Cargando…
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...
Autor principal: | |
---|---|
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 |
_version_ | 1782486866524635136 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5187558 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT marenaliannaj theclustervariationmethodaprimerforneuroscientists AT marenaliannaj clustervariationmethodaprimerforneuroscientists |