Optimizing information processing in neuronal networks beyond critical states

Author(s)

Ayres Ferraz, Mariana Sacrini ; Cardeal Melo-Silva, Hiago Lucas ; Kihara, Alexandre Hiroaki

• Document type: Journal article
• Source: PLoS One; v. 12, n. 9 SEP 18 2017.
• Web of Science Citations: 2

Abstract

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. (AU)

• FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
• Grantee: Elbert Einstein Nehrer Macau
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 14/16711-6 - MicroRNAs and cell coupling interplay in the development, adaptation and degeneration of the nervous system
• Grantee: Alexandre Hiroaki Kihara
• Support Opportunities: Regular Research Grants