Reconciliation of theoretical and empirical brain criticality via network heterogeneity
Gu, L.; Ruqian, W.
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Inspired by heterogeneity in biological neural networks, we explore a heterogeneous network consisting of receipt, transmission and computation layers. It reconciles the dilemma that the data analysis scheme for empirical records yields non-power laws when applied to microscopic simulation of critical neural dynamics. Detailed analysis shows that the reconciliation is due to synchronization effect of the feedforward connectivity. The network favours avalanches with denser activity in the first half of life, and the result is consistent with the experimental observation. This heterogeneous structure facilitates robust criticality against external stimuli, which implies the inappropriateness of interpreting the subcritcality signature as an indication of subcrtical dynamics. These results propose the network heterogeneity as an essential piece for understanding the brain criticality.
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