Adding any spontaneous activation to a spreading process on networks shifts the critical behavior from directed to undirected percolation, with a derived critical line and a crossover scale of p^{-2/3}.
Unveiling causal activity of complex networks
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abstract
We introduce a novel tool for analyzing complex network dynamics, allowing for cascades of causally-related events, which we call causal webs (c-webs), to be separated from other non-causally-related events. This tool shows that traditionally-conceived avalanches may contain mixtures of spatially-distinct but temporally-overlapping cascades of events, and dynamical disorder or noise. In contrast, c-webs separate these components, unveiling previously hidden features of the network and dynamics. We apply our method to mouse cortical data with resulting statistics which demonstrate for the first time that neuronal avalanches are not merely composed of causally-related events.
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cond-mat.stat-mech 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Criticality in spreading processes without time-scale separation and the critical brain hypothesis
Adding any spontaneous activation to a spreading process on networks shifts the critical behavior from directed to undirected percolation, with a derived critical line and a crossover scale of p^{-2/3}.