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.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.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.doi http://dx.doi.org/10.1371/journal.pcbi.1000587
• dc.identifier.issn 1553-734X
• dc.identifier.uri http://hdl.handle.net/10230/37042
• dc.language.iso eng
• dc.publisher Public Library of Science (PLoS)
• dc.relation.ispartof PLOS Computational Biology. 2009 Dec 4;5(12):e1000587
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PN/BFU2007-61710
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PN/CSD2007-00012
• 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.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/3.0/es/deed.ca
• 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.title Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion