Information Theoretic Measures of Causal Influences during Transient Neural Events

TL;DR

This paper investigates how information-theoretic measures can quantify causal influences during transient neural events, introduces a new measure, and validates it through simulations and experimental data.

Contribution

It identifies limitations of existing measures and proposes a novel relative Dynamic Causal Strength, enhancing causal analysis during neural transients.

Findings

Limitations of Transfer Entropy and Dynamic Causal Strength identified

A new measure, relative Dynamic Causal Strength, proposed and validated

Results align with current understanding of brain circuit dynamics

Abstract

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.