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 the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-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 limited sampling 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 the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains.
Instituto de Física La Plata
Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas - Materia
-
Física
Mutual information
Sampling bias
Population coding
Somatosensory cortex - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/160253
Ver los metadatos del registro completo
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Information-theoretic methods for studying population codesInce, Robin A.A.Senatore, RiccardoArabzadeh, EhsanMontani, Fernando FabiánDiamond, Mathew E.Panzeri, StefanoFísicaMutual informationSampling biasPopulation codingSomatosensory cortexPopulation coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-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 limited sampling 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 the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains.Instituto de Física La PlataInstituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas2010info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf713-727http://sedici.unlp.edu.ar/handle/10915/160253enginfo:eu-repo/semantics/altIdentifier/issn/0893-6080info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neunet.2010.05.008info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:41:57Zoai:sedici.unlp.edu.ar:10915/160253Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:41:57.483SEDICI (UNLP) - Universidad Nacional de La Platafalse |
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. Física Mutual information Sampling bias Population coding 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 |
Física Mutual information Sampling bias Population coding Somatosensory cortex |
topic |
Física Mutual information Sampling bias Population coding Somatosensory cortex |
dc.description.none.fl_txt_mv |
Population coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-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 limited sampling 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 the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains. Instituto de Física La Plata Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas |
description |
Population coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-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 limited sampling 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 the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo 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://sedici.unlp.edu.ar/handle/10915/160253 |
url |
http://sedici.unlp.edu.ar/handle/10915/160253 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/issn/0893-6080 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neunet.2010.05.008 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
eu_rights_str_mv |
openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
dc.format.none.fl_str_mv |
application/pdf 713-727 |
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