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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/86221
Ver los metadatos del registro completo
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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 |
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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 |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.070432 |