Role of connectivity anisotropies in the dynamics of cultured neuronal networks

An inherent challenge in designing laboratory-grown, engineered living neuronal networks lies in predicting the dynamic repertoire of the resulting network and its sensitivity to experimental variables. To fill this gap, and inspired by recent experimental studies, we present a numerical model desig...

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Detalhes bibliográficos
Autores: Houben, Akke Mats, García Ojalvo, Jordi, Soriano i Fradera, Jordi
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2025
País:España
Recursos:Universidad de Barcelona
Repositório:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/225316
Acesso em linha:https://hdl.handle.net/2445/225316
Access Level:Acceso aberto
Palavra-chave:Anisotropia
Neurociències
Xarxes neuronals (Neurobiologia)
Anisotropy
Neurosciences
Neural networks (Neurobiology)
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spelling Role of connectivity anisotropies in the dynamics of cultured neuronal networksHouben, Akke MatsGarcía Ojalvo, JordiSoriano i Fradera, JordiAnisotropiaNeurociènciesXarxes neuronals (Neurobiologia)AnisotropyNeurosciencesNeural networks (Neurobiology)An inherent challenge in designing laboratory-grown, engineered living neuronal networks lies in predicting the dynamic repertoire of the resulting network and its sensitivity to experimental variables. To fill this gap, and inspired by recent experimental studies, we present a numerical model designed to replicate the anisotropies in connectivity introduced through engineering, characterize the emergent collective behavior of the neuronal network, and make predictions. The numerical model is developed to replicate experimental data, and subsequently used to quantify network dynamics in relation to tunable structural and dynamical parameters. These include the strength of imprinted anisotropies, synaptic noise, and average axon lengths. We show that the model successfully captures the behavior of engineered neuronal cultures, revealing a rich repertoire of activity patterns that are highly sensitive to connectivity architecture and noise levels. Specifically, the imprinted anisotropies promote modularity and high clustering coefficients, substantially reducing the pathological-like bursting of standard neuronal cultures, whereas noise and axonal length influence the variability in dynamical states and activity propagation velocities. Moreover, connectivity anisotropies significantly enhance the ability to reconstruct structural connectivity from activity data, an aspect that is important to understand the structure–function relationship in neuronal networks. Our work provides a robust in silico framework to assist experimentalists in the design of in vitro neuronal systems and in anticipating their outcomes. This predictive capability is particularly valuable in developing reliable brain-on-a-chip platforms and in exploring fundamental aspects of neural computation, including input–output relationships and information coding.Public Library of Science (PLoS)2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/225316Articles publicats en revistes (Física de la Matèria Condensada)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1371/journal.pcbi.1012727PLoS Computational Biology, 2025, vol. 21, num.11, p. 1-27https://doi.org/10.1371/journal.pcbi.1012727cc-by (c) Houben, A.M, et al., 2025http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2253162026-05-27T06:46:51Z
dc.title.none.fl_str_mv Role of connectivity anisotropies in the dynamics of cultured neuronal networks
title Role of connectivity anisotropies in the dynamics of cultured neuronal networks
spellingShingle Role of connectivity anisotropies in the dynamics of cultured neuronal networks
Houben, Akke Mats
Anisotropia
Neurociències
Xarxes neuronals (Neurobiologia)
Anisotropy
Neurosciences
Neural networks (Neurobiology)
title_short Role of connectivity anisotropies in the dynamics of cultured neuronal networks
title_full Role of connectivity anisotropies in the dynamics of cultured neuronal networks
title_fullStr Role of connectivity anisotropies in the dynamics of cultured neuronal networks
title_full_unstemmed Role of connectivity anisotropies in the dynamics of cultured neuronal networks
title_sort Role of connectivity anisotropies in the dynamics of cultured neuronal networks
dc.creator.none.fl_str_mv Houben, Akke Mats
García Ojalvo, Jordi
Soriano i Fradera, Jordi
author Houben, Akke Mats
author_facet Houben, Akke Mats
García Ojalvo, Jordi
Soriano i Fradera, Jordi
author_role author
author2 García Ojalvo, Jordi
Soriano i Fradera, Jordi
author2_role author
author
dc.subject.none.fl_str_mv Anisotropia
Neurociències
Xarxes neuronals (Neurobiologia)
Anisotropy
Neurosciences
Neural networks (Neurobiology)
topic Anisotropia
Neurociències
Xarxes neuronals (Neurobiologia)
Anisotropy
Neurosciences
Neural networks (Neurobiology)
description An inherent challenge in designing laboratory-grown, engineered living neuronal networks lies in predicting the dynamic repertoire of the resulting network and its sensitivity to experimental variables. To fill this gap, and inspired by recent experimental studies, we present a numerical model designed to replicate the anisotropies in connectivity introduced through engineering, characterize the emergent collective behavior of the neuronal network, and make predictions. The numerical model is developed to replicate experimental data, and subsequently used to quantify network dynamics in relation to tunable structural and dynamical parameters. These include the strength of imprinted anisotropies, synaptic noise, and average axon lengths. We show that the model successfully captures the behavior of engineered neuronal cultures, revealing a rich repertoire of activity patterns that are highly sensitive to connectivity architecture and noise levels. Specifically, the imprinted anisotropies promote modularity and high clustering coefficients, substantially reducing the pathological-like bursting of standard neuronal cultures, whereas noise and axonal length influence the variability in dynamical states and activity propagation velocities. Moreover, connectivity anisotropies significantly enhance the ability to reconstruct structural connectivity from activity data, an aspect that is important to understand the structure–function relationship in neuronal networks. Our work provides a robust in silico framework to assist experimentalists in the design of in vitro neuronal systems and in anticipating their outcomes. This predictive capability is particularly valuable in developing reliable brain-on-a-chip platforms and in exploring fundamental aspects of neural computation, including input–output relationships and information coding.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/225316
url https://hdl.handle.net/2445/225316
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1371/journal.pcbi.1012727
PLoS Computational Biology, 2025, vol. 21, num.11, p. 1-27
https://doi.org/10.1371/journal.pcbi.1012727
dc.rights.none.fl_str_mv cc-by (c) Houben, A.M, et al., 2025
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Houben, A.M, et al., 2025
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv Articles publicats en revistes (Física de la Matèria Condensada)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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