Intrinsic gain modulation and adaptive neural coding.

In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sungho Hong, Brian Nils Lundstrom, Adrienne L Fairhall
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/132cd1d45fe4470fbbda6478267ed743
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:132cd1d45fe4470fbbda6478267ed743
record_format	dspace
spelling	oai:doaj.org-article:132cd1d45fe4470fbbda6478267ed7432021-11-25T05:41:13ZIntrinsic gain modulation and adaptive neural coding.1553-734X1553-735810.1371/journal.pcbi.1000119https://doaj.org/article/132cd1d45fe4470fbbda6478267ed7432008-07-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18636100/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters and gain curve adjust very rapidly to changes in the variance of a randomly varying driving input. An apparently similar but previously unrelated issue is the observation of gain control by background noise in cortical neurons: the slope of the firing rate versus current (f-I) curve changes with the variance of background random input. Here, we show a direct correspondence between these two observations by relating variance-dependent changes in the gain of f-I curves to characteristics of the changing empirical linear/nonlinear model obtained by sampling. In the case that the underlying system is fixed, we derive relationships relating the change of the gain with respect to both mean and variance with the receptive fields derived from reverse correlation on a white noise stimulus. Using two conductance-based model neurons that display distinct gain modulation properties through a simple change in parameters, we show that coding properties of both these models quantitatively satisfy the predicted relationships. Our results describe how both variance-dependent gain modulation and adaptive neural computation result from intrinsic nonlinearity.Sungho HongBrian Nils LundstromAdrienne L FairhallPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 7, p e1000119 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Sungho Hong Brian Nils Lundstrom Adrienne L Fairhall Intrinsic gain modulation and adaptive neural coding.
description	In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters and gain curve adjust very rapidly to changes in the variance of a randomly varying driving input. An apparently similar but previously unrelated issue is the observation of gain control by background noise in cortical neurons: the slope of the firing rate versus current (f-I) curve changes with the variance of background random input. Here, we show a direct correspondence between these two observations by relating variance-dependent changes in the gain of f-I curves to characteristics of the changing empirical linear/nonlinear model obtained by sampling. In the case that the underlying system is fixed, we derive relationships relating the change of the gain with respect to both mean and variance with the receptive fields derived from reverse correlation on a white noise stimulus. Using two conductance-based model neurons that display distinct gain modulation properties through a simple change in parameters, we show that coding properties of both these models quantitatively satisfy the predicted relationships. Our results describe how both variance-dependent gain modulation and adaptive neural computation result from intrinsic nonlinearity.
format	article
author	Sungho Hong Brian Nils Lundstrom Adrienne L Fairhall
author_facet	Sungho Hong Brian Nils Lundstrom Adrienne L Fairhall
author_sort	Sungho Hong
title	Intrinsic gain modulation and adaptive neural coding.
title_short	Intrinsic gain modulation and adaptive neural coding.
title_full	Intrinsic gain modulation and adaptive neural coding.
title_fullStr	Intrinsic gain modulation and adaptive neural coding.
title_full_unstemmed	Intrinsic gain modulation and adaptive neural coding.
title_sort	intrinsic gain modulation and adaptive neural coding.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/132cd1d45fe4470fbbda6478267ed743
work_keys_str_mv	AT sunghohong intrinsicgainmodulationandadaptiveneuralcoding AT briannilslundstrom intrinsicgainmodulationandadaptiveneuralcoding AT adriennelfairhall intrinsicgainmodulationandadaptiveneuralcoding
_version_	1718414501230936064

Intrinsic gain modulation and adaptive neural coding.

Ejemplares similares