Robust timing and motor patterns by taming chaos in recurrent neural networks

Autores
Laje, Rodrigo; Buonomano, Dean V.
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The brain's ability to tell time and produce complex spatiotemporal motor patterns is critical for anticipating the next ring of a telephone or playing a musical instrument. One class of models proposes that these abilities emerge from dynamically changing patterns of neural activity generated in recurrent neural networks. However, the relevant dynamic regimes of recurrent networks are highly sensitive to noise; that is, chaotic. We developed a firing rate model that tells time on the order of seconds and generates complex spatiotemporal patterns in the presence of high levels of noise. This is achieved through the tuning of the recurrent connections. The network operates in a dynamic regime that exhibits coexisting chaotic and locally stable trajectories. These stable patterns function as 'dynamic attractors' and provide a feature that is characteristic of biological systems: the ability to 'return' to the pattern being generated in the face of perturbations.
Fil: Laje, Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. University of California at Los Angeles. School of Medicine. Department of Neurobiology; Estados Unidos
Fil: Buonomano, Dean V.. University of California at Los Angeles. School of Medicine. Department of Neurobiology; Estados Unidos
Materia
NEUROSCIENCE
TIME PROCESSING
NEURAL NETWORKS
NONLINEAR DYNAMICS
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:11336/86221

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network_name_str CONICET Digital (CONICET)
spelling Robust timing and motor patterns by taming chaos in recurrent neural networksLaje, RodrigoBuonomano, Dean V.NEUROSCIENCETIME PROCESSINGNEURAL NETWORKSNONLINEAR DYNAMICShttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1The brain's ability to tell time and produce complex spatiotemporal motor patterns is critical for anticipating the next ring of a telephone or playing a musical instrument. One class of models proposes that these abilities emerge from dynamically changing patterns of neural activity generated in recurrent neural networks. However, the relevant dynamic regimes of recurrent networks are highly sensitive to noise; that is, chaotic. We developed a firing rate model that tells time on the order of seconds and generates complex spatiotemporal patterns in the presence of high levels of noise. This is achieved through the tuning of the recurrent connections. The network operates in a dynamic regime that exhibits coexisting chaotic and locally stable trajectories. These stable patterns function as 'dynamic attractors' and provide a feature that is characteristic of biological systems: the ability to 'return' to the pattern being generated in the face of perturbations.Fil: Laje, Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. University of California at Los Angeles. School of Medicine. Department of Neurobiology; Estados UnidosFil: Buonomano, Dean V.. University of California at Los Angeles. School of Medicine. Department of Neurobiology; Estados UnidosNature Publishing Group2013-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/86221Laje, Rodrigo; Buonomano, Dean V.; Robust timing and motor patterns by taming chaos in recurrent neural networks; Nature Publishing Group; Nature Neuroscience.; 16; 7; 7-2013; 925-9331097-6256CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1038/nn.3405info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/nn.3405info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:36:31Zoai:ri.conicet.gov.ar:11336/86221instacron: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 10:36:31.699CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust timing and motor patterns by taming chaos in recurrent neural networks
title Robust timing and motor patterns by taming chaos in recurrent neural networks
spellingShingle Robust timing and motor patterns by taming chaos in recurrent neural networks
Laje, Rodrigo
NEUROSCIENCE
TIME PROCESSING
NEURAL NETWORKS
NONLINEAR DYNAMICS
title_short Robust timing and motor patterns by taming chaos in recurrent neural networks
title_full Robust timing and motor patterns by taming chaos in recurrent neural networks
title_fullStr Robust timing and motor patterns by taming chaos in recurrent neural networks
title_full_unstemmed Robust timing and motor patterns by taming chaos in recurrent neural networks
title_sort Robust timing and motor patterns by taming chaos in recurrent neural networks
dc.creator.none.fl_str_mv Laje, Rodrigo
Buonomano, Dean V.
author Laje, Rodrigo
author_facet Laje, Rodrigo
Buonomano, Dean V.
author_role author
author2 Buonomano, Dean V.
author2_role author
dc.subject.none.fl_str_mv NEUROSCIENCE
TIME PROCESSING
NEURAL NETWORKS
NONLINEAR DYNAMICS
topic NEUROSCIENCE
TIME PROCESSING
NEURAL NETWORKS
NONLINEAR DYNAMICS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The brain's ability to tell time and produce complex spatiotemporal motor patterns is critical for anticipating the next ring of a telephone or playing a musical instrument. One class of models proposes that these abilities emerge from dynamically changing patterns of neural activity generated in recurrent neural networks. However, the relevant dynamic regimes of recurrent networks are highly sensitive to noise; that is, chaotic. We developed a firing rate model that tells time on the order of seconds and generates complex spatiotemporal patterns in the presence of high levels of noise. This is achieved through the tuning of the recurrent connections. The network operates in a dynamic regime that exhibits coexisting chaotic and locally stable trajectories. These stable patterns function as 'dynamic attractors' and provide a feature that is characteristic of biological systems: the ability to 'return' to the pattern being generated in the face of perturbations.
Fil: Laje, Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. University of California at Los Angeles. School of Medicine. Department of Neurobiology; Estados Unidos
Fil: Buonomano, Dean V.. University of California at Los Angeles. School of Medicine. Department of Neurobiology; Estados Unidos
description The brain's ability to tell time and produce complex spatiotemporal motor patterns is critical for anticipating the next ring of a telephone or playing a musical instrument. One class of models proposes that these abilities emerge from dynamically changing patterns of neural activity generated in recurrent neural networks. However, the relevant dynamic regimes of recurrent networks are highly sensitive to noise; that is, chaotic. We developed a firing rate model that tells time on the order of seconds and generates complex spatiotemporal patterns in the presence of high levels of noise. This is achieved through the tuning of the recurrent connections. The network operates in a dynamic regime that exhibits coexisting chaotic and locally stable trajectories. These stable patterns function as 'dynamic attractors' and provide a feature that is characteristic of biological systems: the ability to 'return' to the pattern being generated in the face of perturbations.
publishDate 2013
dc.date.none.fl_str_mv 2013-07
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/11336/86221
Laje, Rodrigo; Buonomano, Dean V.; Robust timing and motor patterns by taming chaos in recurrent neural networks; Nature Publishing Group; Nature Neuroscience.; 16; 7; 7-2013; 925-933
1097-6256
CONICET Digital
CONICET
url http://hdl.handle.net/11336/86221
identifier_str_mv Laje, Rodrigo; Buonomano, Dean V.; Robust timing and motor patterns by taming chaos in recurrent neural networks; Nature Publishing Group; Nature Neuroscience.; 16; 7; 7-2013; 925-933
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/doi/10.1038/nn.3405
info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/nn.3405
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
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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