Quantifying higher-order correlations in a neuronal pool
- Autores
- Montangie, Lisandro; Montani, Fernando Fabián
- Año de publicación
- 2015
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Recent experiments involving a relatively large population of neurons have shown a very significant amount of higher-order correlations. However, little is known of how these affect the integration and firing behavior of a population of neurons beyond the second order statistics. To investigate how higher-order inputs statistics can shape beyond pairwise spike correlations and affect information coding in the brain, we consider a neuronal pool where each neuron fires stochastically. We develop a simple mathematically tractable model that makes it feasible to account for higher-order spike correlations in a neuronal pool with highly interconnected common inputs beyond second order statistics. In our model, correlations between neurons appear from q-Gaussian inputs into threshold neurons. The approach constitutes the natural extension of the Dichotomized Gaussian model, where the inputs to the model are just Gaussian distributed and therefore have no input interactions beyond second order. We obtain an exact analytical expression for the joint distribution of firing, quantifying the degree of higher-order spike correlations, truly emphasizing the functional aspects of higher-order statistics, as we account for beyond second order inputs correlations seen by each neuron within the pool. We determine how higher-order correlations depend on the interaction structure of the input, showing that the joint distribution of firing is skewed as the parameter q increases inducing larger excursions of synchronized spikes. We show how input nonlinearities can shape higher-order correlations and enhance coding performance by neural populations.
Fil: Montangie, Lisandro. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina
Fil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina - Materia
-
EXTENDED CENTRAL LIMIT THEOREM
HIGHER ORDER CORRELATIONS
INFORMATION GEOMETRY
LARGE NEURAL ENSEMBLE - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/148575
Ver los metadatos del registro completo
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Quantifying higher-order correlations in a neuronal poolMontangie, LisandroMontani, Fernando FabiánEXTENDED CENTRAL LIMIT THEOREMHIGHER ORDER CORRELATIONSINFORMATION GEOMETRYLARGE NEURAL ENSEMBLEhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Recent experiments involving a relatively large population of neurons have shown a very significant amount of higher-order correlations. However, little is known of how these affect the integration and firing behavior of a population of neurons beyond the second order statistics. To investigate how higher-order inputs statistics can shape beyond pairwise spike correlations and affect information coding in the brain, we consider a neuronal pool where each neuron fires stochastically. We develop a simple mathematically tractable model that makes it feasible to account for higher-order spike correlations in a neuronal pool with highly interconnected common inputs beyond second order statistics. In our model, correlations between neurons appear from q-Gaussian inputs into threshold neurons. The approach constitutes the natural extension of the Dichotomized Gaussian model, where the inputs to the model are just Gaussian distributed and therefore have no input interactions beyond second order. We obtain an exact analytical expression for the joint distribution of firing, quantifying the degree of higher-order spike correlations, truly emphasizing the functional aspects of higher-order statistics, as we account for beyond second order inputs correlations seen by each neuron within the pool. We determine how higher-order correlations depend on the interaction structure of the input, showing that the joint distribution of firing is skewed as the parameter q increases inducing larger excursions of synchronized spikes. We show how input nonlinearities can shape higher-order correlations and enhance coding performance by neural populations.Fil: Montangie, Lisandro. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; ArgentinaFil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; ArgentinaElsevier Science2015-03-01info: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/11336/148575Montangie, Lisandro; Montani, Fernando Fabián; Quantifying higher-order correlations in a neuronal pool; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 421; 1; 1-3-2015; 388-4000378-4371CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0378437114010048info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2014.11.046info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:36:20Zoai:ri.conicet.gov.ar:11336/148575instacron: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 09:36:21.264CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Quantifying higher-order correlations in a neuronal pool |
title |
Quantifying higher-order correlations in a neuronal pool |
spellingShingle |
Quantifying higher-order correlations in a neuronal pool Montangie, Lisandro EXTENDED CENTRAL LIMIT THEOREM HIGHER ORDER CORRELATIONS INFORMATION GEOMETRY LARGE NEURAL ENSEMBLE |
title_short |
Quantifying higher-order correlations in a neuronal pool |
title_full |
Quantifying higher-order correlations in a neuronal pool |
title_fullStr |
Quantifying higher-order correlations in a neuronal pool |
title_full_unstemmed |
Quantifying higher-order correlations in a neuronal pool |
title_sort |
Quantifying higher-order correlations in a neuronal pool |
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 |
EXTENDED CENTRAL LIMIT THEOREM HIGHER ORDER CORRELATIONS INFORMATION GEOMETRY LARGE NEURAL ENSEMBLE |
topic |
EXTENDED CENTRAL LIMIT THEOREM HIGHER ORDER CORRELATIONS INFORMATION GEOMETRY LARGE NEURAL ENSEMBLE |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Recent experiments involving a relatively large population of neurons have shown a very significant amount of higher-order correlations. However, little is known of how these affect the integration and firing behavior of a population of neurons beyond the second order statistics. To investigate how higher-order inputs statistics can shape beyond pairwise spike correlations and affect information coding in the brain, we consider a neuronal pool where each neuron fires stochastically. We develop a simple mathematically tractable model that makes it feasible to account for higher-order spike correlations in a neuronal pool with highly interconnected common inputs beyond second order statistics. In our model, correlations between neurons appear from q-Gaussian inputs into threshold neurons. The approach constitutes the natural extension of the Dichotomized Gaussian model, where the inputs to the model are just Gaussian distributed and therefore have no input interactions beyond second order. We obtain an exact analytical expression for the joint distribution of firing, quantifying the degree of higher-order spike correlations, truly emphasizing the functional aspects of higher-order statistics, as we account for beyond second order inputs correlations seen by each neuron within the pool. We determine how higher-order correlations depend on the interaction structure of the input, showing that the joint distribution of firing is skewed as the parameter q increases inducing larger excursions of synchronized spikes. We show how input nonlinearities can shape higher-order correlations and enhance coding performance by neural populations. Fil: Montangie, Lisandro. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina Fil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina |
description |
Recent experiments involving a relatively large population of neurons have shown a very significant amount of higher-order correlations. However, little is known of how these affect the integration and firing behavior of a population of neurons beyond the second order statistics. To investigate how higher-order inputs statistics can shape beyond pairwise spike correlations and affect information coding in the brain, we consider a neuronal pool where each neuron fires stochastically. We develop a simple mathematically tractable model that makes it feasible to account for higher-order spike correlations in a neuronal pool with highly interconnected common inputs beyond second order statistics. In our model, correlations between neurons appear from q-Gaussian inputs into threshold neurons. The approach constitutes the natural extension of the Dichotomized Gaussian model, where the inputs to the model are just Gaussian distributed and therefore have no input interactions beyond second order. We obtain an exact analytical expression for the joint distribution of firing, quantifying the degree of higher-order spike correlations, truly emphasizing the functional aspects of higher-order statistics, as we account for beyond second order inputs correlations seen by each neuron within the pool. We determine how higher-order correlations depend on the interaction structure of the input, showing that the joint distribution of firing is skewed as the parameter q increases inducing larger excursions of synchronized spikes. We show how input nonlinearities can shape higher-order correlations and enhance coding performance by neural populations. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-03-01 |
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/11336/148575 Montangie, Lisandro; Montani, Fernando Fabián; Quantifying higher-order correlations in a neuronal pool; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 421; 1; 1-3-2015; 388-400 0378-4371 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/148575 |
identifier_str_mv |
Montangie, Lisandro; Montani, Fernando Fabián; Quantifying higher-order correlations in a neuronal pool; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 421; 1; 1-3-2015; 388-400 0378-4371 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0378437114010048 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2014.11.046 |
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 |
Elsevier Science |
publisher.none.fl_str_mv |
Elsevier Science |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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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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13.070432 |