Cargando…

Coherent chaos in a recurrent neural network with structured connectivity

We present a simple model for coherent, spatially correlated chaos in a recurrent neural network. Networks of randomly connected neurons exhibit chaotic fluctuations and have been studied as a model for capturing the temporal variability of cortical activity. The dynamics generated by such networks,...

Descripción completa

Detalles Bibliográficos
Autores principales: Landau, Itamar Daniel, Sompolinsky, Haim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6307850/
https://www.ncbi.nlm.nih.gov/pubmed/30543634
http://dx.doi.org/10.1371/journal.pcbi.1006309
_version_ 1783383082869456896
author Landau, Itamar Daniel
Sompolinsky, Haim
author_facet Landau, Itamar Daniel
Sompolinsky, Haim
author_sort Landau, Itamar Daniel
collection PubMed
description We present a simple model for coherent, spatially correlated chaos in a recurrent neural network. Networks of randomly connected neurons exhibit chaotic fluctuations and have been studied as a model for capturing the temporal variability of cortical activity. The dynamics generated by such networks, however, are spatially uncorrelated and do not generate coherent fluctuations, which are commonly observed across spatial scales of the neocortex. In our model we introduce a structured component of connectivity, in addition to random connections, which effectively embeds a feedforward structure via unidirectional coupling between a pair of orthogonal modes. Local fluctuations driven by the random connectivity are summed by an output mode and drive coherent activity along an input mode. The orthogonality between input and output mode preserves chaotic fluctuations by preventing feedback loops. In the regime of weak structured connectivity we apply a perturbative approach to solve the dynamic mean-field equations, showing that in this regime coherent fluctuations are driven passively by the chaos of local residual fluctuations. When we introduce a row balance constraint on the random connectivity, stronger structured connectivity puts the network in a distinct dynamical regime of self-tuned coherent chaos. In this regime the coherent component of the dynamics self-adjusts intermittently to yield periods of slow, highly coherent chaos. The dynamics display longer time-scales and switching-like activity. We show how in this regime the dynamics depend qualitatively on the particular realization of the connectivity matrix: a complex leading eigenvalue can yield coherent oscillatory chaos while a real leading eigenvalue can yield chaos with broken symmetry. The level of coherence grows with increasing strength of structured connectivity until the dynamics are almost entirely constrained to a single spatial mode. We examine the effects of network-size scaling and show that these results are not finite-size effects. Finally, we show that in the regime of weak structured connectivity, coherent chaos emerges also for a generalized structured connectivity with multiple input-output modes.
format Online
Article
Text
id pubmed-6307850
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-63078502019-01-08 Coherent chaos in a recurrent neural network with structured connectivity Landau, Itamar Daniel Sompolinsky, Haim PLoS Comput Biol Research Article We present a simple model for coherent, spatially correlated chaos in a recurrent neural network. Networks of randomly connected neurons exhibit chaotic fluctuations and have been studied as a model for capturing the temporal variability of cortical activity. The dynamics generated by such networks, however, are spatially uncorrelated and do not generate coherent fluctuations, which are commonly observed across spatial scales of the neocortex. In our model we introduce a structured component of connectivity, in addition to random connections, which effectively embeds a feedforward structure via unidirectional coupling between a pair of orthogonal modes. Local fluctuations driven by the random connectivity are summed by an output mode and drive coherent activity along an input mode. The orthogonality between input and output mode preserves chaotic fluctuations by preventing feedback loops. In the regime of weak structured connectivity we apply a perturbative approach to solve the dynamic mean-field equations, showing that in this regime coherent fluctuations are driven passively by the chaos of local residual fluctuations. When we introduce a row balance constraint on the random connectivity, stronger structured connectivity puts the network in a distinct dynamical regime of self-tuned coherent chaos. In this regime the coherent component of the dynamics self-adjusts intermittently to yield periods of slow, highly coherent chaos. The dynamics display longer time-scales and switching-like activity. We show how in this regime the dynamics depend qualitatively on the particular realization of the connectivity matrix: a complex leading eigenvalue can yield coherent oscillatory chaos while a real leading eigenvalue can yield chaos with broken symmetry. The level of coherence grows with increasing strength of structured connectivity until the dynamics are almost entirely constrained to a single spatial mode. We examine the effects of network-size scaling and show that these results are not finite-size effects. Finally, we show that in the regime of weak structured connectivity, coherent chaos emerges also for a generalized structured connectivity with multiple input-output modes. Public Library of Science 2018-12-13 /pmc/articles/PMC6307850/ /pubmed/30543634 http://dx.doi.org/10.1371/journal.pcbi.1006309 Text en © 2018 Landau, Sompolinsky http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Landau, Itamar Daniel
Sompolinsky, Haim
Coherent chaos in a recurrent neural network with structured connectivity
title Coherent chaos in a recurrent neural network with structured connectivity
title_full Coherent chaos in a recurrent neural network with structured connectivity
title_fullStr Coherent chaos in a recurrent neural network with structured connectivity
title_full_unstemmed Coherent chaos in a recurrent neural network with structured connectivity
title_short Coherent chaos in a recurrent neural network with structured connectivity
title_sort coherent chaos in a recurrent neural network with structured connectivity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6307850/
https://www.ncbi.nlm.nih.gov/pubmed/30543634
http://dx.doi.org/10.1371/journal.pcbi.1006309
work_keys_str_mv AT landauitamardaniel coherentchaosinarecurrentneuralnetworkwithstructuredconnectivity
AT sompolinskyhaim coherentchaosinarecurrentneuralnetworkwithstructuredconnectivity