Performance of fly visual interneurons during object fixation

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

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

Spatio-temporal dynamics of impulse responses to figure motion in optic flow neurons

White noise techniques have been used widely to investigate sensory systems in both vertebrates and invertebrates. White noise stimuli are powerful in their ability to rapidly generate data that help the experimenter decipher the spatio-temporal dynamics of neural and behavioral responses. One type of white noise stimuli, maximal length shift register sequences (m-sequences), have recently become particularly popular for extracting response kernels in insect motion vision. We here use such m-sequences to extract the impulse responses to figure motion in hoverfly lobula plate tangential cells (LPTCs). Figure motion is behaviorally important and many visually guided animals orient towards salient features in the surround. We show that LPTCs respond robustly to figure motion in the receptive field. The impulse response is scaled down in amplitude when the figure size is reduced, but its time course remains unaltered. However, a low contrast stimulus generates a slower response with a significantly longer time-to-peak and half-width. Impulse responses in females have a slower time-to-peak than males, but are otherwise similar. Finally we show that the shapes of the impulse response to a figure and a widefield stimulus are very similar, suggesting that the figure response could be coded by the same input as the widefield response.

Method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in Drosophila

A moving visual figure may contain first-order signals defined by variation in mean luminance, as well as second-order signals defined by constant mean luminance and variation in luminance envelope, or higher-order signals that cannot be estimated by taking higher moments of the luminance distribution. Separating these properties of a moving figure to experimentally probe the visual subsystems that encode them is technically challenging and has resulted in debated mechanisms of visual object detection by flies. Our prior work took a white noise systems identification approach using a commercially available electronic display system to characterize the spatial variation in the temporal dynamics of two distinct subsystems for first- and higher-order components of visual figure tracking. The method relied on the use of single pixel displacements of two visual stimuli according to two binary maximum length shift register sequences (m-sequences) and cross-correlation of each m-sequence with time-varying flight steering measurements. The resultant spatio-temporal action fields represent temporal impulse responses parameterized by the azimuthal location of the visual figure, one STAF for first-order and another for higher-order components of compound stimuli. Here we review m-sequence and reverse correlation procedures, then describe our application in detail, provide Matlab code, validate the STAFs, and demonstrate the utility and robustness of STAFs by predicting the results of other published experimental procedures. This method has demonstrated how two relatively modest innovations on classical white noise analysis—the inclusion of space as a way to organize response kernels and the use of linear decoupling to measure the response to two channels of visual information simultaneously—could substantially improve our basic understanding of visual processing in the fly.

Impact of stride-coupled gaze shifts of walking blowflies on the neuronal representation of visual targets

During locomotion animals rely heavily on visual cues gained from the environment to guide their behavior. Examples are basic behaviors like collision avoidance or the approach to a goal. The saccadic gaze strategy of flying flies, which separates translational from rotational phases of locomotion, has been suggested to facilitate the extraction of environmental information, because only image flow evoked by translational self-motion contains relevant distance information about the surrounding world. In contrast to the translational phases of flight during which gaze direction is kept largely constant, walking flies experience continuous rotational image flow that is coupled to their stride-cycle. The consequences of these self-produced image shifts for the extraction of environmental information are still unclear. To assess the impact of stride-coupled image shifts on visual information processing, we performed electrophysiological recordings from the HSE cell, a motion sensitive wide-field neuron in the blowfly visual system. This cell has been concluded to play a key role in mediating optomotor behavior, self-motion estimation and spatial information processing. We used visual stimuli that were based on the visual input experienced by walking blowflies while approaching a black vertical bar. The response of HSE to these stimuli was dominated by periodic membrane potential fluctuations evoked by stride-coupled image shifts. Nevertheless, during the approach the cell's response contained information about the bar and its background. The response components evoked by the bar were larger than the responses to its background, especially during the last phase of the approach. However, as revealed by targeted modifications of the visual input during walking, the extraction of distance information on the basis of HSE responses is much impaired by stride-coupled retinal image shifts. Possible mechanisms that may cope with these stride-coupled responses are discussed.

Gaze characteristics of freely walking blowflies Calliphora vicina in a goal-directed task

In contrast to flying flies, walking flies experience relatively strong rotational gaze shifts, even during overall straight phases of locomotion. These gaze shifts are caused by the walking apparatus and modulated by the stride frequency. Accordingly, even during straight walking phases, the retinal image flow is composed of both translational and rotational optic flow, which might affect spatial vision, as well as fixation behavior. We addressed this issue for an orientation task where walking blowflies approached a black vertical bar. The visual stimulus was stationary, or either the bar or the background moved horizontally. The stride-coupled gaze shifts of flies walking toward the bar had similar amplitudes under all visual conditions tested. This finding indicates that these shifts are an inherent feature of walking, which are not even compensated during a visual goal fixation task. By contrast, approaching flies showed a frequent stop-and-go behavior that was affected by the stimulus conditions. As sustained image rotations may impair distance estimation during walking, we propose a hypothesis that explains how rotation-independent translatory image flow containing distance information can be determined. The algorithm proposed works without requiring differentiation at the behavioral level of the rotational and translational flow components. By contrast, disentangling both has been proposed to be necessary during flight. By comparing the retinal velocities of the edges of the goal, its rotational image motion component can be removed. Consequently, the expansion velocity of the goal and, thus, its proximity can be extracted, irrespective of distance-independent stride-coupled rotational image shifts.

Higher-order figure discrimination in fly and human vision

Visually-guided animals rely on their ability to stabilize the panorama and simultaneously track salient objects, or figures, that are distinct from the background in order to avoid predators, pursue food resources and mates, and navigate spatially. Visual figures are distinguished by luminance signals that produce coherent motion cues as well as more enigmatic 'higher-order' statistical features. Figure discrimination is thus a complex form of motion vision requiring specialized neural processing. In this minireview, we will highlight recent advances in understanding the perceptual, behavioral, and neurophysiological basis of higher-order figure detection in flies, much of which is grounded in the historical perspective and mechanistic underpinnings of human psychophysics.

Object representation and distance encoding in three-dimensional environments by a neural circuit in the visual system of the blowfly

Three motion-sensitive key elements of a neural circuit, presumably involved in processing object and distance information, were analyzed with optic flow sequences as experienced by blowflies in a three-dimensional environment. This optic flow is largely shaped by the blowfly's saccadic flight and gaze strategy, which separates translational flight segments from fast saccadic rotations. By modifying this naturalistic optic flow, all three analyzed neurons could be shown to respond during the intersaccadic intervals not only to nearby objects but also to changes in the distance to background structures. In the presence of strong background motion, the three types of neuron differ in their sensitivity for object motion. Object-induced response increments are largest in FD1, a neuron long known to respond better to moving objects than to spatially extended motion patterns, but weakest in VCH, a neuron that integrates wide-field motion from both eyes and, by inhibiting the FD1 cell, is responsible for its object preference. Small but significant object-induced response increments are present in HS cells, which serve both as a major input neuron of VCH and as output neurons of the visual system. In both HS and FD1, intersaccadic background responses decrease with increasing distance to the animal, although much more prominently in FD1. This strong dependence of FD1 on background distance is concluded to be the consequence of the activity of VCH that dramatically increases its activity and, thus, its inhibitory strength with increasing distance.

Neural specializations for small target detection in insects

Despite being equipped with low-resolution eyes and tiny brains, many insects show exquisite abilities to detect and pursue targets even in highly textured surrounds. Target tracking behavior is subserved by neurons that are sharply tuned to the motion of small high-contrast targets. These neurons respond robustly to target motion, even against self-generated optic flow. A recent model, supported by neurophysiology, generates target selectivity by being sharply tuned to the unique spatiotemporal profile associated with target motion. Target neurons are likely connected in a complex network where some provide more direct output to behavior, whereas others serve an inter-regulatory role. These interactions may regulate attention and aid in the robust detection of targets in clutter observed in behavior.

Drosophila fly straight by fixating objects in the face of expanding optic flow

Flies, like all animals that depend on vision to navigate through the world, must integrate the optic flow created by self-motion with the images generated by prominent features in their environment. Although much is known about the responses of Drosophila melanogaster to rotating flow fields, their reactions to the more complex patterns of motion that occur as they translate through the world are not well understood. In the present study we explore the interactions between two visual reflexes in Drosophila: object fixation and expansion avoidance. As a fly flies forward, it encounters an expanding visual flow field. However, recent results have demonstrated that Drosophila strongly turn away from patterns of expansion. Given the strength of this reflex, it is difficult to explain how flies make forward progress through a visual landscape. This paradox is partially resolved by the finding reported here that when undergoing flight directed towards a conspicuous object, Drosophila will tolerate a level of expansion that would otherwise induce avoidance. This navigation strategy allows flies to fly straight when orienting towards prominent visual features.

Feature detection and the hypercomplex property in insects

Discerning a target amongst visual 'clutter' is a complicated task that has been elegantly solved by flying insects, as evidenced by their mid-air interactions with conspecifics and prey. The neurophysiology of small-target motion detectors (STMDs) underlying these complex behaviors has recently been described and suggests that insects use mechanisms similar to those of hypercomplex cells of the mammalian visual cortex to achieve target-specific tuning. Cortical hypercomplex cells are end-stopped, which means that they respond optimally to small moving targets, with responses to extended bars attenuated. We review not only the underlying mechanisms involved in this tuning but also how recently proposed models provide a possible explanation for another remarkable property of these neurons - their ability to respond robustly to the motion of targets even against moving backgrounds.

Dynamic properties of large-field and small-field optomotor flight responses in Drosophila

Optomotor flight control in houseflies shows bandwidth fractionation such that steering responses to an oscillating large-field rotating panorama peak at low frequency, whereas responses to small-field objects peak at high frequency. In fruit flies, steady-state large-field translation generates steering responses that are three times larger than large-field rotation. Here, we examine the optomotor steering reactions to dynamically oscillating visual stimuli consisting of large-field rotation, large-field expansion, and small-field motion. The results show that, like in larger flies, large-field optomotor steering responses peak at low frequency, whereas small-field responses persist under high frequency conditions. However, in fruit flies large-field expansion elicits higher magnitude and tighter phase-locked optomotor responses than rotation throughout the frequency spectrum, which may suggest a further segregation within the large-field pathway. An analysis of wing beat frequency and amplitude reveals that mechanical power output during flight varies according to the spatial organization and motion dynamics of the visual scene. These results suggest that, like in larger flies, the optomotor control system is organized into parallel large-field and small-field pathways, and extends previous analyses to quantify expansion-sensitivity for steering reflexes and flight power output across the frequency spectrum.

Responses of blowfly motion-sensitive neurons to reconstructed optic flow along outdoor flight paths

The retinal image flow a blowfly experiences in its daily life on the wing is determined by both the structure of the environment and the animal's own movements. To understand the design of visual processing mechanisms, there is thus a need to analyse the performance of neurons under natural operating conditions. To this end, we recorded flight paths of flies outdoors and reconstructed what they had seen, by moving a panoramic camera along exactly the same paths. The reconstructed image sequences were later replayed on a fast, panoramic flight simulator to identified, motion sensitive neurons of the so-called horizontal system (HS) in the lobula plate of the blowfly, which are assumed to extract self-motion parameters from optic flow. We show that under real life conditions HS-cells not only encode information about self-rotation, but are also sensitive to translational optic flow and, thus, indirectly signal information about the depth structure of the environment. These properties do not require an elaboration of the known model of these neurons, because the natural optic flow sequences generate--at least qualitatively--the same depth-related response properties when used as input to a computational HS-cell model and to real neurons.

An elaborated model of fly small-target tracking

Flies have the capability to visually track small moving targets, even across cluttered backgrounds. Previous computational models, based on figure detection (FD) cells identified in the fly, have suggested how this may be accomplished at a neuronal level based on information about relative motion between the target and the background. We experimented with the use of this "small-field system model" for the tracking of small moving targets by a simulated fly in a cluttered environment and discovered some functional limitations. As a result of these experiments, we propose elaborations of the original small-field system model to support stronger effects of background motion on small-field responses, proper accounting for more complex optical flow fields, and more direct guidance toward the target. We show that the elaborated model achieves much better tracking performance than the original model in complex visual environments and discuss the biological implications of our elaborations. The elaborated model may help to explain recent electrophysiological data on FD cells that seem to contradict the original model.

Input organization of multifunctional motion-sensitive neurons in the blowfly

Flies rely heavily on visual motion cues for course control. This is mediated by a small set of motion-sensitive neurons called lobula plate tangential cells. A single class of these, the centrifugal horizontal (CH) neurons, play an important role in two pathways: figure-ground discrimination and flow-field selectivity. As was recently found, the dendrites of CH cells are electrically coupled with the dendritic tree of another class of neurons sensitive to horizontal image motion, the horizontal system (HS) cells. However, whether motion information arrives independently at both of these cells or is passed from one to the other is not known. Here, we examine the ipsilateral input circuitry to HS and CH neurons by selective laser ablation of individual interneurons. We find that the response of CH neurons to motion presented in front of the ipsilateral eye is entirely abolished after ablation of HS cells. In contrast, the motion response of HS cells persists after the ablation of CH cells. We conclude that HS cells receive direct motion input from local motion elements, whereas CH cells do not; their motion response is driven by HS cells. This connection scheme is discussed with reference to how the dendritic networks involved in figure-ground detection and flow-field selectivity might operate.

Visually guided orientation in flies: case studies in computational neuroethology

To understand the functioning of nervous systems and, in particular, how they control behaviour we must bridge many levels of complexity from molecules, cells and synapses to perception behaviour. Although experimental analysis is a precondition for understanding by nervous systems, it is in no way sufficient. The understanding is aided at all levels of complexity by modelling. Modelling proved to be an inevitable tool to test the experimentally established hypotheses. In this review it will by exemplified by three case studies that the appropriate level of modelling needs to be adjusted to the particular computational problems that are to be solved. (1) Specific features of the highly virtuosic pursuit behaviour of male flies can be understood on the basis of a phenomenological model that relates the visual input to the motor output. (2) The processing of retinal image motion as is experienced by freely moving animals can be understood on the basis of a model consisting of algorithmic components and components which represent a simple equivalent circuit of nerve cells. (3) Behaviourally relevant features of the reliability of encoding of visual motion information can be understood by modelling the transformation of postsynaptic potentials into sequences of spike trains.

Chasing a dummy target: smooth pursuit and velocity control in male blowflies

Male blowflies chase and catch other flies in fast acrobatic flights. To unravel the underlying control system, we presented a black moving sphere instead of a real fly as a pursuit target. By varying the size and speed of the target, we were able to systematically analyse the decisive visual determinants that guide chasing behaviour. Flies pursue targets of a wide range of sizes and velocities. The percentage of pursuits resulting in target capture decreases with increasing target size and speed. Chasing male flies adjust their forward velocity depending on the retinal size of the target, indicating that retinal size is a relevant input variable of the control system. The chasing fly focuses the target with great accuracy in the frontal part of its visual field by means of a smooth pursuit control system using the retinal position of the target to determine the flight direction. We conclude that for a comprehensive understanding of chasing control different time lags in the control systems of angular and forward velocity together with the impact of inertia on fly movements need to be taken into account.

Vision in flying insects

Vision guides flight behaviour in numerous insects. Despite their small brain, insects easily outperform current man-made autonomous vehicles in many respects. Examples are the virtuosic chasing manoeuvres male flies perform as part of their mating behaviour and the ability of bees to assess, on the basis of visual motion cues, the distance travelled in a novel environment. Analyses at both the behavioural and neuronal levels are beginning to unveil reasons for such extraordinary capabilities of insects. One recipe for their success is the adaptation of visual information processing to the specific requirements of the behavioural tasks and to the specific spatiotemporal properties of the natural input.

Fly flight: a model for the neural control of complex behavior

Flies exhibit a repertoire of aerial acrobatics unmatched in robustness and aerodynamic sophistication. The exquisite control of this complex behavior emerges from encoding intricate patterns of optic flow, and the translation of these visual signals into the mechanical language of the motor system. Recent advances in experimental design toward more naturalistic visual and mechanosensory stimuli have served to reinforce fly flight as a key model system for understanding how feedback from multiple sensory modalities is integrated to control complex and robust motor behaviors across taxa.