Causality analysis of LFPs in micro-electrode arrays based on mutual information

Since perceptual and motor processes in the brain are the result of in-teractions between neurons, layers and areas, a lot of attention has been directed towards the development of techniques to unveil these interactions both in terms of connectivity and direction of interaction. Several tech-niques are derived from the Granger causality principle, and are based on multivariate autoregressive modeling, so that they can only account for the linear aspect of these interactions. We propose here a technique based on conditional mutual information which enables us not only to describe the directions of nonlinear connections, but also their time delays. We com-pare our technique with others using ground truth data, thus, for which we know the connectivity. As an application, we consider local field poten-tials (LFPs) recorded with the 96 micro-electrode UTAH array implanted in area V4 of the macaque monkey’s visual cortex. 1 Causality analysis in neural systems Understanding the connections between different recording sites in the brain