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

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.

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.

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.

Persistent Firing and Adaptation in Optic-Flow-Sensitive Descending Neurons

A general principle of sensory systems is that they adapt to prolonged stimulation by reducing their response over time. Indeed, in many visual systems, including higher-order motion sensitive neurons in the fly optic lobes and the mammalian visual cortex, a reduction in neural activity following prolonged stimulation occurs. In contrast to this phenomenon, the response of the motor system controlling flight maneuvers persists following the offset of visual motion. It has been suggested that this gap is caused by a lingering calcium signal in the output synapses of fly optic lobe neurons. However, whether this directly affects the responses of the post-synaptic descending neurons, leading to the observed behavioral output, is not known. We use extracellular electrophysiology to record from optic-flow-sensitive descending neurons in response to prolonged wide-field stimulation. We find that, as opposed to most sensory and visual neurons, and in particular to the motion vision sensitive neurons in the brains of both flies and mammals, the descending neurons show little adaption during stimulus motion. In addition, we find that the optic-flow-sensitive descending neurons display persistent firing, or an after-effect, following the cessation of visual stimulation, consistent with the lingering calcium signal hypothesis. However, if the difference in after-effect is compensated for, subsequent presentation of stimuli in a test-adapt-test paradigm reveals adaptation to visual motion. Our results thus show a combination of adaptation and persistent firing in the neurons that project to the thoracic ganglia and thereby control behavioral output.

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.

High throughput, rapid receptive field estimation for global motion sensitive neurons using a contiguous motion noise stimulus

BACKGROUND

The systematic characterization of receptive fields (RF) is essential for understanding visual motion processing. The performance of RF estimation depends on the employed stimuli, the complexity of the encoded features, and the quality of the activity readout. Calcium imaging is an attractive readout method for high-throughput neuronal activity recordings. However, calcium recordings are oftentimes noisy and of low temporal resolution. The RF estimation of neurons sensitive to global motion is particularly challenging due to their potentially complex combination of preferred directions across visual field positions.

NEW METHOD

Here, we present a novel noise stimulus, which is enriched with spatiotemporally contiguous motion and thus triggers robust calcium responses. We combined this contiguous motion noise (CMN) stimulus with reverse correlation followed by a two-step nonparametric cluster-based bootstrapping test for efficient and reliable RF estimation.

RESULTS

The in silico evaluation of our approach showed that RF centre positions and preferred directions are reliably detected in most of the simulated neurons. Suppressive RF components were detected in 40% of the simulated neurons. We successfully applied our approach to estimate the RFs of 163 motion-sensitive neurons in vivo within 40 min in the pretectum of zebrafish. Many in vivo neurons were sensitive to elaborate directional flow fields in their RFs.

COMPARISON WITH EXISTING METHODS

Our approach outperforms white noise methods and others due to the optimized motion stimulus statistics and ascertainable fine RF structures.

CONCLUSIONS

The CMN method enables efficient, non-biased RF estimation and will benefit systematic high-throughput investigations of RFs using calcium imaging.

Integration of Small- and Wide-Field Visual Features in Target-Selective Descending Neurons of both Predatory and Nonpredatory Dipterans

For many animals, target motion carries high ecological significance as this may be generated by a predator, prey, or potential mate. Indeed, animals whose survival depends on early target detection are often equipped with a sharply tuned visual system, yielding robust performance in challenging conditions. For example, many fast-flying insects use visual cues for identifying targets, such as prey (e.g., predatory dragonflies and robberflies) or conspecifics (e.g., nonpredatory hoverflies), and can often do so against self-generated background optic flow. Supporting these behaviors, the optic lobes of insects that pursue targets harbor neurons that respond robustly to the motion of small moving objects, even when displayed against syn-directional background clutter. However, in diptera, the encoding of target information by the descending neurons, which are more directly involved in generating the behavioral output, has received less attention. We characterized target-selective neurons by recording in the ventral nerve cord of male and female predatory Holcocephala fusca robberflies and of male nonpredatory Eristalis tenax hoverflies. We show that both species have dipteran target-selective descending neurons that only respond to target motion if the background is stationary or moving slowly, moves in the opposite direction, or has un-naturalistic spatial characteristics. The response to the target is suppressed when background and target move at similar velocities, which is strikingly different to the response of target neurons in the optic lobes. As the neurons we recorded from are premotor, our findings affect our interpretation of the neurophysiology underlying target-tracking behaviors.

SIGNIFICANCE STATEMENT Many animals use sensory cues to detect moving targets that may represent predators, prey, or conspecifics. For example, birds of prey show superb sensitivity to the motion of small prey, and intercept these at high speeds. In a similar manner, predatory insects visually track moving prey, often against cluttered backgrounds. Accompanying this behavior, the brains of insects that pursue targets contain neurons that respond exclusively to target motion. We here show that dipteran insects also have target-selective descending neurons in the part of their nervous system that corresponds to the vertebrate spinal cord. Surprisingly, and in contrast to the neurons in the brain, these premotor neurons are inhibited by background patterns moving in the same direction as the target.

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

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

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.

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.

Motion as a source of environmental information: a fresh view on biological motion computation by insect brains

Despite their miniature brains insects, such as flies, bees and wasps, are able to navigate by highly erobatic flight maneuvers in cluttered environments. They rely on spatial information that is contained in the retinal motion patterns induced on the eyes while moving around ("optic flow") to accomplish their extraordinary performance. Thereby, they employ an active flight and gaze strategy that separates rapid saccade-like turns from translatory flight phases where the gaze direction is kept largely constant. This behavioral strategy facilitates the processing of environmental information, because information about the distance of the animal to objects in the environment is only contained in the optic flow generated by translatory motion. However, motion detectors as are widespread in biological systems do not represent veridically the velocity of the optic flow vectors, but also reflect textural information about the environment. This characteristic has often been regarded as a limitation of a biological motion detection mechanism. In contrast, we conclude from analyses challenging insect movement detectors with image flow as generated during translatory locomotion through cluttered natural environments that this mechanism represents the contours of nearby objects. Contrast borders are a main carrier of functionally relevant object information in artificial and natural sceneries. The motion detection system thus segregates in a computationally parsimonious way the environment into behaviorally relevant nearby objects and-in many behavioral contexts-less relevant distant structures. Hence, by making use of an active flight and gaze strategy, insects are capable of performing extraordinarily well even with a computationally simple motion detection mechanism.

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.

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.

Tracking improves performance of biological collision avoidance models

Collision avoidance models derived from the study of insect brains do not perform universally well in practical collision scenarios, although the insects themselves may perform well in similar situations. In this article, we present a detailed simulation analysis of two well-known collision avoidance models and illustrate their limitations. In doing so, we present a novel continuous-time implementation of a neuronally based collision avoidance model. We then show that visual tracking can improve performance of these models by allowing an relative computation of the distance between the obstacle and the observer. We compare the results of simulations of the two models with and without tracking to show how tracking improves the ability of the model to detect an imminent collision. We present an implementation of one of these models processing imagery from a camera to show how it performs in real-world scenarios. These results suggest that insects may track looming objects with their gaze.

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.

Bio-inspired visual ego-rotation sensor for MAVs

Flies are capable of extraordinary flight maneuvers at very high speeds largely due to their highly elaborate visual system. In this work we present a fly-inspired FPGA based sensor system able to visually sense rotations around different body axes, for use on board micro aerial vehicles (MAVs). Rotation sensing is performed analogously to the fly's VS cell network using zero-crossing detection. An additional key feature of our system is the ease of adding new functionalities akin to the different tasks attributed to the fly's lobula plate tangential cell network, such as object avoidance or collision detection. Our implementation consists of a modified eneo SC-MVC01 SmartCam module and a custom built circuit board, weighing less than 200 g and consuming less than 4 W while featuring 57,600 individual two-dimensional elementary motion detectors, a 185° field of view and a frame rate of 350 frames per second. This makes our sensor system compact in terms of size, weight and power requirements for easy incorporation into MAV platforms, while autonomously performing all sensing and processing on-board and in real time.

An olfactory circuit increases the fidelity of visual behavior

Multimodal integration allows neural circuits to be activated in a behaviorally context-specific manner. In the case of odor plume tracking by Drosophila, an attractive odorant increases the influence of yaw-optic flow on steering behavior in flight, which enhances visual stability reflexes, resulting in straighter flight trajectories within an odor plume. However, it is not well understood whether context-specific changes in optomotor behavior are the result of an increased sensitivity to motion inputs (e.g., through increased visual attention) or direct scaling of motor outputs (i.e., increased steering gain). We address this question by examining the optomotor behavior of Drosophila melanogaster in a tethered flight assay and demonstrate that whereas olfactory cues decrease the gain of the optomotor response to sideslip optic flow, they concomitantly increase the gain of the yaw optomotor response by enhancing the animal's ability to follow transient visual perturbations. Furthermore, ablating the mushroom bodies (MBs) of the fly brain via larval hydroxyurea (HU) treatment results in a loss of olfaction-dependent increase in yaw optomotor fidelity. By expressing either tetanus toxin light chain or diphtheria toxin in gal4-defined neural circuits, we were able to replicate the loss of function observed in the HU treatment within the lines expressing broadly in the mushroom bodies, but not within specific mushroom body lobes. Finally, we were able to genetically separate the yaw responses and sideslip responses in our behavioral assay. Together, our results implicate the MBs in a fast-acting, memory-independent olfactory modification of a visual reflex that is critical for flight control.

Visual response properties of neck motor neurons in the honeybee

Recent behavioural studies have demonstrated that honeybees use visual feedback to stabilize their gaze. However, little is known about the neural circuits that perform the visual motor computations that underlie this ability. We investigated the motor neurons that innervate two neck muscles (m44 and m51), which produce stabilizing yaw movements of the head. Intracellular recordings were made from five (out of eight) identified neuron types in the first cervical nerve (IK1) of honeybees. Two motor neurons that innervate muscle 51 were found to be direction-selective, with a preference for horizontal image motion from the contralateral to the ipsilateral side of the head. Three neurons that innervate muscle 44 were tuned to detect motion in the opposite direction (from ipsilateral to contralateral). These cells were binocularly sensitive and responded optimally to frontal stimulation. By combining the directional tuning of the motor neurons in an opponent manner, the neck motor system would be able to mediate reflexive optomotor head turns in the direction of image motion, thus stabilising the retinal image. When the dorsal ocelli were covered, the spontaneous activity of neck motor neurons increased and visual responses were modified, suggesting an ocellar input in addition to that from the compound eyes.

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.

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.

Sensorimotor transformation: from visual responses to motor commands

A recent study reveals how vision-based estimates of self-motion are passed on to premotor descending neurons which connect to various motor centres in the fly nervous system.

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.