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Explaining Orientation Adaptation in V1 by Updating the State of a Spatial Model

In this work, we extend an influential statistical model based on the spatial classical receptive field (CRF) and non-classical receptive field (nCRF) interactions (Coen-Cagli et al., 2012) to explain the typical orientation adaptation effects observed in V1. If we assume that the temporal adaptatio...

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Autores principales: Gao, Shaobing, Liu, Xiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8895385/
https://www.ncbi.nlm.nih.gov/pubmed/35250523
http://dx.doi.org/10.3389/fncom.2021.759254
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author Gao, Shaobing
Liu, Xiao
author_facet Gao, Shaobing
Liu, Xiao
author_sort Gao, Shaobing
collection PubMed
description In this work, we extend an influential statistical model based on the spatial classical receptive field (CRF) and non-classical receptive field (nCRF) interactions (Coen-Cagli et al., 2012) to explain the typical orientation adaptation effects observed in V1. If we assume that the temporal adaptation modifies the “state” of the model, the spatial statistical model can explain all of the orientation adaptation effects in the context of neuronal output using small and large grating observed in neurophysiological experiments in V1. The “state” of the model represents the internal parameters such as the prior and the covariance trained on a mixed dataset that totally determine the response of the model. These two parameters, respectively, reflect the probability of the orientation component and the connectivity among neurons between CRF and nCRF. Specifically, we have two key findings: First, neural adapted results using a small grating that just covers the CRF can be predicted by the change of the prior of our model. Second, the change of the prior can also predict most of the observed results using a large grating that covers both CRF and nCRF of a neuron. However, the prediction of the novel attractive adaptation using large grating covering both CRF and nCRF also necessitates the involvement of a connectivity change of the center-surround RFs. In addition, our paper contributes a new prior-based winner-take-all (WTA) working mechanism derived from the statistical-based model to explain why and how all of these orientation adaptation effects can be predicted by relying on this spatial model without modifying its structure, a novel application of the spatial model. The research results show that adaptation may link time and space by changing the “state” of the neural system according to a specific adaptor. Furthermore, different forms of stimulus used for adaptation can cause various adaptation effects, such as an a priori shift or a connectivity change, depending on the specific stimulus size.
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spelling pubmed-88953852022-03-05 Explaining Orientation Adaptation in V1 by Updating the State of a Spatial Model Gao, Shaobing Liu, Xiao Front Comput Neurosci Neuroscience In this work, we extend an influential statistical model based on the spatial classical receptive field (CRF) and non-classical receptive field (nCRF) interactions (Coen-Cagli et al., 2012) to explain the typical orientation adaptation effects observed in V1. If we assume that the temporal adaptation modifies the “state” of the model, the spatial statistical model can explain all of the orientation adaptation effects in the context of neuronal output using small and large grating observed in neurophysiological experiments in V1. The “state” of the model represents the internal parameters such as the prior and the covariance trained on a mixed dataset that totally determine the response of the model. These two parameters, respectively, reflect the probability of the orientation component and the connectivity among neurons between CRF and nCRF. Specifically, we have two key findings: First, neural adapted results using a small grating that just covers the CRF can be predicted by the change of the prior of our model. Second, the change of the prior can also predict most of the observed results using a large grating that covers both CRF and nCRF of a neuron. However, the prediction of the novel attractive adaptation using large grating covering both CRF and nCRF also necessitates the involvement of a connectivity change of the center-surround RFs. In addition, our paper contributes a new prior-based winner-take-all (WTA) working mechanism derived from the statistical-based model to explain why and how all of these orientation adaptation effects can be predicted by relying on this spatial model without modifying its structure, a novel application of the spatial model. The research results show that adaptation may link time and space by changing the “state” of the neural system according to a specific adaptor. Furthermore, different forms of stimulus used for adaptation can cause various adaptation effects, such as an a priori shift or a connectivity change, depending on the specific stimulus size. Frontiers Media S.A. 2022-02-18 /pmc/articles/PMC8895385/ /pubmed/35250523 http://dx.doi.org/10.3389/fncom.2021.759254 Text en Copyright © 2022 Gao and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Gao, Shaobing
Liu, Xiao
Explaining Orientation Adaptation in V1 by Updating the State of a Spatial Model
title Explaining Orientation Adaptation in V1 by Updating the State of a Spatial Model
title_full Explaining Orientation Adaptation in V1 by Updating the State of a Spatial Model
title_fullStr Explaining Orientation Adaptation in V1 by Updating the State of a Spatial Model
title_full_unstemmed Explaining Orientation Adaptation in V1 by Updating the State of a Spatial Model
title_short Explaining Orientation Adaptation in V1 by Updating the State of a Spatial Model
title_sort explaining orientation adaptation in v1 by updating the state of a spatial model
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8895385/
https://www.ncbi.nlm.nih.gov/pubmed/35250523
http://dx.doi.org/10.3389/fncom.2021.759254
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