Optimizing information processing in neuronal networks beyond critical states

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 p...

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Bibliographic Details
Published in:PloS one 2017-09, Vol.12 (9), p.e0184367-e0184367
Main Authors: Ferraz, Mariana Sacrini Ayres, Melo-Silva, Hiago Lucas Cardeal, Kihara, Alexandre Hiroaki
Format: Article
Language:English
Subjects:
Activity patterns
Avalanches
Biology and Life Sciences
Brain
Brain - physiology
Coding
Computer and Information Sciences
Data processing
Dynamic range
Entropy
Entropy (Information theory)
Humans
Information processing
Medicine and Health Sciences
Memory
Models, Neurological
Nerve Net - physiology
Neural circuitry
Neural networks
Neurophysiology
Optimization
Phase transitions
Physical Sciences
Physiological aspects
Random variables
Social Sciences
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0184367