Measuring information transfer

By Thomas Schreiber. 2000

4,319 citations recorded by the source corpus.

An information theoretic measure is derived that quantifies the statistical coherence between systems evolving in time. The standard time delayed mutual information fails to distinguish information that is actually exchanged from shared information due to common history and input signals. In our new approach, these influences are excluded by appropriate conditioning of transition probabilities. The resulting transfer entropy is able to distinguish effectively driving and responding elements and to detect asymmetry in the interaction of subsystems.

Subfields: Cognitive Neuroscience, Economics and Econometrics, Plant Science

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Citations in the corpus

Causal Models for Dynamical Systems (2020, neutral): “Diﬀerent causal methodology and several extensions of SCMs have been proposed [Wiener, 1956, Granger, 1969, Schreiber, 2000, White and Lu, 2010, Hyttinen et al., 2013, Peters et al., 2013, Pﬁster et al., 2018b], mostly considering discrete time models such as vector autoregressive models…”
Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect (2014, background): “For example, transfer entropy Tu?v [137] is a measure of the directional information flow from one brain area u to another area v.”
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity (2013, methodology): “…signals without providing any causal information (good examples are the traditional linear methods mentioned above), whereas for the assessment of EC one needs time series techniques that do provide causal information, such as Granger causality (Granger 1969) or transfer entropy (Schreiber 2000).”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, background): “is applicable [316] to both linear and nonlinear systems, but often the required data amount is prohibitively large.”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “TE is a non-parametric approach measuring the transfer of information between joint processes based on information theory (Schreiber, 2000).”
A method for detecting damage-induced nonlinearities in structures using information theory (2006, background): “More recently a different metric, the transfer entropy, has been introduced by Schreiber [19] and incorporates the dynamic nature of the processes directly.”
Widespread Worry and the Stock Market (2010, background): “Recently (Schreiber 2000), techniques have emerged to model nonlinear information flow between time series.”
Complex network approaches to nonlinear time series analysis (2019, methodology): “Variousmethods have been proposed to extract the statistical associations fromdata, for instance, Pearson correlation,mutual information (including its timedelayed version) [24,25], Granger causality [26,27], transfer entropy [28,29], or methods for detecting coupling directions from time series dat”
Robust informational entropy-based descriptors of flow in catchment hydrology (2016, background): “As such, our comments and findings also apply to related measures such as relative entropy, mutual information (Mogheir and Singh 2002, Mishra et al. 2009), transfer entropy (Schreiber 2000, Ruddell and Kumar 2009), etc.”
Predictability: Recent insights from information theory (2007, background): “The last term in braces is related to information transfer [Schreiber, 2000].”
A study of problems encountered in Granger causality analysis from a neuroscience perspective (2017, methodology): “Transfer entropy methods (31, 47, 48) can be viewed as a generalization of the Granger approach and are applicable to nonlinear systems.”
Gamma-band synchronization in the neocortex: novel analysis methods and their application to sensory and motivational systems (2013, methodology): “A causality metric has been developed based on Shannon entropy, called transfer entropy (Schreiber, 2000).”
A MATLAB toolbox for Granger causal connectivity analysis. (2010, positive): “, 2004), and adaptingmethods from information theory such as transfer entropy (Schreiber, 2000).”
Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework (2008, background): “Transfer entropy [8] can be written (for special case l = k = 1) as”
Nonlinear analysis of bivariate data with cross recurrence plots (2002, background): “be found with other nonlinear techniques which are designed for the study of interrelations as described in [19,20].”