Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/51443
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Type: Journal article
Title: What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology
Author: McDonnell, M.
Abbott, D.
Citation: PLoS Computational Biology, 2009; 5(5):1-9
Publisher: Public Library of Science
Issue Date: 2009
ISSN: 1553-734X
1553-734X
Statement of
Responsibility: 
Mark D. McDonnell and Derek Abbott
Abstract: Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology.
Keywords: Brain; Stochastic Processes; Neurosciences; Biomedical Engineering; Signal Processing, Computer-Assisted; Computational Biology; Models, Biological; Models, Neurological
Rights: © 2009 McDonnell, Abbott. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
RMID: 0020090871
DOI: 10.1371/journal.pcbi.1000348
Appears in Collections:Electrical and Electronic Engineering publications

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