila visual system that likely release glutamate as their major neurotransmitter. We identified 33 different types of neurons of the lamina, medulla, lobula and lobula plate. Based on the previous Golgi-staining analysis, the identified neurons are further classified into 16 major subgroups representing lamina monopolar (L), transmedullary (Tm), transmedullary Y (TmY), Y, medulla intrinsic (Mi, Mt, Pm, Dm, Mi Am), bushy T (T), translobula plate (Tlp), lobula intrinsic (Lcn, Lt, Li), lobula plate tangential (LPTCs) and lobula plate intrinsic (LPi) cell types. In addition, we found 11 cell types that were not described by the previous Golgi analysis. This classification of candidate glutamatergic neurons fosters the future neurogenetic dissection of information processing in circuits of the fly visual system.

Polarization-sensitive descending neurons in the locust: connecting the brain to thoracic ganglia

Many animal species, in particular insects, exploit the E-vector pattern of the blue sky for sun compass navigation. Like other insects, locusts detect dorsal polarized light via photoreceptors in a specialized dorsal rim area of the compound eye. Polarized light information is transmitted through several processing stages to the central complex, a brain area involved in the control of goal-directed orientation behavior. To investigate how polarized light information is transmitted to thoracic motor circuits, we studied the responses of locust descending neurons to polarized light. Three sets of polarization-sensitive descending neurons were characterized through intracellular recordings from axonal fibers in the neck connectives combined with single-cell dye injections. Two descending neurons from the brain, one with ipsilaterally and the second with contralaterally descending axon, are likely to bridge the gap between polarization-sensitive neurons in the brain and thoracic motor centers. In both neurons, E-vector tuning changed linearly with daytime, suggesting that they signal time-compensated spatial directions, an important prerequisite for navigation using celestial signals. The third type connects the suboesophageal ganglion with the prothoracic ganglion. It showed no evidence for time compensation in E-vector tuning and might play a role in flight stabilization and control of head movements.

Neurons with cholinergic phenotype in the visual system of Drosophila

The optic lobe of Drosophila houses about 60,000 neurons that are organized in parallel, retinotopically arranged columns. Based on the Golgi-staining method, Fischbach and Dittrich ([1989] Cell Tissue Res 258:441-475) determined that each column contains about 90 identified cells. Each of these cells is supposed to release one or two different neurotransmitters. However, for most cells the released neurotransmitter is not known. Here we characterize the vast majority of the neurons in the Drosophila optic lobe that release acetylcholine (Ach), the major excitatory neurotransmitter of the insect central nervous system. We employed a promoter specific for cholinergic neurons and restricted its activity to single or a few cells using the MARCM technique. This approach allowed us to establish an anatomical map of neurons with a cholinergic phenotype based on their branching pattern. We identified 43 different types of neurons with a cholinergic phenotype. Thirty-one of them match previously described members of nine different subgroups: Transmedullary (Tm), Transmedullary Y (TmY), Medulla intrinsic (Mi, Mt, and Pm), Bushy T (T), Translobula Plate (Tlp), and Lobula intrinsic (Lcn and Lt) neurons (Fischbach and Dittrich [1989]). Intriguingly, 12 newly identified cell types suggest that previous Golgi studies were not saturating and that the actual number of different neurons per column is higher than previously thought. This study and similar ones on other neurotransmitter systems will contribute towards a columnar wiring diagram and foster the functional dissection of the visual circuitry in Drosophila.

Neural action fields for optic flow based navigation: a simulation study of the fly lobula plate network

Optic flow based navigation is a fundamental way of visual course control described in many different species including man. In the fly, an essential part of optic flow analysis is performed in the lobula plate, a retinotopic map of motion in the environment. There, the so-called lobula plate tangential cells possess large receptive fields with different preferred directions in different parts of the visual field. Previous studies demonstrated an extensive connectivity between different tangential cells, providing, in principle, the structural basis for their large and complex receptive fields. We present a network simulation of the tangential cells, comprising most of the neurons studied so far (22 on each hemisphere) with all the known connectivity between them. On their dendrite, model neurons receive input from a retinotopic array of Reichardt-type motion detectors. Model neurons exhibit receptive fields much like their natural counterparts, demonstrating that the connectivity between the lobula plate tangential cells indeed can account for their complex receptive field structure. We describe the tuning of a model neuron to particular types of ego-motion (rotation as well as translation around/along a given body axis) by its ‘action field’. As we show for model neurons of the vertical system (VS-cells), each of them displays a different type of action field, i.e., responds maximally when the fly is rotating around a particular body axis. However, the tuning width of the rotational action fields is relatively broad, comparable to the one with dendritic input only. The additional intra-lobula-plate connectivity mainly reduces their translational action field amplitude, i.e., their sensitivity to translational movements along any body axis of the fly.

Sparse but specific temporal coding by spikes in an insect sensory-motor ocellar pathway

We investigate coding in a locust brain neuron, DNI, which transforms graded synaptic input from ocellar L-neurons into axonal spikes that travel to excite particular thoracic flight neurons. Ocellar neurons are naturally stimulated by fluctuations in light collected from a wide field of view, for example when the visual horizon moves up and down. We used two types of stimuli: fluctuating light from a light-emitting diode (LED), and a visual horizon displayed on an electrostatic monitor. In response to randomly fluctuating light stimuli delivered from the LED, individual spikes in DNI occur sparsely but are timed to sub-millisecond precision, carrying substantial information: 4.5–7 bits per spike in our experiments. In response to these light stimuli, the graded potential signal in DNI carries considerably less information than in presynaptic L-neurons. DNI is excited in phase with either sinusoidal light from an LED or a visual horizon oscillating up and down at 20 Hz, and changes in mean light level or mean horizon level alter the timing of excitation for each cycle. DNI is a multimodal interneuron, but its ability to time spikes precisely in response to ocellar stimulation is not degraded by additional excitation. We suggest that DNI is part of an optical proprioceptor system, responding to the optical signal induced in the ocelli by nodding movements of the locust head during each wing-beat.

Coding efficiency of fly motion processing is set by firing rate, not firing precision

To comprehend the principles underlying sensory information processing, it is important to understand how the nervous system deals with various sources of perturbation. Here, we analyze how the representation of motion information in the fly's nervous system changes with temperature and luminance. Although these two environmental variables have a considerable impact on the fly's nervous system, they do not impede the fly to behave suitably over a wide range of conditions. We recorded responses from a motion-sensitive neuron, the H1-cell, to a time-varying stimulus at many different combinations of temperature and luminance. We found that the mean firing rate, but not firing precision, changes with temperature, while both were affected by mean luminance. Because we also found that information rate and coding efficiency are mainly set by the mean firing rate, our results suggest that, in the face of environmental perturbations, the coding efficiency is improved by an increase in the mean firing rate, rather than by an increased firing precision.

Motion adaptation and the velocity coding of natural scenes

Estimating relative velocity in the natural environment is challenging because natural scenes vary greatly in contrast and spatial structure. Widely accepted correlation-based models for elementary motion detectors (EMDs) are sensitive to contrast and spatial structure and consequently generate ambiguous estimates of velocity. Identified neurons in the third optic lobe of the hoverfly can reliably encode the velocity of natural images largely independent of contrast, despite receiving inputs directly from arrays of such EMDs. This contrast invariance suggests an important role for additional neural processes in robust encoding of image motion. However, it remains unclear which neural processes are contributing to contrast invariance. By recording from horizontal system neurons in the hoverfly lobula, we show two activity-dependent adaptation mechanisms acting as near-ideal normalizers for images of different contrasts that would otherwise produce highly variable response magnitudes. Responses to images that are initially weak neural drivers are boosted over several hundred milliseconds. Responses to images that are initially strong neural drivers are reduced over longer time scales. These adaptation mechanisms appear to be matched to higher-order natural image statistics reconciling the neurons' accurate encoding of image velocity with the inherent ambiguity of correlation-based motion detectors.

Robust coding of ego-motion in descending neurons of the fly

In many species, motion-sensitive neurons responding to optic flow at higher processing stages are well characterized; however, less is known how this representation of ego-motion is further transformed into an appropriate motor response. Here, we analyzed in the blowfly Calliphora vicina the visuomotor transformation from motion-sensitive neurons in the lobula plate [V2 and vertical system (VS) cells] onto premotor descending neurons [descending neurons of the ocellar and vertical system (DNOVS) cells] feeding into the motor circuit of the fly thoracic ganglion. We found that each of these cells is tuned to rotation of the fly around a particular body axis. Comparing the responses of presynaptic and postsynaptic cells revealed that DNOVS cells have approximately the same tuning widths as V2 and VS cells. However, DNOVS signals cells are less corrupted by fluctuations arising from the spatial structure of the visual input than their presynaptic elements. This leads to a more robust representation of ego-motion at the level of descending neurons. Thus, when moving from lobula plate cells to descending neurons, the selectivity for a particular optic flow remains unaltered, but the robustness of the representation increases.

Nonlinear integration of visual and haltere inputs in fly neck motor neurons

Animals use information from multiple sensory organs to generate appropriate behavior. Exactly how these different sensory inputs are fused at the motor system is not well understood. Here we study how fly neck motor neurons integrate information from two well characterized sensory systems: visual information from the compound eye and gyroscopic information from the mechanosensory halteres. Extracellular recordings reveal that a subpopulation of neck motor neurons display "gating-like" behavior: they do not fire action potentials in response to visual stimuli alone but will do so if the halteres are coactivated. Intracellular recordings show that these motor neurons receive small, sustained subthreshold visual inputs in addition to larger inputs that are phase locked to haltere movements. Our results suggest that the nonlinear gating-like effect results from summation of these two inputs with the action potential threshold providing the nonlinearity. As a result of this summation, the sustained visual depolarization is transformed into a temporally structured train of action potentials synchronized to the haltere beating movements. This simple mechanism efficiently fuses two different sensory signals and may also explain the context-dependent effects of visual inputs on fly behavior.

Local and global motion preferences in descending neurons of the fly

For a moving animal, optic flow is an important source of information about its ego-motion. In flies, the processing of optic flow is performed by motion sensitive tangential cells in the lobula plate. Amongst them, cells of the vertical system (VS cells) have receptive fields with similarities to optic flows generated during rotations around different body axes. Their output signals are further processed by pre-motor descending neurons. Here, we investigate the local motion preferences of two descending neurons called descending neurons of the ocellar and vertical system (DNOVS1 and DNOVS2). Using an LED arena subtending 240° × 95° of visual space, we mapped the receptive fields of DNOVS1 and DNOVS2 as well as those of their presynaptic elements, i.e. VS cells 1–10 and V2. The receptive field of DNOVS1 can be predicted in detail from the receptive fields of those VS cells that are most strongly coupled to the cell. The receptive field of DNOVS2 is a combination of V2 and VS cells receptive fields. Predicting the global motion preferences from the receptive field revealed a linear spatial integration in DNOVS1 and a superlinear spatial integration in DNOVS2. In addition, the superlinear integration of V2 output is necessary for DNOVS2 to differentiate between a roll rotation and a lift translation of the fly.

State-dependent performance of optic-flow processing interneurons

Active locomotive states are metabolically expensive and require efficient sensory processing both to avoid wasteful movements and to cope with an extended bandwidth of sensory stimuli. This is particularly true for flying animals because flight, as opposed to walking or resting, imposes a steplike increase in metabolism for the precise execution and control of movements. Sensory processing itself carries a significant metabolic cost, but the principles governing the adjustment of sensory processing to different locomotor states are not well understood. We use the blowfly as a model system to study the impact on visual processing of a neuromodulator, octopamine, which is known to be involved in the regulation of flight physiology. We applied an octopamine agonist and recorded the directional motion responses of identified visual interneurons known to process self-motion-induced optic flow to directional motion stimuli. The neural response range of these neurons is increased and the response latency is reduced. We also found that, due to an elevated spontaneous spike rate, the cells' negative signaling range is increased. Meanwhile, the preferred self-motion parameters the cells encode were state independent. Our results indicate that in the blowfly energetically expensive sensory coding strategies, such as rapid, large responses, and high spontaneous spike activity could be adjusted by the neuromodulator octopamine, likely to save energy during quiet locomotor states.

Drosophila's view on insect vision

Within the last 400 million years, insects have radiated into at least a million species, accounting for more than half of all known living organisms: they are the most successful group in the animal kingdom, found in almost all environments of the planet, ranging in body size from a mere 0.1 mm up to half a meter. Their eyes, together with the respective parts of the nervous system dedicated to the processing of visual information, have long been the subject of intense investigation but, with the exception of some very basic reflexes, it is still not possible to link an insect's visual input to its behavioral output. Fortunately for the field, the fruit fly Drosophila is an insect, too. This genetic workhorse holds great promise for the insect vision field, offering the possibility of recording, suppressing or stimulating any single neuron in its nervous system. Here, I shall give a brief synopsis of what we currently know about insect vision, describe the genetic toolset available in Drosophila and give some recent examples of how the application of these tools have furthered our understanding of color and motion vision in Drosophila.

Visuomotor transformation in the fly gaze stabilization system

For sensory signals to control an animal's behavior, they must first be transformed into a format appropriate for use by its motor systems. This fundamental problem is faced by all animals, including humans. Beyond simple reflexes, little is known about how such sensorimotor transformations take place. Here we describe how the outputs of a well-characterized population of fly visual interneurons, lobula plate tangential cells (LPTCs), are used by the animal's gaze-stabilizing neck motor system. The LPTCs respond to visual input arising from both self-rotations and translations of the fly. The neck motor system however is involved in gaze stabilization and thus mainly controls compensatory head rotations. We investigated how the neck motor system is able to selectively extract rotation information from the mixed responses of the LPTCs. We recorded extracellularly from fly neck motor neurons (NMNs) and mapped the directional preferences across their extended visual receptive fields. Our results suggest that-like the tangential cells-NMNs are tuned to panoramic retinal image shifts, or optic flow fields, which occur when the fly rotates about particular body axes. In many cases, tangential cells and motor neurons appear to be tuned to similar axes of rotation, resulting in a correlation between the coordinate systems the two neural populations employ. However, in contrast to the primarily monocular receptive fields of the tangential cells, most NMNs are sensitive to visual motion presented to either eye. This results in the NMNs being more selective for rotation than the LPTCs. Thus, the neck motor system increases its rotation selectivity by a comparatively simple mechanism: the integration of binocular visual motion information.