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Model-based and model-free connectivity measure by means of Granger Causality and Transfer Entropy. Neural networks used too.
A challenge for physiologists and neuroscientists is to map information transfer between components of the systems that they study at different scales, in order to derive important knowledge on structure and function from the analysis of the recorded dynamics. We propose a freeware MATLAB toolbox, MuTE (Multivariate Transfer Entropy), that implements four both Granger causality and transfer entropy estimators according to uniform and non-uniform embedding frameworks. The resulting eight methods can be easily compared showing all the pros and cons of the methodologies used to detect the directed dynamical information transfers. The toolbox provides a completely brand-new approach that bridges machine learning and information theory fields. MuTE is also able to perform bivariate and fully multivariate analyses. Furthermore, users can easily implement their own methods or change some features of the already existing approaches due to the modularity of the toolbox.
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