Expanding the Transfer Entropy to Identify Information Subgraphs in Complex Systems

Abstract

We propose a formal expansion of the transfer entropy to put in evidence irreducible sets of variables which provide information for the future state of each assigned target. Multiplets characterized by a large contribution to the expansion are associated to informational circuits present in the system, with an informational character which can be associated to the sign of the contribution. For the sake of computational complexity, we adopt the assumption of Gaussianity and use the corresponding exact formula for the conditional mutual information. We report the application of the proposed methodology on two EEG data sets.

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Cited by in corpus (17)

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• Quantifying High-order Interdependencies via Multivariate Extensions of the Mutual Information
• Information Flows? A Critique of Transfer Entropies
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• Bits from Biology for Computational Intelligence
• Synergy and redundancy in the Granger causal analysis of dynamical networks
• Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes
• Transfer entropy in continuous time, with applications to jump and neural spiking processes
• Quantifying information transfer and mediation along causal pathways in complex systems
• Causality in cognitive neuroscience: concepts, challenges, and distributional robustness
• Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI
• Generalised Measures of Multivariate Information Content
• Identifying delayed directional couplings with symbolic transfer entropy
• Exact partial information decompositions for Gaussian systems based on dependency constraints
• Assessing transfer entropy from biochemical data
• Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
• Causality and Generalizability: Identifiability and Learning Methods