Quantifying Dynamical High-Order Interdependencies From the O-Information: An Application to Neural Spiking Dynamics.
dynamical systems
information theory
partial information decomposition
spiking neurons
transfer entropy
Journal
Frontiers in physiology
ISSN: 1664-042X
Titre abrégé: Front Physiol
Pays: Switzerland
ID NLM: 101549006
Informations de publication
Date de publication:
2020
2020
Historique:
received:
17
08
2020
accepted:
16
12
2020
entrez:
1
2
2021
pubmed:
2
2
2021
medline:
2
2
2021
Statut:
epublish
Résumé
We address the problem of efficiently and informatively quantifying how multiplets of variables carry information about the future of the dynamical system they belong to. In particular we want to identify groups of variables carrying redundant or synergistic information, and track how the size and the composition of these multiplets changes as the collective behavior of the system evolves. In order to afford a parsimonious expansion of shared information, and at the same time control for lagged interactions and common effect, we develop a dynamical, conditioned version of the O-information, a framework recently proposed to quantify high-order interdependencies via multivariate extension of the mutual information. The dynamic O-information, here introduced, allows to separate multiplets of variables which influence synergistically the future of the system from redundant multiplets. We apply this framework to a dataset of spiking neurons from a monkey performing a perceptual discrimination task. The method identifies synergistic multiplets that include neurons previously categorized as containing little relevant information individually.
Identifiants
pubmed: 33519503
doi: 10.3389/fphys.2020.595736
pmc: PMC7841410
doi:
Types de publication
Journal Article
Langues
eng
Pagination
595736Informations de copyright
Copyright © 2021 Stramaglia, Scagliarini, Daniels and Marinazzo.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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