Characterizing spike trains with Lempel-Ziv complexity

We review several applications of Lempel–Ziv complexity to the characterization of neural responses. In particular, Lempel–Ziv complexity allows to estimate the entropy of binned spike trains in an alternative way to the usual method based on the relative frequencies of words, with the definitive ad...

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Detalles Bibliográficos
Autores: Szczepanski, Janusz, Amigó, José María, Wajnryb, Eligiusz, Sánchez-Vives, María V.
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2004
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/288997
Acceso en línea:http://hdl.handle.net/10261/288997
Access Level:acceso abierto
Palabra clave:Lempel–Ziv complexity
Entropy
Spike trains
Neuronal sources
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spelling Characterizing spike trains with Lempel-Ziv complexitySzczepanski, JanuszAmigó, José MaríaWajnryb, EligiuszSánchez-Vives, María V.Lempel–Ziv complexityEntropySpike trainsNeuronal sourcesWe review several applications of Lempel–Ziv complexity to the characterization of neural responses. In particular, Lempel–Ziv complexity allows to estimate the entropy of binned spike trains in an alternative way to the usual method based on the relative frequencies of words, with the definitive advantage of no requiring very long registers. We also use complexity to discriminate neural responses to different kinds of stimuli and to evaluate the number of states of neuronal sources.Peer reviewedElsevierConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232004info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttp://hdl.handle.net/10261/288997reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésNeurocomputinghttps://doi.org/10.1016/j.neucom.2004.01.026Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2889972026-05-22T06:33:51Z
dc.title.none.fl_str_mv Characterizing spike trains with Lempel-Ziv complexity
title Characterizing spike trains with Lempel-Ziv complexity
spellingShingle Characterizing spike trains with Lempel-Ziv complexity
Szczepanski, Janusz
Lempel–Ziv complexity
Entropy
Spike trains
Neuronal sources
title_short Characterizing spike trains with Lempel-Ziv complexity
title_full Characterizing spike trains with Lempel-Ziv complexity
title_fullStr Characterizing spike trains with Lempel-Ziv complexity
title_full_unstemmed Characterizing spike trains with Lempel-Ziv complexity
title_sort Characterizing spike trains with Lempel-Ziv complexity
dc.creator.none.fl_str_mv Szczepanski, Janusz
Amigó, José María
Wajnryb, Eligiusz
Sánchez-Vives, María V.
author Szczepanski, Janusz
author_facet Szczepanski, Janusz
Amigó, José María
Wajnryb, Eligiusz
Sánchez-Vives, María V.
author_role author
author2 Amigó, José María
Wajnryb, Eligiusz
Sánchez-Vives, María V.
author2_role author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Lempel–Ziv complexity
Entropy
Spike trains
Neuronal sources
topic Lempel–Ziv complexity
Entropy
Spike trains
Neuronal sources
description We review several applications of Lempel–Ziv complexity to the characterization of neural responses. In particular, Lempel–Ziv complexity allows to estimate the entropy of binned spike trains in an alternative way to the usual method based on the relative frequencies of words, with the definitive advantage of no requiring very long registers. We also use complexity to discriminate neural responses to different kinds of stimuli and to evaluate the number of states of neuronal sources.
publishDate 2004
dc.date.none.fl_str_mv 2004
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/288997
url http://hdl.handle.net/10261/288997
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Neurocomputing
https://doi.org/10.1016/j.neucom.2004.01.026
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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