Reservoir computing in simulated neuronal cultures: Effectof network structure

Biological neurons are emerging as attractive candidates for artificial intelligence and machine learning applications given their natural energy efficiency and self-repair capacity. However, they differ from their idealized artificial counterparts. Biological neurons have highly variable and noisy...

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Autores: Mats Houben, Akke, Haeb, Anna-Christina, García Ojalvo, Jordi, Soriano i Fradera, Jordi
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/228267
Acceso en línea:https://hdl.handle.net/2445/228267
Access Level:acceso abierto
Palabra clave:Xarxes neuronals (Neurobiologia)
Neurotecnologia
Xarxes neuronals (Informàtica)
Neural networks (Neurobiology)
Neurotechnology
Neural networks (Computer science)
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spelling Reservoir computing in simulated neuronal cultures: Effectof network structureMats Houben, AkkeHaeb, Anna-ChristinaGarcía Ojalvo, JordiSoriano i Fradera, JordiXarxes neuronals (Neurobiologia)NeurotecnologiaXarxes neuronals (Informàtica)Neural networks (Neurobiology)NeurotechnologyNeural networks (Computer science)Biological neurons are emerging as attractive candidates for artificial intelligence and machine learning applications given their natural energy efficiency and self-repair capacity. However, they differ from their idealized artificial counterparts. Biological neurons have highly variable and noisy dynamics and display intrinsic spontaneous activity instead of purely input-driven dynamics. Moreover, biological neuronal networks have physically constrained and highly plastic connections, leading to a complex and ever evolving connectivity structure. Here, we investigate (numerically and with preliminary experimental data) the stability of the input responses of neuronal cultures using a reservoir computing framework. Utilizing a numerical model for the growth and activity of neuronal cultures, previously used to model experimental data, we investigate the effect of large-scale network topology, specifically homogeneous vs modular architectures, on fading memory, reservoir performance under increasingly noisy dynamics, and robustness to network rewiring. We find that modular networks exhibit longer fading memory time, sustain higher performance under noisy conditions, and are more robust to connectivity rewiring than homogeneous networks. Finally, we observe no relationship between some characteristics of the network adjacency matrix (specifically its spectral properties) and reservoir computing performance.American Institute of Physics (AIP)202620262026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion15 p.application/pdfhttps://hdl.handle.net/2445/228267Articles publicats en revistes (Física de la Matèria Condensada)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1063/5.0278517Chaos, 2026, vol. 36, p. 1-14https://doi.org/10.1063/5.0278517cc-by (c) Mats Houben, Akke, et al, 2026https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2282672026-05-29T05:05:01Z
dc.title.none.fl_str_mv Reservoir computing in simulated neuronal cultures: Effectof network structure
title Reservoir computing in simulated neuronal cultures: Effectof network structure
spellingShingle Reservoir computing in simulated neuronal cultures: Effectof network structure
Mats Houben, Akke
Xarxes neuronals (Neurobiologia)
Neurotecnologia
Xarxes neuronals (Informàtica)
Neural networks (Neurobiology)
Neurotechnology
Neural networks (Computer science)
title_short Reservoir computing in simulated neuronal cultures: Effectof network structure
title_full Reservoir computing in simulated neuronal cultures: Effectof network structure
title_fullStr Reservoir computing in simulated neuronal cultures: Effectof network structure
title_full_unstemmed Reservoir computing in simulated neuronal cultures: Effectof network structure
title_sort Reservoir computing in simulated neuronal cultures: Effectof network structure
dc.creator.none.fl_str_mv Mats Houben, Akke
Haeb, Anna-Christina
García Ojalvo, Jordi
Soriano i Fradera, Jordi
author Mats Houben, Akke
author_facet Mats Houben, Akke
Haeb, Anna-Christina
García Ojalvo, Jordi
Soriano i Fradera, Jordi
author_role author
author2 Haeb, Anna-Christina
García Ojalvo, Jordi
Soriano i Fradera, Jordi
author2_role author
author
author
dc.subject.none.fl_str_mv Xarxes neuronals (Neurobiologia)
Neurotecnologia
Xarxes neuronals (Informàtica)
Neural networks (Neurobiology)
Neurotechnology
Neural networks (Computer science)
topic Xarxes neuronals (Neurobiologia)
Neurotecnologia
Xarxes neuronals (Informàtica)
Neural networks (Neurobiology)
Neurotechnology
Neural networks (Computer science)
description Biological neurons are emerging as attractive candidates for artificial intelligence and machine learning applications given their natural energy efficiency and self-repair capacity. However, they differ from their idealized artificial counterparts. Biological neurons have highly variable and noisy dynamics and display intrinsic spontaneous activity instead of purely input-driven dynamics. Moreover, biological neuronal networks have physically constrained and highly plastic connections, leading to a complex and ever evolving connectivity structure. Here, we investigate (numerically and with preliminary experimental data) the stability of the input responses of neuronal cultures using a reservoir computing framework. Utilizing a numerical model for the growth and activity of neuronal cultures, previously used to model experimental data, we investigate the effect of large-scale network topology, specifically homogeneous vs modular architectures, on fading memory, reservoir performance under increasingly noisy dynamics, and robustness to network rewiring. We find that modular networks exhibit longer fading memory time, sustain higher performance under noisy conditions, and are more robust to connectivity rewiring than homogeneous networks. Finally, we observe no relationship between some characteristics of the network adjacency matrix (specifically its spectral properties) and reservoir computing performance.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026
2026
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/2445/228267
url https://hdl.handle.net/2445/228267
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/10.1063/5.0278517
Chaos, 2026, vol. 36, p. 1-14
https://doi.org/10.1063/5.0278517
dc.rights.none.fl_str_mv cc-by (c) Mats Houben, Akke, et al, 2026
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Mats Houben, Akke, et al, 2026
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 15 p.
application/pdf
dc.publisher.none.fl_str_mv American Institute of Physics (AIP)
publisher.none.fl_str_mv American Institute of Physics (AIP)
dc.source.none.fl_str_mv Articles publicats en revistes (Física de la Matèria Condensada)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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