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Abstract

Information theory is becoming an increasingly popular framework for the study of sensory coding. Despite its widespread use for the analysis of single-cell spike trains, it has found relatively limited application to the analysis of other types of neurophysiological signals: This is due to the difficulty in estimating mutual information or entropies and to the lack of fast routines for the application of these techniques to multi-dimensional responses. Here we introduce our toolbox which allows to compute mutual information and other information theoretic quantities for the estimation of the information transmitted by different mechanisms of correlational coding. We also demonstrate the utility of our toolbox by applying it to the computation of the information conveyed by the power and phase of simulated and real Local Field Potentials and Electroencephalography.