Abstract
Sensory systems need to reliably extract information from highly variable natural signals. Flies, for instance, use optic flow to guide their course and are remarkably adept at estimating image velocity regardless of image statistics. Current circuit models, however, cannot account for this robustness. Here, we demonstrate that the Drosophila visual system reduces input variability by rapidly adjusting its sensitivity to local contrast conditions. We exhaustively map functional properties of neurons in the motion detection circuit and find that local responses are compressed by surround contrast. The compressive signal is fast, integrates spatially, and derives from neural feedback. Training convolutional neural networks on estimating the velocity of natural stimuli shows that this dynamic signal compression can close the performance gap between model and organism. Overall, our work represents a comprehensive mechanistic account of how neural systems attain the robustness to carry out survival-critical tasks in challenging real-world environments.
Highlights
Visual motion represents a critical source of sensory feedback for navigation
For the ON pathway unit Tm3, we observed significantly reduced suppression across background frequencies when compared to controls with inactive toxin light chain (TNT) (Figures 5B and 5C)
We evaluated natural image responses in the data-driven lobula plate tangential cells (LPTCs) model and found moderate reduction of cross-image variability compared to a model with bypassed normalization (Figures S5L–S5N)
Summary
Visual motion represents a critical source of sensory feedback for navigation. Self-motion results in particular patterns of local directional cues across the retina. For instance, react to whole-field retinal motion by turning in the same direction as their surroundings. This optomotor response enables them to maintain a straight path under perturbations as well as over long distances [2, 3]. For the reflex to work effectively, biological motion detectors need to respond reliably and independently of the particular visual statistics of the environment. This poses a challenge given the complexity of natural scenes [4, 5]. Motion vision systems need to employ processing strategies that maintain robust performance despite the variability of natural visual input
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