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The Cluster Variation Method: A Primer for Neuroscientists

Effective Brain–Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers wit...

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Autor principal: Maren, Alianna J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5187558/
https://www.ncbi.nlm.nih.gov/pubmed/27706022
http://dx.doi.org/10.3390/brainsci6040044
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author Maren, Alianna J.
author_facet Maren, Alianna J.
author_sort Maren, Alianna J.
collection PubMed
description Effective Brain–Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables, is defined in terms of a single interaction enthalpy parameter (h) for the case of an equiprobable distribution of bistate (neural/neural ensemble) units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution) yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found.
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spelling pubmed-51875582016-12-30 The Cluster Variation Method: A Primer for Neuroscientists Maren, Alianna J. Brain Sci Article Effective Brain–Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables, is defined in terms of a single interaction enthalpy parameter (h) for the case of an equiprobable distribution of bistate (neural/neural ensemble) units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution) yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found. MDPI 2016-09-30 /pmc/articles/PMC5187558/ /pubmed/27706022 http://dx.doi.org/10.3390/brainsci6040044 Text en © 2016 by the author; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Maren, Alianna J.
The Cluster Variation Method: A Primer for Neuroscientists
title The Cluster Variation Method: A Primer for Neuroscientists
title_full The Cluster Variation Method: A Primer for Neuroscientists
title_fullStr The Cluster Variation Method: A Primer for Neuroscientists
title_full_unstemmed The Cluster Variation Method: A Primer for Neuroscientists
title_short The Cluster Variation Method: A Primer for Neuroscientists
title_sort cluster variation method: a primer for neuroscientists
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5187558/
https://www.ncbi.nlm.nih.gov/pubmed/27706022
http://dx.doi.org/10.3390/brainsci6040044
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