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.
Highlights
Maximum unpredictability is reported to occur around phase transitions where criticality is observed
This hypothesis finds important concrete realizations in biological systems, including the brain physiology [1]. It is still under discussion, a cornerstone from the theoretical perspective is that neuronal networks work in the vicinities of a critical regime, i.e., the activities observed in the neuronal networks, in vivo [2,3,4,5], in vitro [2,6,7,8,9,10] silico [1,11,12,13], are found to exhibit neuronal-avalanche-like behaviors whose size distribution can be approximated by a power law
In the neuronal network context, we can cite the maximization of both information processing [2,3,11] and dynamic range [1,11]
Summary
Maximum unpredictability is reported to occur around phase transitions where criticality is observed This hypothesis finds important concrete realizations in biological systems, including the brain physiology [1]. It is still under discussion, a cornerstone from the theoretical perspective is that neuronal networks work in the vicinities of a critical regime, i.e., the activities observed in the neuronal networks, in vivo [2,3,4,5], in vitro [2,6,7,8,9,10] silico [1,11,12,13], are found to exhibit neuronal-avalanche-like behaviors whose size distribution can be approximated by a power law.
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