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Representational untangling by the firing rate nonlinearity in V1 simple cells

Published version
Peer-reviewed

Type

Article

Change log

Authors

Gaspar, Merse E 
Polack, Pierre-Olivier 
Peyman, Golshani 
Orban, Gergo 

Abstract

An important computational goal of the visual system is “representational untangling” (RU): representing increasingly complex features of visual scenes in an easily decodable format. RU is typically assumed to be achieved in high-level visual cortices via several stages of cortical processing. Here we show, using a canonical population coding model, that RU of low-level orientation information is already performed at the first cortical stage of visual processing, but not before that, by a fundamental cellular-level property: the thresholded firing rate nonlinearity of simple cells in the primary visual cortex (V1). We identified specific, experimentally measurable parameters that determined the optimal firing threshold for RU and found that the thresholds of V1 simple cells extracted from in vivo recordings in awake behaving mice were near optimal. These results suggest that information re-formatting, rather than maximisation, may already be a relevant computational goal for the early visual system.

Description

Keywords

firing rate nonlinearity, intracellular, linear decoding, membrane potential, mixed selectivity, mouse, neuroscience, vision, Action Potentials, Animals, Mice, Models, Neurological, Neurons, Orientation, Spatial, Visual Cortex, Visual Perception

Journal Title

eLife

Conference Name

Journal ISSN

2050-084X
2050-084X

Volume Title

8

Publisher

eLife Sciences Publications, Ltd
Sponsorship
Wellcome Trust (095621/Z/11/Z)
Human Frontier Science Program (HFSP) (RGP0044/2018)
This work was supported by a Lendulet Award of the Hungarian Academy of Sciences (G.O.), an award from the National Brain Research Program of Hungary (NAP-B, KTIA_NAP_12-2-201, GO), and the Wellcome Trust (M.L.), the Whitehall Foundation (P.G) and NIH R01 grant MH105427 (P.G) and the Human Frontier Science Program (RGP0044/2018, P.G.,M.L.,G.O.).