Information theoretic measures of causal influences during transient neural events

Frontiers in Network Physiology

Network Physiology

Michel Besserve¹  Nikos K. Logothetis²  Kaidi Shao³ 

[1] Department of Cognitive Neurophysiology, Max Planck Institute for Biological Cybernetics, Tübingen, Germany;Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany;International Center for Primate Brain Research (ICPBR), Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences (CAS), Shanghai, China;Department of Cognitive Neurophysiology, Max Planck Institute for Biological Cybernetics, Tübingen, Germany;Centre for Imaging Sciences, Biomedical Imaging Institute, The University of Manchester, Manchester, United Kingdom;International Center for Primate Brain Research (ICPBR), Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences (CAS), Shanghai, China;Department of Cognitive Neurophysiology, Max Planck Institute for Biological Cybernetics, Tübingen, Germany;Graduate School of Neural and Behavioral Sciences, International Max Planck Research School, Eberhard-Karls University of Tübingen, Tübingen, Germany;

关键词: information theory;    causal strength;    graphical models;    transfer entropy;    structural equations;    neural oscillations;   

DOI  :  10.3389/fnetp.2023.1085347

 received in 2022-10-31, accepted in 2023-05-11,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: 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.Methods: 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.Results: After showing the limitations of Transfer Entropy and Dynamic Causal Strength in this setting, we introduce a novel measure, relative Dynamic Causal Strength, and provide theoretical and empirical support for its benefits.Discussion: 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.

【 授权许可】

Unknown   
Copyright © 2023 Shao, Logothetis and Besserve.