Statistical modelling of higher-order correlations in pools of neural activity

Autores
Montani, Fernando Fabián; Phoka, Elena; Portesi, Mariela Adelina; Schultz, Simon R.
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Simultaneous recordings from multiple neural units allow us to investigate the activity of very large neural ensembles. To understand how large ensembles of neurons process sensory information, it is necessary to develop suitable statistical models to describe the response variability of the recorded spike trains. Using the information geometry framework, it is possible to estimate higher-order correlations by assigning one interaction parameter to each degree of correlation, leading to a (2^N-1)-dimensional model for a population with N neurons. However, this model suffers greatly from a combinatorial explosion, and the number of parameters to be estimated from the available sample size constitutes the main intractability reason of this approach. To quantify the extent of higher than pairwise spike correlations in pools of multiunit activity, we use an information-geometric approach within the framework of the extended central limit theorem considering all possible contributions from higher-order spike correlations. The identification of a deformation parameter allows us to provide a statistical characterisation of the amount of higher-order correlations in the case of a very large neural ensemble, significantly reducing the number of parameters, avoiding the sampling problem, and inferring the underlying dynamical properties of the network within pools of multiunit neural activity.
Fil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina
Fil: Phoka, Elena. Imperial College London; Reino Unido
Fil: Portesi, Mariela Adelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina
Fil: Schultz, Simon R.. Imperial College London; Reino Unido
Materia
NEURAL ACTIVITY
SPIKE CORRELATIONS
HIGH-ORDER CORRELATIONS
INFORMATION-GEOMETRY APPROACH
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/23406
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Statistical modelling of higher-order correlations in pools of neural activityMontani, Fernando FabiánPhoka, ElenaPortesi, Mariela AdelinaSchultz, Simon R.NEURAL ACTIVITYSPIKE CORRELATIONSHIGH-ORDER CORRELATIONSINFORMATION-GEOMETRY APPROACHSimultaneous recordings from multiple neural units allow us to investigate the activity of very large neural ensembles. To understand how large ensembles of neurons process sensory information, it is necessary to develop suitable statistical models to describe the response variability of the recorded spike trains. Using the information geometry framework, it is possible to estimate higher-order correlations by assigning one interaction parameter to each degree of correlation, leading to a (2^N-1)-dimensional model for a population with N neurons. However, this model suffers greatly from a combinatorial explosion, and the number of parameters to be estimated from the available sample size constitutes the main intractability reason of this approach. To quantify the extent of higher than pairwise spike correlations in pools of multiunit activity, we use an information-geometric approach within the framework of the extended central limit theorem considering all possible contributions from higher-order spike correlations. The identification of a deformation parameter allows us to provide a statistical characterisation of the amount of higher-order correlations in the case of a very large neural ensemble, significantly reducing the number of parameters, avoiding the sampling problem, and inferring the underlying dynamical properties of the network within pools of multiunit neural activity.Fil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; ArgentinaFil: Phoka, Elena. Imperial College London; Reino UnidoFil: Portesi, Mariela Adelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; ArgentinaFil: Schultz, Simon R.. Imperial College London; Reino UnidoElsevier Science2013-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/23406Montani, Fernando Fabián; Phoka, Elena; Portesi, Mariela Adelina; Schultz, Simon R.; Statistical modelling of higher-order correlations in pools of neural activity; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 392; 14; 3-2013; 3066-30860378-4371CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2013.03.012info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S037843711300215Xinfo:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1211.6348info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:11:30Zoai:ri.conicet.gov.ar:11336/23406instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:11:30.473CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Statistical modelling of higher-order correlations in pools of neural activity
title Statistical modelling of higher-order correlations in pools of neural activity
spellingShingle Statistical modelling of higher-order correlations in pools of neural activity
Montani, Fernando Fabián
NEURAL ACTIVITY
SPIKE CORRELATIONS
HIGH-ORDER CORRELATIONS
INFORMATION-GEOMETRY APPROACH
title_short Statistical modelling of higher-order correlations in pools of neural activity
title_full Statistical modelling of higher-order correlations in pools of neural activity
title_fullStr Statistical modelling of higher-order correlations in pools of neural activity
title_full_unstemmed Statistical modelling of higher-order correlations in pools of neural activity
title_sort Statistical modelling of higher-order correlations in pools of neural activity
dc.creator.none.fl_str_mv Montani, Fernando Fabián
Phoka, Elena
Portesi, Mariela Adelina
Schultz, Simon R.
author Montani, Fernando Fabián
author_facet Montani, Fernando Fabián
Phoka, Elena
Portesi, Mariela Adelina
Schultz, Simon R.
author_role author
author2 Phoka, Elena
Portesi, Mariela Adelina
Schultz, Simon R.
author2_role author
author
author
dc.subject.none.fl_str_mv NEURAL ACTIVITY
SPIKE CORRELATIONS
HIGH-ORDER CORRELATIONS
INFORMATION-GEOMETRY APPROACH
topic NEURAL ACTIVITY
SPIKE CORRELATIONS
HIGH-ORDER CORRELATIONS
INFORMATION-GEOMETRY APPROACH
dc.description.none.fl_txt_mv Simultaneous recordings from multiple neural units allow us to investigate the activity of very large neural ensembles. To understand how large ensembles of neurons process sensory information, it is necessary to develop suitable statistical models to describe the response variability of the recorded spike trains. Using the information geometry framework, it is possible to estimate higher-order correlations by assigning one interaction parameter to each degree of correlation, leading to a (2^N-1)-dimensional model for a population with N neurons. However, this model suffers greatly from a combinatorial explosion, and the number of parameters to be estimated from the available sample size constitutes the main intractability reason of this approach. To quantify the extent of higher than pairwise spike correlations in pools of multiunit activity, we use an information-geometric approach within the framework of the extended central limit theorem considering all possible contributions from higher-order spike correlations. The identification of a deformation parameter allows us to provide a statistical characterisation of the amount of higher-order correlations in the case of a very large neural ensemble, significantly reducing the number of parameters, avoiding the sampling problem, and inferring the underlying dynamical properties of the network within pools of multiunit neural activity.
Fil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina
Fil: Phoka, Elena. Imperial College London; Reino Unido
Fil: Portesi, Mariela Adelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina
Fil: Schultz, Simon R.. Imperial College London; Reino Unido
description Simultaneous recordings from multiple neural units allow us to investigate the activity of very large neural ensembles. To understand how large ensembles of neurons process sensory information, it is necessary to develop suitable statistical models to describe the response variability of the recorded spike trains. Using the information geometry framework, it is possible to estimate higher-order correlations by assigning one interaction parameter to each degree of correlation, leading to a (2^N-1)-dimensional model for a population with N neurons. However, this model suffers greatly from a combinatorial explosion, and the number of parameters to be estimated from the available sample size constitutes the main intractability reason of this approach. To quantify the extent of higher than pairwise spike correlations in pools of multiunit activity, we use an information-geometric approach within the framework of the extended central limit theorem considering all possible contributions from higher-order spike correlations. The identification of a deformation parameter allows us to provide a statistical characterisation of the amount of higher-order correlations in the case of a very large neural ensemble, significantly reducing the number of parameters, avoiding the sampling problem, and inferring the underlying dynamical properties of the network within pools of multiunit neural activity.
publishDate 2013
dc.date.none.fl_str_mv 2013-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/23406
Montani, Fernando Fabián; Phoka, Elena; Portesi, Mariela Adelina; Schultz, Simon R.; Statistical modelling of higher-order correlations in pools of neural activity; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 392; 14; 3-2013; 3066-3086
0378-4371
CONICET Digital
CONICET
url http://hdl.handle.net/11336/23406
identifier_str_mv Montani, Fernando Fabián; Phoka, Elena; Portesi, Mariela Adelina; Schultz, Simon R.; Statistical modelling of higher-order correlations in pools of neural activity; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 392; 14; 3-2013; 3066-3086
0378-4371
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2013.03.012
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S037843711300215X
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1211.6348
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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