  We advance our approach of analyzing the dynamics of interacting complex
systems with the nonlinear dynamics of interacting nonlinear elements. We
replace the widely used lattice-like connection topology of cellular neural
networks (CNN) by complex topologies that include both short- and long-ranged
connections. With an exemplary time-resolved analysis of asymmetric nonlinear
interdependences between the seizure generating area and its immediate
surrounding we provide first evidence for complex CNN connection topologies to
allow for a faster network optimization together with an improved approximation
accuracy of directed interactions.
