Noise in attractor networks in the brain produced by graded firing rate representations.

Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate probability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as decision-making, that are influenced by the noise pr...

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Autores principales:	Tristan J Webb, Edmund T Rolls, Gustavo Deco, Jianfeng Feng
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/090c8ca457ef41ac836fdff1fa13d412
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spelling	oai:doaj.org-article:090c8ca457ef41ac836fdff1fa13d4122021-11-04T06:09:06ZNoise in attractor networks in the brain produced by graded firing rate representations.1932-620310.1371/journal.pone.0023630https://doaj.org/article/090c8ca457ef41ac836fdff1fa13d4122011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21931607/?tool=EBIhttps://doaj.org/toc/1932-6203Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate probability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as decision-making, that are influenced by the noise produced by the close to random spike timings of each neuron for a given mean rate, the noise with this graded type of representation may be larger than with the binary firing rate distribution that is usually investigated. In integrate-and-fire simulations of an attractor decision-making network, we show that the noise is indeed greater for a given sparseness of the representation for graded, exponential, than for binary firing rate distributions. The greater noise was measured by faster escaping times from the spontaneous firing rate state when the decision cues are applied, and this corresponds to faster decision or reaction times. The greater noise was also evident as less stability of the spontaneous firing state before the decision cues are applied. The implication is that spiking-related noise will continue to be a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall even with the quite large networks found in the cerebral cortex. In these networks there are several thousand recurrent collateral synapses onto each neuron. The greater noise with graded firing rate distributions has the advantage that it can increase the speed of operation of cortical circuitry.Tristan J WebbEdmund T RollsGustavo DecoJianfeng FengPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 9, p e23630 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tristan J Webb Edmund T Rolls Gustavo Deco Jianfeng Feng Noise in attractor networks in the brain produced by graded firing rate representations.
description	Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate probability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as decision-making, that are influenced by the noise produced by the close to random spike timings of each neuron for a given mean rate, the noise with this graded type of representation may be larger than with the binary firing rate distribution that is usually investigated. In integrate-and-fire simulations of an attractor decision-making network, we show that the noise is indeed greater for a given sparseness of the representation for graded, exponential, than for binary firing rate distributions. The greater noise was measured by faster escaping times from the spontaneous firing rate state when the decision cues are applied, and this corresponds to faster decision or reaction times. The greater noise was also evident as less stability of the spontaneous firing state before the decision cues are applied. The implication is that spiking-related noise will continue to be a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall even with the quite large networks found in the cerebral cortex. In these networks there are several thousand recurrent collateral synapses onto each neuron. The greater noise with graded firing rate distributions has the advantage that it can increase the speed of operation of cortical circuitry.
format	article
author	Tristan J Webb Edmund T Rolls Gustavo Deco Jianfeng Feng
author_facet	Tristan J Webb Edmund T Rolls Gustavo Deco Jianfeng Feng
author_sort	Tristan J Webb
title	Noise in attractor networks in the brain produced by graded firing rate representations.
title_short	Noise in attractor networks in the brain produced by graded firing rate representations.
title_full	Noise in attractor networks in the brain produced by graded firing rate representations.
title_fullStr	Noise in attractor networks in the brain produced by graded firing rate representations.
title_full_unstemmed	Noise in attractor networks in the brain produced by graded firing rate representations.
title_sort	noise in attractor networks in the brain produced by graded firing rate representations.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/090c8ca457ef41ac836fdff1fa13d412
work_keys_str_mv	AT tristanjwebb noiseinattractornetworksinthebrainproducedbygradedfiringraterepresentations AT edmundtrolls noiseinattractornetworksinthebrainproducedbygradedfiringraterepresentations AT gustavodeco noiseinattractornetworksinthebrainproducedbygradedfiringraterepresentations AT jianfengfeng noiseinattractornetworksinthebrainproducedbygradedfiringraterepresentations
_version_	1718445133393821696

Noise in attractor networks in the brain produced by graded firing rate representations.

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