On quantification and maximization of information transfer in network dynamical systems.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
05 Apr 2023
05 Apr 2023
Historique:
received:
19
10
2022
accepted:
01
04
2023
medline:
6
4
2023
entrez:
5
4
2023
pubmed:
6
4
2023
Statut:
epublish
Résumé
Information flow among nodes in a complex network describes the overall cause-effect relationships among the nodes and provides a better understanding of the contributions of these nodes individually or collectively towards the underlying network dynamics. Variations in network topologies result in varying information flows among nodes. We integrate theories from information science with control network theory into a framework that enables us to quantify and control the information flows among the nodes in a complex network. The framework explicates the relationships between the network topology and the functional patterns, such as the information transfers in biological networks, information rerouting in sensor nodes, and influence patterns in social networks. We show that by designing or re-configuring the network topology, we can optimize the information transfer function between two chosen nodes. As a proof of concept, we apply our proposed methods in the context of brain networks, where we reconfigure neural circuits to optimize excitation levels among the excitatory neurons.
Identifiants
pubmed: 37019948
doi: 10.1038/s41598-023-32762-7
pii: 10.1038/s41598-023-32762-7
pmc: PMC10076297
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5588Informations de copyright
© 2023. The Author(s).
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