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Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics

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dc.contributor.author Deco, Gustavo
dc.contributor.author Martí, Daniel
dc.contributor.author Ledberg, Anders
dc.contributor.author Reig, Ramon
dc.contributor.author Sanchez-Vives, Maria V.
dc.date.accessioned 2019-04-04T08:41:58Z
dc.date.available 2019-04-04T08:41:58Z
dc.date.issued 2009
dc.identifier.citation Deco G, Martí D, Ledberg A, Reig R, Sanchez Vives MV. Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics. PLoS Comput Biol. 2009 Dec 4;5(12):e1000587. DOI: 10.1371/journal.pcbi.1000587
dc.identifier.issn 1553-734X
dc.identifier.uri http://hdl.handle.net/10230/37042
dc.description.abstract We introduce in this paper a new method for reducing neurodynamical data to an effective diffusion equation, either experimentally or using simulations of biophysically detailed models. The dimensionality of the data is first reduced to the first principal component, and then fitted by the stationary solution of a mean-field-like one-dimensional Langevin equation, which describes the motion of a Brownian particle in a potential. The advantage of such description is that the stationary probability density of the dynamical variable can be easily derived. We applied this method to the analysis of cortical network dynamics during up and down states in an anesthetized animal. During deep anesthesia, intracellularly recorded up and down states transitions occurred with high regularity and could not be adequately described by a onedimensional diffusion equation. Under lighter anesthesia, however, the distributions of the times spent in the up and down states were better fitted by such a model, suggesting a role for noise in determining the time spent in a particular state.
dc.description.sponsorship This work was supported by the Spanish Ministry of Science (Spanish Research Project BFU2007-61710, and CONSOLIDER CSD2007-00012). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Public Library of Science (PLoS)
dc.relation.ispartof PLOS Computational Biology. 2009 Dec 4;5(12):e1000587
dc.rights © 2009 Deco et al. 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.
dc.rights.uri https://creativecommons.org/licenses/by/3.0/es/deed.ca
dc.title Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1000587
dc.subject.keyword Anesthesia
dc.subject.keyword Neurons
dc.subject.keyword Neural networks
dc.subject.keyword Probability density
dc.subject.keyword Data reduction
dc.subject.keyword Approximation methods
dc.subject.keyword Cerebral cortex
dc.subject.keyword Simulation and modeling
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PN/BFU2007-61710
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PN/CSD2007-00012
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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