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Chaotic dynamics in spatially distributed neuronal networks generate population-wide shared variability.
Mosheiff, Noga; Ermentrout, Bard; Huang, Chengcheng.
Afiliação
  • Mosheiff N; Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
  • Ermentrout B; Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America.
  • Huang C; Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
PLoS Comput Biol ; 19(1): e1010843, 2023 Jan.
Article em En | MEDLINE | ID: mdl-36626362
ABSTRACT
Neural activity in the cortex is highly variable in response to repeated stimuli. Population recordings across the cortex demonstrate that the variability of neuronal responses is shared among large groups of neurons and concentrates in a low dimensional space. However, the source of the population-wide shared variability is unknown. In this work, we analyzed the dynamical regimes of spatially distributed networks of excitatory and inhibitory neurons. We found chaotic spatiotemporal dynamics in networks with similar excitatory and inhibitory projection widths, an anatomical feature of the cortex. The chaotic solutions contain broadband frequency power in rate variability and have distance-dependent and low-dimensional correlations, in agreement with experimental findings. In addition, rate chaos can be induced by globally correlated noisy inputs. These results suggest that spatiotemporal chaos in cortical networks can explain the shared variability observed in neuronal population responses.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dinâmica não Linear / Modelos Neurológicos Tipo de estudo: Prognostic_studies Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dinâmica não Linear / Modelos Neurológicos Tipo de estudo: Prognostic_studies Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos