Attractor Dynamics Driven by Interactivity in Boolean Recurrent Neural Networks
Details
Serval ID
serval:BIB_78DE60C1EAE7
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Attractor Dynamics Driven by Interactivity in Boolean Recurrent Neural Networks
Title of the conference
Artificial Neural Networks and Machine Learning – ICANN 2016
Publisher
Springer International Publishing
Address
Barcelona, Spain
ISBN
978-3-319-44777-3
978-3-319-44778-0
978-3-319-44778-0
ISSN
0302-9743
1611-3349
1611-3349
Publication state
Published
Issued date
2016
Peer-reviewed
Oui
Volume
9886
Series
Lecture Notes in Computer Science
Pages
115-122
Language
english
Abstract
We study the attractor dynamics of a Boolean model of the basal ganglia-thalamocortical network as a function of its interactive synaptic connections and global threshold. We show that the regulation of the interactive feedback and global threshold are significantly involved in the maintenance and robustness of the attractor basin. These results support the hypothesis that, beyond mere structural architecture, global plasticity and interactivity play a crucial role in the computational and dynamical capabilities of biological neural networks.
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Create date
02/08/2017 14:59
Last modification date
20/08/2019 15:35