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Dynamics of Structured Networks of Winfree Oscillators

Winfree oscillators are phase oscillator models of neurons, characterized by their phase response curve and pulsatile interaction function. We use the Ott/Antonsen ansatz to study large heterogeneous networks of Winfree oscillators, deriving low-dimensional differential equations which describe the...

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Detalles Bibliográficos
Autores principales: Laing, Carlo R., Bläsche, Christian, Means, Shawn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902706/
https://www.ncbi.nlm.nih.gov/pubmed/33643004
http://dx.doi.org/10.3389/fnsys.2021.631377
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author Laing, Carlo R.
Bläsche, Christian
Means, Shawn
author_facet Laing, Carlo R.
Bläsche, Christian
Means, Shawn
author_sort Laing, Carlo R.
collection PubMed
description Winfree oscillators are phase oscillator models of neurons, characterized by their phase response curve and pulsatile interaction function. We use the Ott/Antonsen ansatz to study large heterogeneous networks of Winfree oscillators, deriving low-dimensional differential equations which describe the evolution of the expected state of networks of oscillators. We consider the effects of correlations between an oscillator's in-degree and out-degree, and between the in- and out-degrees of an “upstream” and a “downstream” oscillator (degree assortativity). We also consider correlated heterogeneity, where some property of an oscillator is correlated with a structural property such as degree. We finally consider networks with parameter assortativity, coupling oscillators according to their intrinsic frequencies. The results show how different types of network structure influence its overall dynamics.
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spelling pubmed-79027062021-02-25 Dynamics of Structured Networks of Winfree Oscillators Laing, Carlo R. Bläsche, Christian Means, Shawn Front Syst Neurosci Neuroscience Winfree oscillators are phase oscillator models of neurons, characterized by their phase response curve and pulsatile interaction function. We use the Ott/Antonsen ansatz to study large heterogeneous networks of Winfree oscillators, deriving low-dimensional differential equations which describe the evolution of the expected state of networks of oscillators. We consider the effects of correlations between an oscillator's in-degree and out-degree, and between the in- and out-degrees of an “upstream” and a “downstream” oscillator (degree assortativity). We also consider correlated heterogeneity, where some property of an oscillator is correlated with a structural property such as degree. We finally consider networks with parameter assortativity, coupling oscillators according to their intrinsic frequencies. The results show how different types of network structure influence its overall dynamics. Frontiers Media S.A. 2021-02-10 /pmc/articles/PMC7902706/ /pubmed/33643004 http://dx.doi.org/10.3389/fnsys.2021.631377 Text en Copyright © 2021 Laing, Bläsche and Means. 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
Laing, Carlo R.
Bläsche, Christian
Means, Shawn
Dynamics of Structured Networks of Winfree Oscillators
title Dynamics of Structured Networks of Winfree Oscillators
title_full Dynamics of Structured Networks of Winfree Oscillators
title_fullStr Dynamics of Structured Networks of Winfree Oscillators
title_full_unstemmed Dynamics of Structured Networks of Winfree Oscillators
title_short Dynamics of Structured Networks of Winfree Oscillators
title_sort dynamics of structured networks of winfree oscillators
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902706/
https://www.ncbi.nlm.nih.gov/pubmed/33643004
http://dx.doi.org/10.3389/fnsys.2021.631377
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