Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference

Autores
Echeveste, Rodrigo Sebastián; Aitchison, Laurence; Hennequin, Guillaume; Lengyel, Máté
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory?inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function?fast sampling-based inference?and predict further properties of these motifs that can be tested in future experiments.
Fil: Echeveste, Rodrigo Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Aitchison, Laurence. University of Cambridge; Reino Unido
Fil: Hennequin, Guillaume. University of Cambridge; Estados Unidos
Fil: Lengyel, Máté. University of Cambridge; Reino Unido
Materia
Neural Networks
Cortical Dynamics
Bayesian Inference
Optimization
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:11336/114008
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inferenceEcheveste, Rodrigo SebastiánAitchison, LaurenceHennequin, GuillaumeLengyel, MátéNeural NetworksCortical DynamicsBayesian InferenceOptimizationhttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory?inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function?fast sampling-based inference?and predict further properties of these motifs that can be tested in future experiments.Fil: Echeveste, Rodrigo Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Aitchison, Laurence. University of Cambridge; Reino UnidoFil: Hennequin, Guillaume. University of Cambridge; Estados UnidosFil: Lengyel, Máté. University of Cambridge; Reino UnidoNature Publishing Group2020-08info: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/11336/114008Echeveste, Rodrigo Sebastián; Aitchison, Laurence; Hennequin, Guillaume; Lengyel, Máté; Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference; Nature Publishing Group; Nature Neuroscience.; 23; 9; 8-2020; 1138-11491097-6256CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41593-020-0671-1info:eu-repo/semantics/altIdentifier/doi/10.1038/s41593-020-0671-1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:41:41Zoai:ri.conicet.gov.ar:11336/114008instacron: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 10:41:42.171CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
title Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
spellingShingle Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
Echeveste, Rodrigo Sebastián
Neural Networks
Cortical Dynamics
Bayesian Inference
Optimization
title_short Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
title_full Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
title_fullStr Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
title_full_unstemmed Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
title_sort Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
dc.creator.none.fl_str_mv Echeveste, Rodrigo Sebastián
Aitchison, Laurence
Hennequin, Guillaume
Lengyel, Máté
author Echeveste, Rodrigo Sebastián
author_facet Echeveste, Rodrigo Sebastián
Aitchison, Laurence
Hennequin, Guillaume
Lengyel, Máté
author_role author
author2 Aitchison, Laurence
Hennequin, Guillaume
Lengyel, Máté
author2_role author
author
author
dc.subject.none.fl_str_mv Neural Networks
Cortical Dynamics
Bayesian Inference
Optimization
topic Neural Networks
Cortical Dynamics
Bayesian Inference
Optimization
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory?inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function?fast sampling-based inference?and predict further properties of these motifs that can be tested in future experiments.
Fil: Echeveste, Rodrigo Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Aitchison, Laurence. University of Cambridge; Reino Unido
Fil: Hennequin, Guillaume. University of Cambridge; Estados Unidos
Fil: Lengyel, Máté. University of Cambridge; Reino Unido
description Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory?inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function?fast sampling-based inference?and predict further properties of these motifs that can be tested in future experiments.
publishDate 2020
dc.date.none.fl_str_mv 2020-08
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/11336/114008
Echeveste, Rodrigo Sebastián; Aitchison, Laurence; Hennequin, Guillaume; Lengyel, Máté; Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference; Nature Publishing Group; Nature Neuroscience.; 23; 9; 8-2020; 1138-1149
1097-6256
CONICET Digital
CONICET
url http://hdl.handle.net/11336/114008
identifier_str_mv Echeveste, Rodrigo Sebastián; Aitchison, Laurence; Hennequin, Guillaume; Lengyel, Máté; Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference; Nature Publishing Group; Nature Neuroscience.; 23; 9; 8-2020; 1138-1149
1097-6256
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.nature.com/articles/s41593-020-0671-1
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41593-020-0671-1
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 Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
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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score 13.070432

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