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Pooling in a predictive model of V1 explains functional and structural diversity across species

View ORCID ProfileAngelo Franciosini, View ORCID ProfileVictor Boutin, View ORCID ProfileFrédéric Chavane, View ORCID ProfileLaurent U Perrinet
doi: https://doi.org/10.1101/2021.04.19.440444
Angelo Franciosini
1Inst. Neur. Timone, Aix-Marseille Univ, Marseille, France
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  • For correspondence: angelo.franciosini@univ-amu.fr
Victor Boutin
1Inst. Neur. Timone, Aix-Marseille Univ, Marseille, France
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Frédéric Chavane
1Inst. Neur. Timone, Aix-Marseille Univ, Marseille, France
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Laurent U Perrinet
1Inst. Neur. Timone, Aix-Marseille Univ, Marseille, France
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Abstract

Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high selectivity in simple cells to low selectivity in complex cells. Various computational models have suggested a possible link between the presence of phase invariant cells and the existence of cortical orientation maps in higher mammals’ V1. These models, however, do not explain the emergence of complex cells in animals that do not show orientation maps. In this study, we build a model of V1 based on a convolutional network called Sparse Deep Predictive Coding (SDPC) and show that a single computational mechanism, pooling, allows the SDPC model to account for the emergence of complex cells as well as cortical orientation maps in V1, as observed in distinct species of mammals. By using different pooling functions, our model developed complex cells in networks that exhibit orientation maps (e.g., like in carnivores and primates) or not (e.g., rodents and lagomorphs). The SDPC can therefore be viewed as a unifying framework that explains the diversity of structural and functional phenomena observed in V1. In particular, we show that orientation maps emerge naturally as the most cost-efficient structure to generate complex cells under the predictive coding principle.

Significance Cortical orientation maps are among the most fascinating structures observed in higher mammals brains: In such maps, similar orientations in the input image activate neighboring cells in the cortical surface. However, the computational advantage brought by these structures remains unclear, as some species (rodents and lagomorphs) completely lack orientation maps. In this study, we introduce a computational model that links the presence of orientation maps to a class of nonlinear neurons called complex cells. In particular, we propose that the presence or absence orientation maps correspond to different strategies employed by different species to generate invariance to complex stimuli.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 20, 2021.
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Pooling in a predictive model of V1 explains functional and structural diversity across species
Angelo Franciosini, Victor Boutin, Frédéric Chavane, Laurent U Perrinet
bioRxiv 2021.04.19.440444; doi: https://doi.org/10.1101/2021.04.19.440444
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Pooling in a predictive model of V1 explains functional and structural diversity across species
Angelo Franciosini, Victor Boutin, Frédéric Chavane, Laurent U Perrinet
bioRxiv 2021.04.19.440444; doi: https://doi.org/10.1101/2021.04.19.440444

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