Attractor-based complexity of a Boolean model of the basal ganglia-thalamocortical network

Détails

ID Serval
serval:BIB_9E52BE6E9B8C
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Attractor-based complexity of a Boolean model of the basal ganglia-thalamocortical network
Titre de la conférence
2016 International Joint Conference on Neural Networks (IJCNN)
Auteur(s)
Cabessa J., Villa A.E.P.
Editeur
IEEE
Adresse
Vancouver, BC, Canada
ISBN
978-1-5090-0620-5
ISSN
2161-4407
Statut éditorial
Publié
Date de publication
07/2016
Peer-reviewed
Oui
Langue
anglais
Résumé
The attractor-based complexity of a Boolean neural network is a measure which refers to the ability of the network to perform more or less complicated classification tasks of its inputs via the manifestation of meaningful or spurious attractor dynamics. Here, we study the attractor-based complexity of a Boolean model of the basal ganglia-thalamocortical network. We show that the regulation of the interactive feedback is significantly involved in the maintenance of an optimal level of complexity. We also show that the complexity of the network depends sensitively on the values of its synaptic connections. These considerations support the general rationale that the synaptic plasticity and the interactive architecture play a crucial role in the computational and dynamical capabilities of biological neural networks.
Mots-clé
Complexity theory, Recurrent neural networks, Biological neural networks, Automata, Neurons, Basal ganglia, neural nets, Boolean functions, computational complexity
Pubmed
Web of science
Création de la notice
03/08/2017 12:53
Dernière modification de la notice
20/08/2019 15:04
Données d'usage