Information theoretic methods for studying population codes

Autores
Ince, Robin A. A.; Senatore, Riccardo; Arabzadeh, Ehsan; Montani, Fernando Fabián; Diamond, Mathew E.; Panzeri, Stefano
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Population coding is the quantitative study of which algorithms or representations are used by thebrain to combine together and evaluate the messages carried by different neurons. Here, we review ani nformation-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limitedsampling bias which affects the calculation of information from a limited amount of experimental data.We then discuss how to quantify the contribution of individual members of the population, or theinteraction between them, to the overall information encoded by the considered group of neurons. Wefocus in particular on evaluating what is the contribution of interactions up to any given order to thetotal information. We illustrate this formalism with applications to simulated data with realistic neuronal statistic and  to real simultaneous recordings of multiple spike trains.
Fil: Ince, Robin A. A.. University of Manchester; Reino Unido
Fil: Senatore, Riccardo. University of Manchester; Reino Unido
Fil: Arabzadeh, Ehsan. University of New South Wales; Australia
Fil: Montani, Fernando Fabián. 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. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Diamond, Mathew E.. No especifíca;
Fil: Panzeri, Stefano. No especifíca;
Materia
POPULATION CODES
INFORMATION THEORY
SAMPLING BIAS
SOMATOSENSORY CORTEX
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/280921
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Information theoretic methods for studying population codesInce, Robin A. A.Senatore, RiccardoArabzadeh, EhsanMontani, Fernando FabiánDiamond, Mathew E.Panzeri, StefanoPOPULATION CODESINFORMATION THEORYSAMPLING BIASSOMATOSENSORY CORTEXhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Population coding is the quantitative study of which algorithms or representations are used by thebrain to combine together and evaluate the messages carried by different neurons. Here, we review ani nformation-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limitedsampling bias which affects the calculation of information from a limited amount of experimental data.We then discuss how to quantify the contribution of individual members of the population, or theinteraction between them, to the overall information encoded by the considered group of neurons. Wefocus in particular on evaluating what is the contribution of interactions up to any given order to thetotal information. We illustrate this formalism with applications to simulated data with realistic neuronal statistic and  to real simultaneous recordings of multiple spike trains.Fil: Ince, Robin A. A.. University of Manchester; Reino UnidoFil: Senatore, Riccardo. University of Manchester; Reino UnidoFil: Arabzadeh, Ehsan. University of New South Wales; AustraliaFil: Montani, Fernando Fabián. 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. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Diamond, Mathew E.. No especifíca;Fil: Panzeri, Stefano. No especifíca;Pergamon-Elsevier Science Ltd2010-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/280921Ince, Robin A. A.; Senatore, Riccardo; Arabzadeh, Ehsan; Montani, Fernando Fabián; Diamond, Mathew E.; et al.; Information theoretic methods for studying population codes; Pergamon-Elsevier Science Ltd; Neural Networks; 23; 6; 8-2010; 713-7270893-6080CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0893608010001012info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neunet.2010.05.008info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-02-06T12:45:16Zoai:ri.conicet.gov.ar:11336/280921instacron: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:34982026-02-06 12:45:16.325CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Information theoretic methods for studying population codes
title Information theoretic methods for studying population codes
spellingShingle Information theoretic methods for studying population codes
Ince, Robin A. A.
POPULATION CODES
INFORMATION THEORY
SAMPLING BIAS
SOMATOSENSORY CORTEX
title_short Information theoretic methods for studying population codes
title_full Information theoretic methods for studying population codes
title_fullStr Information theoretic methods for studying population codes
title_full_unstemmed Information theoretic methods for studying population codes
title_sort Information theoretic methods for studying population codes
dc.creator.none.fl_str_mv Ince, Robin A. A.
Senatore, Riccardo
Arabzadeh, Ehsan
Montani, Fernando Fabián
Diamond, Mathew E.
Panzeri, Stefano
author Ince, Robin A. A.
author_facet Ince, Robin A. A.
Senatore, Riccardo
Arabzadeh, Ehsan
Montani, Fernando Fabián
Diamond, Mathew E.
Panzeri, Stefano
author_role author
author2 Senatore, Riccardo
Arabzadeh, Ehsan
Montani, Fernando Fabián
Diamond, Mathew E.
Panzeri, Stefano
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv POPULATION CODES
INFORMATION THEORY
SAMPLING BIAS
SOMATOSENSORY CORTEX
topic POPULATION CODES
INFORMATION THEORY
SAMPLING BIAS
SOMATOSENSORY CORTEX
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Population coding is the quantitative study of which algorithms or representations are used by thebrain to combine together and evaluate the messages carried by different neurons. Here, we review ani nformation-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limitedsampling bias which affects the calculation of information from a limited amount of experimental data.We then discuss how to quantify the contribution of individual members of the population, or theinteraction between them, to the overall information encoded by the considered group of neurons. Wefocus in particular on evaluating what is the contribution of interactions up to any given order to thetotal information. We illustrate this formalism with applications to simulated data with realistic neuronal statistic and  to real simultaneous recordings of multiple spike trains.
Fil: Ince, Robin A. A.. University of Manchester; Reino Unido
Fil: Senatore, Riccardo. University of Manchester; Reino Unido
Fil: Arabzadeh, Ehsan. University of New South Wales; Australia
Fil: Montani, Fernando Fabián. 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. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Diamond, Mathew E.. No especifíca;
Fil: Panzeri, Stefano. No especifíca;
description Population coding is the quantitative study of which algorithms or representations are used by thebrain to combine together and evaluate the messages carried by different neurons. Here, we review ani nformation-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limitedsampling bias which affects the calculation of information from a limited amount of experimental data.We then discuss how to quantify the contribution of individual members of the population, or theinteraction between them, to the overall information encoded by the considered group of neurons. Wefocus in particular on evaluating what is the contribution of interactions up to any given order to thetotal information. We illustrate this formalism with applications to simulated data with realistic neuronal statistic and  to real simultaneous recordings of multiple spike trains.
publishDate 2010
dc.date.none.fl_str_mv 2010-08
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/11336/280921
Ince, Robin A. A.; Senatore, Riccardo; Arabzadeh, Ehsan; Montani, Fernando Fabián; Diamond, Mathew E.; et al.; Information theoretic methods for studying population codes; Pergamon-Elsevier Science Ltd; Neural Networks; 23; 6; 8-2010; 713-727
0893-6080
CONICET Digital
CONICET
url http://hdl.handle.net/11336/280921
identifier_str_mv Ince, Robin A. A.; Senatore, Riccardo; Arabzadeh, Ehsan; Montani, Fernando Fabián; Diamond, Mathew E.; et al.; Information theoretic methods for studying population codes; Pergamon-Elsevier Science Ltd; Neural Networks; 23; 6; 8-2010; 713-727
0893-6080
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0893608010001012
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neunet.2010.05.008
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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 13.11174

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