A Chimera Model for Motion Anticipation in the Retina and the Primary Visual Cortex

We propose a mean field model of the primary visual cortex (V1), connected to a realistic retina model, to study the impact of the retina on motion anticipation. We first consider the case where the retina does not itself provide anticipation-which is then only triggered by a cortical mechanism, the "anticipation by latency"-and unravel the effects of the retinal input amplitude, of stimulus features such as speed and contrast and of the size of cortical extensions and fiber conduction speed. Then we explore the changes in the cortical wave of anticipation when V1 is triggered by retina-driven anticipatory mechanisms: gain control and lateral inhibition by amacrine cells. Here, we show how retinal and cortical anticipation combine to provide an efficient processing where the simulated cortical response is in advance over the moving object that triggers this response, compensating the delays in visual processing.

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Publication Details

Journal
Neural Computation
Published
2025-09-22
DOI
https://doi.org/10.1162/neco.a.34
Citations
3
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
4.29

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article

A Chimera Model for Motion Anticipation in the Retina and the Primary Visual Cortex

Bruno Cessac, Alain Destexhe, Frédéric Chavane, Matteo di Volo et al.
3 citations
Neural Computation
Neural dynamics and brain function
4.29
article

A Chimera Model for Motion Anticipation in the Retina and the Primary Visual Cortex

Bruno Cessac, Alain Destexhe, Frédéric Chavane, Matteo di Volo, Selma Souihel, Jérôme Emonet
article en
3 citations

Abstract

We propose a mean field model of the primary visual cortex (V1), connected to a realistic retina model, to study the impact of the retina on motion anticipation. We first consider the case where the retina does not itself provide anticipation-which is then only triggered by a cortical mechanism, the "anticipation by latency"-and unravel the effects of the retinal input amplitude, of stimulus features such as speed and contrast and of the size of cortical extensions and fiber conduction speed. Then we explore the changes in the cortical wave of anticipation when V1 is triggered by retina-driven anticipatory mechanisms: gain control and lateral inhibition by amacrine cells. Here, we show how retinal and cortical anticipation combine to provide an efficient processing where the simulated cortical response is in advance over the moving object that triggers this response, compensating the delays in visual processing.

Neural ComputationVol. 37(11)
Université Claude Bernard Lyon 1 (FR), Centre National de la Recherche Scientifique (FR), Institut national de recherche en sciences et technologies du numérique (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Institut des Neurosciences Paris-Saclay (FR), CEA Paris-Saclay (FR), Institut Cellule Souche et Cerveau (FR), Institut de Neurosciences de la Timone (FR), Genomic Vision (France) (FR)
European Commission, Agence Nationale de la Recherche, Centre National de la Recherche Scientifique, Horizon 2020 Framework Programme, CHIST-ERA
Openalex Percentile: Top 8%
Neural dynamics and brain function
4.29
DOIOpen Access PDFOpenAlex
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