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Adaptive rewiring evolves brain-like structure in weighted networks
Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections. We model their common principle in terms of adaptive rewiring of network connectivity, while representing neural activity by diffusion on the network: Where diffusion is intensive, shortcut c...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7142112/ https://www.ncbi.nlm.nih.gov/pubmed/32269235 http://dx.doi.org/10.1038/s41598-020-62204-7 |
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author | Rentzeperis, Ilias van Leeuwen, Cees |
author_facet | Rentzeperis, Ilias van Leeuwen, Cees |
author_sort | Rentzeperis, Ilias |
collection | PubMed |
description | Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections. We model their common principle in terms of adaptive rewiring of network connectivity, while representing neural activity by diffusion on the network: Where diffusion is intensive, shortcut connections are established, while underused connections are pruned. In binary networks, this process is known to steer initially random networks robustly to high levels of structural complexity, reflecting the global characteristics of brain anatomy: modular or centralized small world topologies. We investigate whether this result extends to more realistic, weighted networks. Both normally- and lognormally-distributed weighted networks evolve either modular or centralized topologies. Which of these prevails depends on a single control parameter, representing global homeostatic or normalizing regulation mechanisms. Intermediate control parameter values exhibit the greatest levels of network complexity, incorporating both modular and centralized tendencies. The simulation results allow us to propose diffusion based adaptive rewiring as a parsimonious model for activity-dependent reshaping of brain connectivity structure. |
format | Online Article Text |
id | pubmed-7142112 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-71421122020-04-11 Adaptive rewiring evolves brain-like structure in weighted networks Rentzeperis, Ilias van Leeuwen, Cees Sci Rep Article Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections. We model their common principle in terms of adaptive rewiring of network connectivity, while representing neural activity by diffusion on the network: Where diffusion is intensive, shortcut connections are established, while underused connections are pruned. In binary networks, this process is known to steer initially random networks robustly to high levels of structural complexity, reflecting the global characteristics of brain anatomy: modular or centralized small world topologies. We investigate whether this result extends to more realistic, weighted networks. Both normally- and lognormally-distributed weighted networks evolve either modular or centralized topologies. Which of these prevails depends on a single control parameter, representing global homeostatic or normalizing regulation mechanisms. Intermediate control parameter values exhibit the greatest levels of network complexity, incorporating both modular and centralized tendencies. The simulation results allow us to propose diffusion based adaptive rewiring as a parsimonious model for activity-dependent reshaping of brain connectivity structure. Nature Publishing Group UK 2020-04-08 /pmc/articles/PMC7142112/ /pubmed/32269235 http://dx.doi.org/10.1038/s41598-020-62204-7 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Rentzeperis, Ilias van Leeuwen, Cees Adaptive rewiring evolves brain-like structure in weighted networks |
title | Adaptive rewiring evolves brain-like structure in weighted networks |
title_full | Adaptive rewiring evolves brain-like structure in weighted networks |
title_fullStr | Adaptive rewiring evolves brain-like structure in weighted networks |
title_full_unstemmed | Adaptive rewiring evolves brain-like structure in weighted networks |
title_short | Adaptive rewiring evolves brain-like structure in weighted networks |
title_sort | adaptive rewiring evolves brain-like structure in weighted networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7142112/ https://www.ncbi.nlm.nih.gov/pubmed/32269235 http://dx.doi.org/10.1038/s41598-020-62204-7 |
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