CELLULAR NEURAL NETWORKS: SPACE-DEPENDENT TEMPLATE, MOSAIC PATTERNS AND SPATIAL CHAOS

Abstract

We consider a Cellular Neural Network (CNN) with a bias term in the integer lattice tenpoint ℤ2 on the plane tenpoint ℤ2. We impose a space-dependent coupling (template) appropriate for CNN in the hexagonal lattice on tenpoint ℤ2. Stable mosaic patterns of such CNN are completely characterized. The spatial entropy of a tenpoint (p1, p2)-translation invariant set is proved to be well-defined and exists. Using such a theorem, we are also able to address the complexities of resulting mosaic patterns.