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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. doi:10.1371/journal.pone.0023630 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 | ||||
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Subjects: | Q Science > QA Mathematics Q Science > QP Physiology |
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Divisions: | Faculty of Science, Engineering and Medicine > Research Centres > Centre for Complexity Science Faculty of Science, Engineering and Medicine > Science > Computer Science |
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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 | ||||
Official Date: | 8 September 2011 | ||||
Dates: |
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Volume: | Vol.6 | ||||
Number: | No.9 | ||||
Page Range: | e23630 | ||||
DOI: | 10.1371/journal.pone.0023630 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Date of first compliant deposit: | 18 December 2015 | ||||
Date of first compliant Open Access: | 18 December 2015 | ||||
Funder: | Oxford Centre for Computational Neuroscience, University of Oxford. McDonnell Centre for Cognitive Neuroscience, Spain. Ministerio de Ciencia y Tecnología (MCT) |
Data sourced from Thomson Reuters' Web of Knowledge
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