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

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BMC Neuroscience

, 12:119

First Online: 18 November 2011Received: 18 May 2011Accepted: 18 November 2011


BackgroundTransfer 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.

ResultsIn 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.

ConclusionsTRENTOOL 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.

AbbreviationsACTautocorrelation decay time

AR10stable autoregressive processes with order 10

dembedding dimension



εmixing coefficient

ηGaussian white noise process

FDRFalse Discovery Rate

fMRIfunctional magnetic resonance imaging

LFPlocal field potential


MImutual information

τembedding delay

TETransfer entropy

TRENTOOLTRansfer ENtropy TOOLbox

uprediction time

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2202-12-119 contains supplementary material, which is available to authorized users.

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Author: Michael Lindner - Raul Vicente - Viola Priesemann - Michael Wibral


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