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  Dynamic signal compression for robust motion vision in flies

Drews, M. S., Leonhardt, A., Pirogova, N., Richter, F. G., Schuetzenberger, A., Braun, L., et al. (2020). Dynamic signal compression for robust motion vision in flies. Current Biology, 30(2), 209-221.e8. doi:10.1016/j.cub.2019.10.035.

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 Creators:
Drews, Michael S.1, Author           
Leonhardt, Aljoscha1, Author           
Pirogova, Nadezhda1, Author           
Richter, Florian G.1, Author           
Schuetzenberger, Anna1, Author           
Braun, Lukas, Author
Serbe, Etienne1, Author           
Borst, Alexander1, Author           
Affiliations:
1Department: Circuits-Computation-Models / Borst, MPI of Neurobiology, Max Planck Society, ou_1113548              

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Free keywords: GAIN-CONTROL; DIRECTIONAL SELECTIVITY; RESPONSE PROPERTIES; NEURAL CIRCUIT; VISUAL-SYSTEM; DROSOPHILA; FLY; CONTRAST; INHIBITION; ADAPTATION
 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.

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Language(s): eng - English
 Dates: 2020-01-20
 Publication Status: Issued
 Pages: 21
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: ISI: 000508195800019
DOI: 10.1016/j.cub.2019.10.035
 Degree: -

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Project name : SFB 870
Grant ID : -
Funding program : -
Funding organization : DFG

Source 1

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Title: Current Biology
  Other : Curr. Biol.
Source Genre: Journal
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Publ. Info: London, UK : Cell Press
Pages: - Volume / Issue: 30 (2) Sequence Number: - Start / End Page: 209 - 221.e8 Identifier: ISSN: 0960-9822
CoNE: https://pure.mpg.de/cone/journals/resource/954925579107