  Transient phenomena play a key role in coordinating brain activity at
multiple scales, however,their underlying mechanisms remain largely unknown. A
key challenge for neural data science is thus to characterize the network
interactions at play during these events. Using the formalism of Structural
Causal Models and their graphical representation, we investigate the
theoretical and empirical properties of Information Theory based causal
strength measures in the context of recurring spontaneous transient events.
After showing the limitations of Transfer Entropy and Dynamic Causal Strength
in such a setting, we introduce a novel measure, relative Dynamic Causal
Strength, and provide theoretical and empirical support for its benefits. These
methods are applied to simulated and experimentally recorded neural time
series, and provide results in agreement with our current understanding of the
underlying brain circuits.
