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Neural field models with transmission delays and diffusion

A neural field models the large scale behaviour of large groups of neurons. We extend previous results for these models by including a diffusion term into the neural field, which models direct, electrical connections. We extend known and prove new sun-star calculus results for delay equations to be...

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Autores principales: Spek, Len, Kuznetsov, Yuri A., van Gils, Stephan A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7726065/
https://www.ncbi.nlm.nih.gov/pubmed/33296032
http://dx.doi.org/10.1186/s13408-020-00098-5
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author Spek, Len
Kuznetsov, Yuri A.
van Gils, Stephan A.
author_facet Spek, Len
Kuznetsov, Yuri A.
van Gils, Stephan A.
author_sort Spek, Len
collection PubMed
description A neural field models the large scale behaviour of large groups of neurons. We extend previous results for these models by including a diffusion term into the neural field, which models direct, electrical connections. We extend known and prove new sun-star calculus results for delay equations to be able to include diffusion and explicitly characterise the essential spectrum. For a certain class of connectivity functions in the neural field model, we are able to compute its spectral properties and the first Lyapunov coefficient of a Hopf bifurcation. By examining a numerical example, we find that the addition of diffusion suppresses non-synchronised steady-states while favouring synchronised oscillatory modes.
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spelling pubmed-77260652020-12-17 Neural field models with transmission delays and diffusion Spek, Len Kuznetsov, Yuri A. van Gils, Stephan A. J Math Neurosci Research A neural field models the large scale behaviour of large groups of neurons. We extend previous results for these models by including a diffusion term into the neural field, which models direct, electrical connections. We extend known and prove new sun-star calculus results for delay equations to be able to include diffusion and explicitly characterise the essential spectrum. For a certain class of connectivity functions in the neural field model, we are able to compute its spectral properties and the first Lyapunov coefficient of a Hopf bifurcation. By examining a numerical example, we find that the addition of diffusion suppresses non-synchronised steady-states while favouring synchronised oscillatory modes. Springer Berlin Heidelberg 2020-12-09 /pmc/articles/PMC7726065/ /pubmed/33296032 http://dx.doi.org/10.1186/s13408-020-00098-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Research
Spek, Len
Kuznetsov, Yuri A.
van Gils, Stephan A.
Neural field models with transmission delays and diffusion
title Neural field models with transmission delays and diffusion
title_full Neural field models with transmission delays and diffusion
title_fullStr Neural field models with transmission delays and diffusion
title_full_unstemmed Neural field models with transmission delays and diffusion
title_short Neural field models with transmission delays and diffusion
title_sort neural field models with transmission delays and diffusion
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7726065/
https://www.ncbi.nlm.nih.gov/pubmed/33296032
http://dx.doi.org/10.1186/s13408-020-00098-5
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