Evidence for ventral optic flow regulation in honeybees

To better grasp the visuomotor control system underlying insects' height and speed control, we attempted to interfere with this system by producing a major perturbation on the free flying insect and obsering the effect of this perturbation. Honeybees were trained to fly along a high-roofed tunnel, part of which was equipped with a moving floor. The bees followed the stationary part of the floor at a given height. On encountering the moving part of the floor, which moved in the same direction as their flight, honeybees descended and flew at a lower height. In so doing, bees gradually restored their ventral optic flow (OF) to a similar value to that they had perceived when flying over the stationary part of the floor. OF restoration therefore relied on lowering the groundheight rather than increasing the groundspeed. This result can be accounted for by the control system called an optic flow regulator that we proposed in previous studies. This visuo-motor control scheme explains how honeybees can navigate safely along tunnels on the sole basis of OF measurements, without any need to measure either their speed or the clearance from the ground, the roof or the surrounding walls

Le pilotage visuel chez l'abeille : expériences et modèle

Quand un insecte vole, l'image des objets à l'entour défile sur sa rétine. De nombreuses expériences ont montré que ce défilement angulaire (flux optique) joue un rôle majeur dans le contrôle du vol. Les insectes semblent maintenir le flux optique perçu à une valeur préférée, les conduisant à adopter une position et une vitesse "de sécurité". Nous avons élaboré un modèle basé sur le principe de "régulation du flux optique" proposé précédemment par notre laboratoire, et capable de rendre compte d'observations et de résultats d'expériences réalisées auparavant chez les insectes. Nous avons ensuite réalisé des expériences comportementales sur des abeilles en vol libre, en environnement contrôlé, visant à mettre en défaut le modèle proposé. Nos résultats révèlent un lien étroit entre flux optique ventral et hauteur de vol. Ils montrent aussi que l'abeille est sensible au flux optique dorsal et enfin que l'abeille adapte sa vitesse à l'encombrement de l'environnement dans les plans vertical et horizontal. Tous ces résultats étayent le modèle proposé. Cependant une expérience finale suggère qu'au delà de l'aspect "réflexe" du contrôle du vol, l'apprentissage joue un rôle et module le comportement de vol. Ceci impose d'inclure un élément "cognitif" au schéma proposé. Cette thèse décrit, pour la première fois sous la forme d'un schéma fonctionnel, les principes mis en œuvre dans le contrôle 3D du vol d'un insecte par le flux optique. Le caractère explicite du modèle proposé, d'une part ouvre la voie à de nouvelles expériences comportementales susceptibles de le mettre en défaut ou d'en préciser les limites, d'autre part le rend directement applicable à la robotique mobile, aérienne ou spatiale.When an insect flies in its environment, the image of the surrounding objects moves on its retina. Several studies have shown that this angular movement, called "optic flow", plays a major role in the insect flight control. Flying insects seem to maintain the perceived optic flow at a prefered value, which makes them choose a "safe" position and a "safe" speed. We first designed a model based on the "optic flow regulation principle" recently proposed at our laboratory, which can account for observations and results previously shown on insects. We then performed behavioral experiments using free flying bees in controlled environments, which aimed at refuting the proposed model. Our results show a direct link between the ventral optic flow and the flight height. They also show that the honeybee is sensitive to the dorsal optic flow and that the honeybee can adjust its speed according to the cluttering of the environment in both the vertical and horizontal planes. All these results support the proposed model. The results of a last experiment suggest, however, that beyond the "reflex" part of the flight control system, a learning process may play a role and modulate the flight behavior. This last point requires that a learning process be incorporated into the model. This thesis for the first time proposes an explicit and functional scheme based on optic flow, describing the principles involved in the 3D flight control system of an insect. This model suggests new behavioral experiments liable to fault it. Because this model is explicit, it may be directly implemented onboard aerial or spatial robots

3D Navigation With An Insect-Inspired Autopilot

ISBN : 978-2-9532965-0-1Using computer-simulation experiments, we developed a vision-based autopilot that enables a ‘simulated bee' to travel along a tunnel by controlling both its speed and its clearance from the right wall, the left wall, the ground, and the ceiling. The flying agent can translate along three directions (surge, sway, and heave): the agent is therefore fully actuated. The visuo-motor control system, called ALIS (AutopiLot using an Insect based vision System), is a dual OF regulator consisting of two interdependent feedback loops, each of which has its own OF set-point. The experiments show that the simulated bee navigates safely along a straight tunnel, while reacting sensibly to the major OF perturbation caused by the presence of a tapered tunnel. The visual system is minimalistic (only eight pixels) and it suffices to control the clearance from the four walls and the forward speed jointly, without the need to measure any speeds and distances. The OF sensors and the simple visuo-motor control system developed here are suitable for use on MAVs with avionic payloads as small as a few grams. Besides, the ALIS autopilot accounts remarkably for the quantitative results of ethological experiments performed on honeybees flying freely in straight or tapered corridors

A 3D insect-inspired visual autopilot for corridor-following

International audienceMotivated by the results of behavioral studies performed on bees over the last two decades, we have attempted to decipher the logics behind the bee's autopilot, with specific reference to their use of optic flow (OF). Using computer-simulation experiments, we developed a vision-based autopilot that enables a 'simulated bee' to travel along a tunnel by controlling both its speed and its clearance from the walls, the ground, and the ceiling. The flying agent is fully actuated and can translate along three directions: surge, sway, and heave. The visuo-motor control system, called ALIS (AutopiLot using an Insect based vision System), is a dual OF regulator consisting of intertwined feedback loops, each of which has its own OF set-point. The experiments show that the simulated bee navigates safely along a straight or tapered tunnel and reacts sensibly to major OF perturbations caused, e.g., by the lack of texture on one wall or by the presence of a tapered tunnel. The agent is equipped with a minimalistic visual system (comprised of only eight pixels) that suffices to control the clearance from the four walls and the forward speed jointly, without the need to measure any speeds and distances. The OF sensors and the simple visuo-motor control system developed here are suitable for use on MAVs with avionic payloads as small as a few grams. Besides, the ALIS autopilot accounts remarkably for the quantitative results of ethological experiments performed on honeybees flying freely in straight or tapered corridors

Decoding the retina with the first wave of spikes

International audienceUnderstanding how the retina encodes visual information remains an open question. Using MEAs on salamander retinas, Gollisch & Meister (2008) showed that the relative latencies between some neuron pairs carry sufficient information to identify the phase of square-wave gratings (,. Using gratings of varying phase, spatial frequency, and contrast on mouse retinas, we extended this idea by systematically considering the relative order of all spike latencies, i.e. the shape of the first wave of spikes after stimulus onset. The discrimination task was to identify the phase among gratings of identical spatial frequency. We compared the performance (fraction correct predictions) of our approach under classical Bayesian and LDA decoders to spike count and response latency of each recorded neuron. Best results were obtained for the lowest spatial frequency. There, results showed that the spike count discrimination performance was higher than for latency under both the Bayesian (0,95+-0,02 and 0,75+-0,11 respectively) and LDA (0,95+-0,01 and 0,62+-0,03 respectively) decoders. The first wave of spikes decoder is (0,46+-0,06) less efficient than the spike count. Nevertheless, it accounts for 50% of the overall performance. Interestingly, these results tend to confirm the rank order coding hypothesis (Thorpe & Gautrais, 1998)

The wave of first spikes provides robust spatial cues for retinal information processing

How a population of retinal ganglion cells (RGCs) encode the visual scene remains an open question. Several coding strategies have been investigated out of which two main views have emerged: considering RGCs as independent encoders or as synergistic encoders, i.e. when the concerted spiking in a RGC population carries more information than the sum of the information contained in the spiking of individual RGCs. Although the RGCs assumed as independent encode the main information, there is currently a growing body of evidence that considering RGCs as synergistic encoders provides complementary and more precise information. Based on salamander retina recordings, it has been suggested [11] that a code based on di erential spike latencies between RGC pairs could be a powerful mechanism. Here, we have tested this hypothesis in the mammalian retina. We recorded responses to stationary gratings from 469 RGCs in 5 mouse retinas. Interestingly, we did not nd any RGC pairs exhibiting clear latency correlations (presumably due to the presence of spontaneous activity), showing that individual RGC pairs do not provide su cient information in our conditions. However considering the whole RGC population, we show that the shape of the wave of rst spikes (WFS) successfully encodes for spatial cues. To quantify its coding capabilities, we performed a discrimination task and we showed that the WFS was more robust to the spontaneous ring than the absolute latencies are. We also investigated the impact of a post-processing neural layer. The recorded spikes were fed into an arti cial lateral geniculate nucleus (LGN) layer. We found that the WFS is not only preserved but even re ned through the LGN-like layer, while classical independent coding strategies become impaired. These ndings suggest that even at the level of the retina, the WFS provides a reliable strategy to encode spatial cues.Comment une population de cellules ganglionnaires de la rétine (RGC) encode la scène visuelle reste une question ouverte. Plusieurs stratégies de codage ont été étudiés à partir desquelles deux principales vues ont émergé: considérer les RGCs en tant que encodeurs indépendants ou en tant que encodeurs synergique; c'est à dire lorsque la réponse concertée dans une population de RGCs contient plus d'informations que la somme des informations contenues dans les réponses individuelles. Bien que en considérant les RGCs comme des encodeurs indépendants donne accès à l'information principale, il existe actuellement un nombre croissant de preuves qui montrent que considérer les RGCs comme des encodeurs synergiques fournit des informations complémentaires et plus précises. Basé sur des enregistrements de la rétine de salamandre, il a été suggéré [11] qu'un code basé sur les di érences entre les latences des paires de RGCs pourrait être un mécanisme puissant. Ici, nous avons testé cette hypothèse dans la rétine de mammifère. Nous avons enregistré les réponses de 469 RGCs de 5 rétines de souris. Fait intéressant, nous n'avons pas trouvé de paires de RGCs présentant des corrélations de latence claires (probablement en raison de la présence d'une forte activité spontanée). Cela montre que les paires de RGCs individuelles ne fournissent pas su samment d'informations dans nos conditions. Toutefois, en considérant la population de RGCs, nous avons montré que la forme de la première vague de potentiels d'action (WFS) code avec succès des indices spatiaux. Pour quanti er ses capacités de codage, nous avons réalisé une tâche de discrimination et nous avons montré que la WFS était plus robuste à l'activitée spontannée que les latences absolues. Nous avons également étudié l'impact du traitement par une couche de neurones. Les réponses enregistrées ont été introduits dans une couche de corps géniculé latéral arti ciel (LGN). Nous avons constaté que la WFS est non seulement préservée mais mÃame ra née à travers la couche LGN, tandis que les stratégies classiques de codage indépendant deviennent altérées. Ces résultats suggèrent que, même au niveau de la rétine, la WFS propose une stratégie able pour coder l'information visuelle

ENAS: A new software for spike train analysis and simulation

International audienceAs one gains more intuitions and results on the importance of concerted activity in spike trains, models are developed to extract potential canonical principles underlying spike coding. These methods shed a new light on spike train dynamics. However, they require time and expertise to be implemented efficiently, making them hard to use in a daily basis by neuroscientists or modelers. To bridge this gap, we developed the license free multiplatform software ENAS integrating tools for spike trains analysis and simulation. These tools are accessible through a friendly Graphical User Interface that avoids any scripting or writing code from user. Most of them have been implemented to run in parallel to reduce the time and memory consumption. One of the main strength of ENAS when compared to competing software is to provide statistical analysis with Maximum Entropy-Gibbs distributions taking into account both spatial and temporal correlations as constraints, allowing to introduce causality and memory in statistics. Conversely, given this analysis or other known statistics, ENAS one can also generate new spike trains. These methods result from a series of work and they have already been applied to the analysis of retina data. This comes in addition to basic visualizations and classical analysis for statistics of spike trains analysis. All these tools are generic and can be applied to any spike train. Interestingly, ENAS also includes specific tools dedicated for vision, and the retina in particular. For example, one can jointly visualize stimulus and spiking activity or estimate receptive fields. ENAS also includes a virtual retina simulator extending former Virtual Retina simulator to include lateral connections in the IPL. We hope that ENAS will become a useful tool for neuroscientists to analyse spike trains and we hope to improve it thanks to user feedback. Our goal is to progressively enrich ENAS with the latest research results, in order to facilitate transfer of new methods to the community. It is downloadable from https://enas.inria.fr where documentation, tutorials and samples of spike trains are available

Rank order coding: a retinal information decoding strategy revealed by large-scale multielectrode array retinal recordings

International audienceHow a population of retinal ganglion cells (RGCs) encodes the visual scene remains an open question. Going beyond individual RGC coding strategies, results in salamander suggest that the relative latencies of an RGC pair encodes spatial information. Thus a population code based on this concerted spiking could be a powerful mechanism to transmit visual information rapidly and efficiently. Here, we tested this hypothesis in mouse by recording simultaneous light-evoked responses from hundreds of RGCs, at pan-retinal level, using a new generation of large-scale, high density multielectrode array consisting of 4096 electrodes. Interestingly, we did not find any RGCs exhibiting a clear latency tuning to the stimuli, suggesting that in mouse, individual RGC pairs may not provide sufficient information. We show that a significant amount of information is encoded synergistically in the concerted spiking of large RGC populations. Thus, the RGC population response described with relative activities, or ranks, provides more relevant information than classical independent spike count- or latency- based codes. In particular, we report for the first time that when considering the relative activities across the whole population, the wave of first stimulus-evoked spikes (WFS) is an accurate indicator of stimulus content. We show that this coding strategy co-exists with classical neural codes, and that it is more efficient and faster. Overall, these novel observations suggest that already at the level of the retina, concerted spiking provides a reliable and fast strategy to rapidly transmit new visual scenes

Honeybees' Speed Depends on Dorsal as Well as Lateral, Ventral and Frontal Optic Flows

Flying insects use the optic flow to navigate safely in unfamiliar environments, especially by adjusting their speed and their clearance from surrounding objects. It has not yet been established, however, which specific parts of the optical flow field insects use to control their speed. With a view to answering this question, freely flying honeybees were trained to fly along a specially designed tunnel including two successive tapering parts: the first part was tapered in the vertical plane and the second one, in the horizontal plane. The honeybees were found to adjust their speed on the basis of the optic flow they perceived not only in the lateral and ventral parts of their visual field, but also in the dorsal part. More specifically, the honeybees' speed varied monotonically, depending on the minimum cross-section of the tunnel, regardless of whether the narrowing occurred in the horizontal or vertical plane. The honeybees' speed decreased or increased whenever the minimum cross-section decreased or increased. In other words, the larger sum of the two opposite optic flows in the horizontal and vertical planes was kept practically constant thanks to the speed control performed by the honeybees upon encountering a narrowing of the tunnel. The previously described ALIS (“AutopiLot using an Insect-based vision System”) model nicely matches the present behavioral findings. The ALIS model is based on a feedback control scheme that explains how honeybees may keep their speed proportional to the minimum local cross-section of a tunnel, based solely on optic flow processing, without any need for speedometers or rangefinders. The present behavioral findings suggest how flying insects may succeed in adjusting their speed in their complex foraging environments, while at the same time adjusting their distance not only from lateral and ventral objects but also from those located in their dorsal visual field

Honeybee visual flight control (experiments and model)

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