On directed information theory and Granger causality graphs

2010/02/07 by Pierre-Olivier Amblard, P. O. Amblard, O. J. J. Michel +1 · 3 citations
Computer Science · Engineering · Mathematics · Neuroscience · Physics and Astronomy · #Neural dynamics and brain function #Statistical Mechanics and Entropy #Wireless Communication Security Techniques #cs.IT #math.IT

paper · pdf · doi:10.1007/s10827-010-0231-x

published as J. Comput. Neurosci. (2010), 30:7-16 · accepted for publications, Journal of Computational Neuroscience

arxiv created 2010/02/07 · openalex publication_date 2010/03/23 · arxiv updated 2011/11/02 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.

Citations

• Estimation of Entropy and Mutual Information

Cited by

• LQG Control With Minimum Directed Information: Semidefinite Programming Approach
• Directed Information on Abstract Spaces: Properties and Variational Equalities
• Identifying Seizure Onset Zone From the Causal Connectivity Inferred Using Directed Information

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• Relating Granger causality to directed information theory for networks of stochastic processes
• Causal conditioning and instantaneous coupling in causality graphs
• Directed Information Graphs
• Directed information and Pearl's causal calculus
• Quantifying causal influences