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

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Webb, Tristan J., Rolls, Edmund T., Deco, Gustavo and Feng, Jianfeng. (2011) Noise in attractor networks in the brain produced by graded firing rate representations. PLoS ONE, Vol.6 (No.9). e23630. ISSN 1932-6203

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Official URL: http://dx.doi.org/10.1371/journal.pone.0023630

Abstract

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.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QP Physiology
Divisions:	Faculty of Science > Centre for Complexity Science Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Attractors (Mathematics), Cerebral cortex -- Mathematical models, Neurons -- Mathematical models
Journal or Publication Title:	PLoS ONE
Publisher:	Public Library of Science
ISSN:	1932-6203
Date:	8 September 2011
Volume:	Vol.6
Number:	No.9
Page Range:	e23630
Identification Number:	10.1371/journal.pone.0023630
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Oxford Centre for Computational Neuroscience, University of Oxford. McDonnell Centre for Cognitive Neuroscience, Spain. Ministerio de Ciencia y Tecnología (MCT)
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URI:	http://wrap.warwick.ac.uk/id/eprint/38329