Open AccessOptimizing information processing in neuronal networks beyond critical states
Author(s) -
Mariana Sacrini Ayres Ferraz,
Hiago Lucas Cardeal Melo-Silva,
Alexandre Hiroaki Kihara
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0184367
Subject(s) - transfer entropy , information processing , computer science , entropy (arrow of time) , network dynamics , information theory , complex network , functional connectivity , principle of maximum entropy , statistical physics , biological system , neuroscience , artificial intelligence , physics , biology , mathematics , statistics , discrete mathematics , quantum mechanics , world wide web
Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states.