Unveiling causal activity of complex networks

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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Cited by in corpus (10)

• Colloquium: Criticality and dynamical scaling in living systems
• Evidence for quasicritical brain dynamics
• Hysteresis, neural avalanches and critical behaviour near a first-order transition of a spiking neural network
• Hopf Bifurcation in Mean Field Explains Critical Avalanches in Excitation-Inhibition Balanced Neuronal Networks: A Mechanism for Multiscale Variability
• Criticality in spreading processes without time-scale separation and the critical brain hypothesis
• Tackling the subsampling problem to infer collective properties from limited data
• Critical neuronal models with relaxed timescales separation
• Are Triggering Rates of Labquakes Universal? Inferring Triggering Rates From Incomplete Information
• Discovering the mesoscale for chains of conflict
• Scaling of causal neural avalanches in a neutral model