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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/23406
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:42:54Zoai: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:34982025-09-29 09:42:54.44CONICET 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 |
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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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13.070432 |