Causal and Structural Connectivity of Neuronal Networks

Douglas Zhou

Department of Mathematics and Institute of Natural Sciences

Shanghai Jiao Tong University




Current experimental techniques usually cannot probe the global interconnection pattern of a network. Thus, reconstructing or reverse-engineering the network topology of coupled nodes based upon observed data has become a very active research area. Most existing reconstruction methods are based on networks of oscillators with generally smooth dynamics. However, for nonlinear and non-smooth network dynamical systems, e.g., neuronal networks, the reconstruction of the full topology remains a challenge. Here, we present a noninterventional reconstruction method, which is based on Granger causality theory, for the widely used conductance-based, integrate-and-fire type neuronal networks. For this system, we have established a direct connection between Granger causal connectivity and structural connectivity.