Stochastic resonance at criticality in a network model of the human cortex.

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_7B210AA2C5A5
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Stochastic resonance at criticality in a network model of the human cortex.
Journal
Scientific reports
Author(s)
Vázquez-Rodríguez B., Avena-Koenigsberger A., Sporns O., Griffa A., Hagmann P., Larralde H.
ISSN
2045-2322 (Electronic)
ISSN-L
2045-2322
Publication state
Published
Issued date
12/10/2017
Peer-reviewed
Oui
Volume
7
Number
1
Pages
13020
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Abstract
Stochastic resonance is a phenomenon in which noise enhances the response of a system to an input signal. The brain is an example of a system that has to detect and transmit signals in a noisy environment, suggesting that it is a good candidate to take advantage of stochastic resonance. In this work, we aim to identify the optimal levels of noise that promote signal transmission through a simple network model of the human brain. Specifically, using a dynamic model implemented on an anatomical brain network (connectome), we investigate the similarity between an input signal and a signal that has traveled across the network while the system is subject to different noise levels. We find that non-zero levels of noise enhance the similarity between the input signal and the signal that has traveled through the system. The optimal noise level is not unique; rather, there is a set of parameter values at which the information is transmitted with greater precision, this set corresponds to the parameter values that place the system in a critical regime. The multiplicity of critical points in our model allows it to adapt to different noise situations and remain at criticality.
Keywords
Adult, Cerebral Cortex/anatomy & histology, Cerebral Cortex/physiology, Female, Humans, Male, Models, Neurological, Probability, Stochastic Processes, Time Factors
Pubmed
Web of science
Open Access
Yes
Create date
26/10/2017 17:01
Last modification date
30/04/2021 7:12
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