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Brain connectivity meets reservoir computing

The connectivity of Artificial Neural Networks (ANNs) is different from the one observed in Biological Neural Networks (BNNs). Can the wiring of actual brains help improve ANNs architectures? Can we learn from ANNs about what network features support computation in the brain when solving a task? At...

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Detalles Bibliográficos
Autores principales: Damicelli, Fabrizio, Hilgetag, Claus C., Goulas, Alexandros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710781/
https://www.ncbi.nlm.nih.gov/pubmed/36383563
http://dx.doi.org/10.1371/journal.pcbi.1010639
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author Damicelli, Fabrizio
Hilgetag, Claus C.
Goulas, Alexandros
author_facet Damicelli, Fabrizio
Hilgetag, Claus C.
Goulas, Alexandros
author_sort Damicelli, Fabrizio
collection PubMed
description The connectivity of Artificial Neural Networks (ANNs) is different from the one observed in Biological Neural Networks (BNNs). Can the wiring of actual brains help improve ANNs architectures? Can we learn from ANNs about what network features support computation in the brain when solving a task? At a meso/macro-scale level of the connectivity, ANNs’ architectures are carefully engineered and such those design decisions have crucial importance in many recent performance improvements. On the other hand, BNNs exhibit complex emergent connectivity patterns at all scales. At the individual level, BNNs connectivity results from brain development and plasticity processes, while at the species level, adaptive reconfigurations during evolution also play a major role shaping connectivity. Ubiquitous features of brain connectivity have been identified in recent years, but their role in the brain’s ability to perform concrete computations remains poorly understood. Computational neuroscience studies reveal the influence of specific brain connectivity features only on abstract dynamical properties, although the implications of real brain networks topologies on machine learning or cognitive tasks have been barely explored. Here we present a cross-species study with a hybrid approach integrating real brain connectomes and Bio-Echo State Networks, which we use to solve concrete memory tasks, allowing us to probe the potential computational implications of real brain connectivity patterns on task solving. We find results consistent across species and tasks, showing that biologically inspired networks perform as well as classical echo state networks, provided a minimum level of randomness and diversity of connections is allowed. We also present a framework, bio2art, to map and scale up real connectomes that can be integrated into recurrent ANNs. This approach also allows us to show the crucial importance of the diversity of interareal connectivity patterns, stressing the importance of stochastic processes determining neural networks connectivity in general.
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spelling pubmed-97107812022-12-01 Brain connectivity meets reservoir computing Damicelli, Fabrizio Hilgetag, Claus C. Goulas, Alexandros PLoS Comput Biol Research Article The connectivity of Artificial Neural Networks (ANNs) is different from the one observed in Biological Neural Networks (BNNs). Can the wiring of actual brains help improve ANNs architectures? Can we learn from ANNs about what network features support computation in the brain when solving a task? At a meso/macro-scale level of the connectivity, ANNs’ architectures are carefully engineered and such those design decisions have crucial importance in many recent performance improvements. On the other hand, BNNs exhibit complex emergent connectivity patterns at all scales. At the individual level, BNNs connectivity results from brain development and plasticity processes, while at the species level, adaptive reconfigurations during evolution also play a major role shaping connectivity. Ubiquitous features of brain connectivity have been identified in recent years, but their role in the brain’s ability to perform concrete computations remains poorly understood. Computational neuroscience studies reveal the influence of specific brain connectivity features only on abstract dynamical properties, although the implications of real brain networks topologies on machine learning or cognitive tasks have been barely explored. Here we present a cross-species study with a hybrid approach integrating real brain connectomes and Bio-Echo State Networks, which we use to solve concrete memory tasks, allowing us to probe the potential computational implications of real brain connectivity patterns on task solving. We find results consistent across species and tasks, showing that biologically inspired networks perform as well as classical echo state networks, provided a minimum level of randomness and diversity of connections is allowed. We also present a framework, bio2art, to map and scale up real connectomes that can be integrated into recurrent ANNs. This approach also allows us to show the crucial importance of the diversity of interareal connectivity patterns, stressing the importance of stochastic processes determining neural networks connectivity in general. Public Library of Science 2022-11-16 /pmc/articles/PMC9710781/ /pubmed/36383563 http://dx.doi.org/10.1371/journal.pcbi.1010639 Text en © 2022 Damicelli et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Damicelli, Fabrizio
Hilgetag, Claus C.
Goulas, Alexandros
Brain connectivity meets reservoir computing
title Brain connectivity meets reservoir computing
title_full Brain connectivity meets reservoir computing
title_fullStr Brain connectivity meets reservoir computing
title_full_unstemmed Brain connectivity meets reservoir computing
title_short Brain connectivity meets reservoir computing
title_sort brain connectivity meets reservoir computing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710781/
https://www.ncbi.nlm.nih.gov/pubmed/36383563
http://dx.doi.org/10.1371/journal.pcbi.1010639
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