Higher-order correlations in common input shapes the output spiking activity of a neural population

Autores
Montangie, Lisandro; Montani, Fernando Fabián
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from -Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter . We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.
Instituto de Física de Líquidos y Sistemas Biológicos
Materia
Física
Higher-order correlations
Extended Central Limit Theorem
Large neural ensemble
Information geometry
Neuronal inputs
Spiking outputs
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/160308

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spelling Higher-order correlations in common input shapes the output spiking activity of a neural populationMontangie, LisandroMontani, Fernando FabiánFísicaHigher-order correlationsExtended Central Limit TheoremLarge neural ensembleInformation geometryNeuronal inputsSpiking outputsRecent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from -Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter . We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.Instituto de Física de Líquidos y Sistemas Biológicos2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/160308enginfo:eu-repo/semantics/altIdentifier/issn/0378-4371info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2016.12.002info: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:10915/160308Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:41:57.609SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Higher-order correlations in common input shapes the output spiking activity of a neural population
title Higher-order correlations in common input shapes the output spiking activity of a neural population
spellingShingle Higher-order correlations in common input shapes the output spiking activity of a neural population
Montangie, Lisandro
Física
Higher-order correlations
Extended Central Limit Theorem
Large neural ensemble
Information geometry
Neuronal inputs
Spiking outputs
title_short Higher-order correlations in common input shapes the output spiking activity of a neural population
title_full Higher-order correlations in common input shapes the output spiking activity of a neural population
title_fullStr Higher-order correlations in common input shapes the output spiking activity of a neural population
title_full_unstemmed Higher-order correlations in common input shapes the output spiking activity of a neural population
title_sort Higher-order correlations in common input shapes the output spiking activity of a neural population
dc.creator.none.fl_str_mv Montangie, Lisandro
Montani, Fernando Fabián
author Montangie, Lisandro
author_facet Montangie, Lisandro
Montani, Fernando Fabián
author_role author
author2 Montani, Fernando Fabián
author2_role author
dc.subject.none.fl_str_mv Física
Higher-order correlations
Extended Central Limit Theorem
Large neural ensemble
Information geometry
Neuronal inputs
Spiking outputs
topic Física
Higher-order correlations
Extended Central Limit Theorem
Large neural ensemble
Information geometry
Neuronal inputs
Spiking outputs
dc.description.none.fl_txt_mv Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from -Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter . We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.
Instituto de Física de Líquidos y Sistemas Biológicos
description Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from -Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter . We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.
publishDate 2017
dc.date.none.fl_str_mv 2017
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info:eu-repo/semantics/publishedVersion
Articulo
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format article
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/0378-4371
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2016.12.002
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
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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