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

Details

Serval ID
serval:BIB_9E52BE6E9B8C
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Attractor-based complexity of a Boolean model of the basal ganglia-thalamocortical network
Title of the conference
2016 International Joint Conference on Neural Networks (IJCNN)
Author(s)
Cabessa J., Villa A.E.P.
Publisher
IEEE
Address
Vancouver, BC, Canada
ISBN
978-1-5090-0620-5
ISSN
2161-4407
Publication state
Published
Issued date
07/2016
Peer-reviewed
Oui
Language
english
Abstract
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.
Keywords
Complexity theory, Recurrent neural networks, Biological neural networks, Automata, Neurons, Basal ganglia, neural nets, Boolean functions, computational complexity
Pubmed
Web of science
Create date
03/08/2017 12:53
Last modification date
20/08/2019 15:04
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