Dynamic Signal Compression for Robust Motion Vision in Flies.

Autor:	Drews MS; Department Circuits-Computation-Models, Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany; Graduate School of Systemic Neurosciences, LMU Munich, 82152 Martinsried, Germany. Electronic address: drews@neuro.mpg.de., Leonhardt A; Department Circuits-Computation-Models, Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany. Electronic address: leonhardt@neuro.mpg.de., Pirogova N; Department Circuits-Computation-Models, Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany; Graduate School of Systemic Neurosciences, LMU Munich, 82152 Martinsried, Germany., Richter FG; Department Circuits-Computation-Models, Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany; Graduate School of Systemic Neurosciences, LMU Munich, 82152 Martinsried, Germany., Schuetzenberger A; Department Circuits-Computation-Models, Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany; Graduate School of Systemic Neurosciences, LMU Munich, 82152 Martinsried, Germany., Braun L; Bernstein Center for Computational Neuroscience, 10115 Berlin, Germany., Serbe E; Department Circuits-Computation-Models, Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany., Borst A; Department Circuits-Computation-Models, Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany.
Jazyk:	angličtina
Zdroj:	Current biology : CB [Curr Biol] 2020 Jan 20; Vol. 30 (2), pp. 209-221.e8. Date of Electronic Publication: 2020 Jan 10.
DOI:	10.1016/j.cub.2019.10.035
Abstrakt:	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. (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze:	MEDLINE
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