Nonlinear Parametric Granger Causality in Dynamical Networks
Determining how the brain is connected is a crucial point in neuroscience. Advances in imaging techniques guarantee an immediate improvement in our knowledge of structural connectivity. A constant computational and modeling effort has to be done in order to optimize and adapt functional and effective connectivity to the qualitative and quantitative changes in data and physiological applications. We are interested in the paths of information flow throughout the brain and what this can tell us about the functionality of a healthy and pathological brain. Every time we record brain activity, we can imagine that we are monitoring the activity at the nodes of a network. This
activity is dynamical and sometimes chaotic. Dynamical networks (Baraba´si, 2002) model physical and biological behavior in many applications.