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Predicting neuronal dynamics with a delayed gain control model

Visual neurons respond to static images with specific dynamics: neuronal responses sum sub-additively over time, reduce in amplitude with repeated or sustained stimuli (neuronal adaptation), and are slower at low stimulus contrast. Here, we propose a simple model that predicts these seemingly dispar...

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Detalles Bibliográficos
Autores principales: Zhou, Jingyang, Benson, Noah C., Kay, Kendrick, Winawer, Jonathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6892546/
https://www.ncbi.nlm.nih.gov/pubmed/31747389
http://dx.doi.org/10.1371/journal.pcbi.1007484
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author Zhou, Jingyang
Benson, Noah C.
Kay, Kendrick
Winawer, Jonathan
author_facet Zhou, Jingyang
Benson, Noah C.
Kay, Kendrick
Winawer, Jonathan
author_sort Zhou, Jingyang
collection PubMed
description Visual neurons respond to static images with specific dynamics: neuronal responses sum sub-additively over time, reduce in amplitude with repeated or sustained stimuli (neuronal adaptation), and are slower at low stimulus contrast. Here, we propose a simple model that predicts these seemingly disparate response patterns observed in a diverse set of measurements–intracranial electrodes in patients, fMRI, and macaque single unit spiking. The model takes a time-varying contrast time course of a stimulus as input, and produces predicted neuronal dynamics as output. Model computation consists of linear filtering, expansive exponentiation, and a divisive gain control. The gain control signal relates to but is slower than the linear signal, and this delay is critical in giving rise to predictions matched to the observed dynamics. Our model is simpler than previously proposed related models, and fitting the model to intracranial EEG data uncovers two regularities across human visual field maps: estimated linear filters (temporal receptive fields) systematically differ across and within visual field maps, and later areas exhibit more rapid and substantial gain control. The model is further generalizable to account for dynamics of contrast-dependent spike rates in macaque V1, and amplitudes of fMRI BOLD in human V1.
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spelling pubmed-68925462019-12-13 Predicting neuronal dynamics with a delayed gain control model Zhou, Jingyang Benson, Noah C. Kay, Kendrick Winawer, Jonathan PLoS Comput Biol Research Article Visual neurons respond to static images with specific dynamics: neuronal responses sum sub-additively over time, reduce in amplitude with repeated or sustained stimuli (neuronal adaptation), and are slower at low stimulus contrast. Here, we propose a simple model that predicts these seemingly disparate response patterns observed in a diverse set of measurements–intracranial electrodes in patients, fMRI, and macaque single unit spiking. The model takes a time-varying contrast time course of a stimulus as input, and produces predicted neuronal dynamics as output. Model computation consists of linear filtering, expansive exponentiation, and a divisive gain control. The gain control signal relates to but is slower than the linear signal, and this delay is critical in giving rise to predictions matched to the observed dynamics. Our model is simpler than previously proposed related models, and fitting the model to intracranial EEG data uncovers two regularities across human visual field maps: estimated linear filters (temporal receptive fields) systematically differ across and within visual field maps, and later areas exhibit more rapid and substantial gain control. The model is further generalizable to account for dynamics of contrast-dependent spike rates in macaque V1, and amplitudes of fMRI BOLD in human V1. Public Library of Science 2019-11-20 /pmc/articles/PMC6892546/ /pubmed/31747389 http://dx.doi.org/10.1371/journal.pcbi.1007484 Text en © 2019 Zhou et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhou, Jingyang
Benson, Noah C.
Kay, Kendrick
Winawer, Jonathan
Predicting neuronal dynamics with a delayed gain control model
title Predicting neuronal dynamics with a delayed gain control model
title_full Predicting neuronal dynamics with a delayed gain control model
title_fullStr Predicting neuronal dynamics with a delayed gain control model
title_full_unstemmed Predicting neuronal dynamics with a delayed gain control model
title_short Predicting neuronal dynamics with a delayed gain control model
title_sort predicting neuronal dynamics with a delayed gain control model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6892546/
https://www.ncbi.nlm.nih.gov/pubmed/31747389
http://dx.doi.org/10.1371/journal.pcbi.1007484
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