Reaction-Diffusion systems can be applied to describe a broad class of nonlinear phenomena, in particular in biological systems and in the propagation of nonlinear waves in excitable media. Especially, pattern formation and chaotic behavior are observed in Reaction-Diffusion systems and can be analyzed. Due to their structure multi-layer Cellular Neural Networks (CNN) are capable of representing Reaction-Diffusion systems effectively. In this contribution Reaction-Diffusion CNN are considered for modeling dynamics of brain activity in epilepsy. Thereby the parameters of Reaction-Diffusion systems are determined in a supervised optimization process, and brain electrical activity using invasive multi-electrode EEG recordings is analyzed with the aim to detect of precursors of impending epileptic seizures. A detailed discussion of first results and potentiality of the proposed approach will be given.