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Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity

Oscillations in the beta/low gamma range (10–45 Hz) are recorded in diverse neural structures. They have successfully been modeled as sparsely synchronized oscillations arising from reciprocal interactions between randomly connected excitatory (E) pyramidal cells and local interneurons (I). The sync...

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Autores principales: Kulkarni, Anirudh, Ranft, Jonas, Hakim, Vincent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7604323/
https://www.ncbi.nlm.nih.gov/pubmed/33192427
http://dx.doi.org/10.3389/fncom.2020.569644
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author Kulkarni, Anirudh
Ranft, Jonas
Hakim, Vincent
author_facet Kulkarni, Anirudh
Ranft, Jonas
Hakim, Vincent
author_sort Kulkarni, Anirudh
collection PubMed
description Oscillations in the beta/low gamma range (10–45 Hz) are recorded in diverse neural structures. They have successfully been modeled as sparsely synchronized oscillations arising from reciprocal interactions between randomly connected excitatory (E) pyramidal cells and local interneurons (I). The synchronization of spatially distant oscillatory spiking E–I modules has been well-studied in the rate model framework but less so for modules of spiking neurons. Here, we first show that previously proposed modifications of rate models provide a quantitative description of spiking E–I modules of Exponential Integrate-and-Fire (EIF) neurons. This allows us to analyze the dynamical regimes of sparsely synchronized oscillatory E–I modules connected by long-range excitatory interactions, for two modules, as well as for a chain of such modules. For modules with a large number of neurons (> 10(5)), we obtain results similar to previously obtained ones based on the classic deterministic Wilson-Cowan rate model, with the added bonus that the results quantitatively describe simulations of spiking EIF neurons. However, for modules with a moderate (~ 10(4)) number of neurons, stochastic variations in the spike emission of neurons are important and need to be taken into account. On the one hand, they modify the oscillations in a way that tends to promote synchronization between different modules. On the other hand, independent fluctuations on different modules tend to disrupt synchronization. The correlations between distant oscillatory modules can be described by stochastic equations for the oscillator phases that have been intensely studied in other contexts. On shorter distances, we develop a description that also takes into account amplitude modes and that quantitatively accounts for our simulation data. Stochastic dephasing of neighboring modules produces transient phase gradients and the transient appearance of phase waves. We propose that these stochastically-induced phase waves provide an explanative framework for the observations of traveling waves in the cortex during beta oscillations.
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spelling pubmed-76043232020-11-13 Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity Kulkarni, Anirudh Ranft, Jonas Hakim, Vincent Front Comput Neurosci Neuroscience Oscillations in the beta/low gamma range (10–45 Hz) are recorded in diverse neural structures. They have successfully been modeled as sparsely synchronized oscillations arising from reciprocal interactions between randomly connected excitatory (E) pyramidal cells and local interneurons (I). The synchronization of spatially distant oscillatory spiking E–I modules has been well-studied in the rate model framework but less so for modules of spiking neurons. Here, we first show that previously proposed modifications of rate models provide a quantitative description of spiking E–I modules of Exponential Integrate-and-Fire (EIF) neurons. This allows us to analyze the dynamical regimes of sparsely synchronized oscillatory E–I modules connected by long-range excitatory interactions, for two modules, as well as for a chain of such modules. For modules with a large number of neurons (> 10(5)), we obtain results similar to previously obtained ones based on the classic deterministic Wilson-Cowan rate model, with the added bonus that the results quantitatively describe simulations of spiking EIF neurons. However, for modules with a moderate (~ 10(4)) number of neurons, stochastic variations in the spike emission of neurons are important and need to be taken into account. On the one hand, they modify the oscillations in a way that tends to promote synchronization between different modules. On the other hand, independent fluctuations on different modules tend to disrupt synchronization. The correlations between distant oscillatory modules can be described by stochastic equations for the oscillator phases that have been intensely studied in other contexts. On shorter distances, we develop a description that also takes into account amplitude modes and that quantitatively accounts for our simulation data. Stochastic dephasing of neighboring modules produces transient phase gradients and the transient appearance of phase waves. We propose that these stochastically-induced phase waves provide an explanative framework for the observations of traveling waves in the cortex during beta oscillations. Frontiers Media S.A. 2020-10-19 /pmc/articles/PMC7604323/ /pubmed/33192427 http://dx.doi.org/10.3389/fncom.2020.569644 Text en Copyright © 2020 Kulkarni, Ranft and Hakim. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Kulkarni, Anirudh
Ranft, Jonas
Hakim, Vincent
Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity
title Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity
title_full Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity
title_fullStr Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity
title_full_unstemmed Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity
title_short Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity
title_sort synchronization, stochasticity, and phase waves in neuronal networks with spatially-structured connectivity
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7604323/
https://www.ncbi.nlm.nih.gov/pubmed/33192427
http://dx.doi.org/10.3389/fncom.2020.569644
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