Assessing the relevance of specific response features in the neural code

Autores
Eyherabide, Hugo Gabriel; Samengo, Ines
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The study of the neural code aims at deciphering how the nervous system maps external stimuli into neural activity-the encoding phase-and subsequently transforms such activity into adequate responses to the original stimuli-the decoding phase. Several information-theoretical methods have been proposed to assess the relevance of individual response features, as for example, the spike count of a given neuron, or the amount of correlation in the activity of two cells. These methods work under the premise that the relevance of a feature is reflected in the information loss that is induced by eliminating the feature from the response. The alternative methods differ in the procedure by which the tested feature is removed, and the algorithm with which the lost information is calculated. Here we compare these methods, and show that more often than not, each method assigns a different relevance to the tested feature. We demonstrate that the differences are both quantitative and qualitative, and connect them with the method employed to remove the tested feature, as well as the procedure to calculate the lost information. By studying a collection of carefully designed examples, and working on analytic derivations, we identify the conditions under which the relevance of features diagnosed by different methods can be ranked, or sometimes even equated. The condition for equality involves both the amount and the type of information contributed by the tested feature. We conclude that the quest for relevant response features is more delicate than previously thought, and may yield to multiple answers depending on methodological subtleties.
Fil: Eyherabide, Hugo Gabriel. University of Helsinki; Finlandia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Samengo, Ines. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
DECODING
DISCRIMINATION
INFORMATION THEORY
MISMATCHED DECODING
NEURAL CODE
NOISE CORRELATIONS
REPRESENTATION
SPIKE-TIME PRECISION
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:11336/98091

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network_name_str CONICET Digital (CONICET)
spelling Assessing the relevance of specific response features in the neural codeEyherabide, Hugo GabrielSamengo, InesDECODINGDISCRIMINATIONINFORMATION THEORYMISMATCHED DECODINGNEURAL CODENOISE CORRELATIONSREPRESENTATIONSPIKE-TIME PRECISIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1The study of the neural code aims at deciphering how the nervous system maps external stimuli into neural activity-the encoding phase-and subsequently transforms such activity into adequate responses to the original stimuli-the decoding phase. Several information-theoretical methods have been proposed to assess the relevance of individual response features, as for example, the spike count of a given neuron, or the amount of correlation in the activity of two cells. These methods work under the premise that the relevance of a feature is reflected in the information loss that is induced by eliminating the feature from the response. The alternative methods differ in the procedure by which the tested feature is removed, and the algorithm with which the lost information is calculated. Here we compare these methods, and show that more often than not, each method assigns a different relevance to the tested feature. We demonstrate that the differences are both quantitative and qualitative, and connect them with the method employed to remove the tested feature, as well as the procedure to calculate the lost information. By studying a collection of carefully designed examples, and working on analytic derivations, we identify the conditions under which the relevance of features diagnosed by different methods can be ranked, or sometimes even equated. The condition for equality involves both the amount and the type of information contributed by the tested feature. We conclude that the quest for relevant response features is more delicate than previously thought, and may yield to multiple answers depending on methodological subtleties.Fil: Eyherabide, Hugo Gabriel. University of Helsinki; Finlandia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Samengo, Ines. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaMolecular Diversity Preservation International2018-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/98091Eyherabide, Hugo Gabriel; Samengo, Ines; Assessing the relevance of specific response features in the neural code; Molecular Diversity Preservation International; Entropy; 20; 11; 11-2018; 1-331099-4300CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1099-4300/20/11/879info:eu-repo/semantics/altIdentifier/doi/10.3390/e20110879info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:11:45Zoai:ri.conicet.gov.ar:11336/98091instacron: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 10:11:45.574CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Assessing the relevance of specific response features in the neural code
title Assessing the relevance of specific response features in the neural code
spellingShingle Assessing the relevance of specific response features in the neural code
Eyherabide, Hugo Gabriel
DECODING
DISCRIMINATION
INFORMATION THEORY
MISMATCHED DECODING
NEURAL CODE
NOISE CORRELATIONS
REPRESENTATION
SPIKE-TIME PRECISION
title_short Assessing the relevance of specific response features in the neural code
title_full Assessing the relevance of specific response features in the neural code
title_fullStr Assessing the relevance of specific response features in the neural code
title_full_unstemmed Assessing the relevance of specific response features in the neural code
title_sort Assessing the relevance of specific response features in the neural code
dc.creator.none.fl_str_mv Eyherabide, Hugo Gabriel
Samengo, Ines
author Eyherabide, Hugo Gabriel
author_facet Eyherabide, Hugo Gabriel
Samengo, Ines
author_role author
author2 Samengo, Ines
author2_role author
dc.subject.none.fl_str_mv DECODING
DISCRIMINATION
INFORMATION THEORY
MISMATCHED DECODING
NEURAL CODE
NOISE CORRELATIONS
REPRESENTATION
SPIKE-TIME PRECISION
topic DECODING
DISCRIMINATION
INFORMATION THEORY
MISMATCHED DECODING
NEURAL CODE
NOISE CORRELATIONS
REPRESENTATION
SPIKE-TIME PRECISION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The study of the neural code aims at deciphering how the nervous system maps external stimuli into neural activity-the encoding phase-and subsequently transforms such activity into adequate responses to the original stimuli-the decoding phase. Several information-theoretical methods have been proposed to assess the relevance of individual response features, as for example, the spike count of a given neuron, or the amount of correlation in the activity of two cells. These methods work under the premise that the relevance of a feature is reflected in the information loss that is induced by eliminating the feature from the response. The alternative methods differ in the procedure by which the tested feature is removed, and the algorithm with which the lost information is calculated. Here we compare these methods, and show that more often than not, each method assigns a different relevance to the tested feature. We demonstrate that the differences are both quantitative and qualitative, and connect them with the method employed to remove the tested feature, as well as the procedure to calculate the lost information. By studying a collection of carefully designed examples, and working on analytic derivations, we identify the conditions under which the relevance of features diagnosed by different methods can be ranked, or sometimes even equated. The condition for equality involves both the amount and the type of information contributed by the tested feature. We conclude that the quest for relevant response features is more delicate than previously thought, and may yield to multiple answers depending on methodological subtleties.
Fil: Eyherabide, Hugo Gabriel. University of Helsinki; Finlandia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Samengo, Ines. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description The study of the neural code aims at deciphering how the nervous system maps external stimuli into neural activity-the encoding phase-and subsequently transforms such activity into adequate responses to the original stimuli-the decoding phase. Several information-theoretical methods have been proposed to assess the relevance of individual response features, as for example, the spike count of a given neuron, or the amount of correlation in the activity of two cells. These methods work under the premise that the relevance of a feature is reflected in the information loss that is induced by eliminating the feature from the response. The alternative methods differ in the procedure by which the tested feature is removed, and the algorithm with which the lost information is calculated. Here we compare these methods, and show that more often than not, each method assigns a different relevance to the tested feature. We demonstrate that the differences are both quantitative and qualitative, and connect them with the method employed to remove the tested feature, as well as the procedure to calculate the lost information. By studying a collection of carefully designed examples, and working on analytic derivations, we identify the conditions under which the relevance of features diagnosed by different methods can be ranked, or sometimes even equated. The condition for equality involves both the amount and the type of information contributed by the tested feature. We conclude that the quest for relevant response features is more delicate than previously thought, and may yield to multiple answers depending on methodological subtleties.
publishDate 2018
dc.date.none.fl_str_mv 2018-11
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/11336/98091
Eyherabide, Hugo Gabriel; Samengo, Ines; Assessing the relevance of specific response features in the neural code; Molecular Diversity Preservation International; Entropy; 20; 11; 11-2018; 1-33
1099-4300
CONICET Digital
CONICET
url http://hdl.handle.net/11336/98091
identifier_str_mv Eyherabide, Hugo Gabriel; Samengo, Ines; Assessing the relevance of specific response features in the neural code; Molecular Diversity Preservation International; Entropy; 20; 11; 11-2018; 1-33
1099-4300
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.mdpi.com/1099-4300/20/11/879
info:eu-repo/semantics/altIdentifier/doi/10.3390/e20110879
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
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
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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