Identify Interactions in Complex Networked Dynamical Systems through Causation Entropy

Explore a comprehensive lecture on identifying interactions in complex networked dynamical systems using causation entropy. Delve into the challenges of inferring coupling structures from time series data across various scientific disciplines. Learn about the optimal causation entropy (oCSE) approach, an information-theoretic method for identifying information flow in actual coupling structures. Examine examples such as functional brain networks inferred from fMRI data, and compare oCSE networks with popular correlation-based brain inference techniques. Gain insights into the dynamical roles these networks play in healthy brain function. This 55-minute presentation by Erik Bollt from Clarkson University's Math/ECE department offers valuable knowledge for researchers and practitioners working with complex systems and network dynamics.

Erik Bollt - Identify Interactions in Complex Networked Dynamical Systems through Causation Entropy