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TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy

Michael Lindner12, Raul Vicente347, Viola Priesemann56 and Michael Wibral7*

Author Affiliations

1 Center for Economics and Neuroscience, University Bonn, Bonn, Germany

2 Center for Individual Development and Adaptive Education of Children at Risk (IDeA), Frankfurt, Germany

3 Dept. Neurophysiology, Max Planck Institute for Brain Research, Frankfurt, Germany

4 Frankfurt Institute for Advanced Studies (FIAS), Frankfurt, Germany

5 Dept. Neural Systems and Coding, Max Planck Institute for Brain Research, Frankfurt, Germany

6 Group of Neural Theory, Ecole Normale Superieure, Paris, France

7 MEG Unit, Brain Imaging Center, Goethe University, Frankfurt, Germany

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BMC Neuroscience 2011, 12:119  doi:10.1186/1471-2202-12-119

Published: 18 November 2011

Abstract

Background

Transfer entropy (TE) is a measure for the detection of directed interactions. Transfer entropy is an information theoretic implementation of Wiener's principle of observational causality. It offers an approach to the detection of neuronal interactions that is free of an explicit model of the interactions. Hence, it offers the power to analyze linear and nonlinear interactions alike. This allows for example the comprehensive analysis of directed interactions in neural networks at various levels of description. Here we present the open-source MATLAB toolbox TRENTOOL that allows the user to handle the considerable complexity of this measure and to validate the obtained results using non-parametrical statistical testing. We demonstrate the use of the toolbox and the performance of the algorithm on simulated data with nonlinear (quadratic) coupling and on local field potentials (LFP) recorded from the retina and the optic tectum of the turtle (Pseudemys scripta elegans) where a neuronal one-way connection is likely present.

Results

In simulated data TE detected information flow in the simulated direction reliably with false positives not exceeding the rates expected under the null hypothesis. In the LFP data we found directed interactions from the retina to the tectum, despite the complicated signal transformations between these stages. No false positive interactions in the reverse directions were detected.

Conclusions

TRENTOOL is an implementation of transfer entropy and mutual information analysis that aims to support the user in the application of this information theoretic measure. TRENTOOL is implemented as a MATLAB toolbox and available under an open source license (GPL v3). For the use with neural data TRENTOOL seamlessly integrates with the popular FieldTrip toolbox.