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ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS

The brain’s ability to tell time and produce complex spatiotemporal motor patterns is critical to 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 within...

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Detalles Bibliográficos
Autores principales: Laje, Rodrigo, Buonomano, Dean V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753043/
https://www.ncbi.nlm.nih.gov/pubmed/23708144
http://dx.doi.org/10.1038/nn.3405
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author Laje, Rodrigo
Buonomano, Dean V.
author_facet Laje, Rodrigo
Buonomano, Dean V.
author_sort Laje, Rodrigo
collection PubMed
description The brain’s ability to tell time and produce complex spatiotemporal motor patterns is critical to 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 within recurrent neural networks. However, the relevant dynamic regimes of recurrent networks are highly sensitive to noise, i.e., chaotic. We describe 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 novel dynamic regime that exhibits coexisting chaotic and locally stable trajectories. These stable patterns function as “dynamic attractors” and provide a novel feature characteristic of biological systems: the ability to “return” to the pattern being generated in the face of perturbations.
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spelling pubmed-37530432014-01-01 ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS Laje, Rodrigo Buonomano, Dean V. Nat Neurosci Article The brain’s ability to tell time and produce complex spatiotemporal motor patterns is critical to 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 within recurrent neural networks. However, the relevant dynamic regimes of recurrent networks are highly sensitive to noise, i.e., chaotic. We describe 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 novel dynamic regime that exhibits coexisting chaotic and locally stable trajectories. These stable patterns function as “dynamic attractors” and provide a novel feature characteristic of biological systems: the ability to “return” to the pattern being generated in the face of perturbations. 2013-05-26 2013-07 /pmc/articles/PMC3753043/ /pubmed/23708144 http://dx.doi.org/10.1038/nn.3405 Text en Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Laje, Rodrigo
Buonomano, Dean V.
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
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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753043/
https://www.ncbi.nlm.nih.gov/pubmed/23708144
http://dx.doi.org/10.1038/nn.3405
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