Unveiling causal activity of complex networks

TL;DR

This paper presents a new analytical tool called causal webs (c-webs) that separates causally-related cascades from non-causal events in complex network dynamics, revealing hidden features and challenging previous assumptions about neuronal avalanches.

Contribution

The paper introduces causal webs (c-webs), a novel method to distinguish causally-related events from non-causal ones in complex networks, providing new insights into neuronal activity.

Findings

C-webs effectively separate causal cascades from non-causal events.

Neuronal avalanches are not solely composed of causally-related events.

The method uncovers previously hidden features of network dynamics.

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.