A geometric approach to properties of the discrete-time cellular neural network

Using the available theory on linear threshold logic, the Discrete-Time Cellular Neural Network (DTCNN) is studied from a geometrical point of view, Different modes of operation are specified. A bound on the number of possible mappings is given for the case of binary inputs. The mapping process in a cell of the network is interpreted in the input space and the parameter space. Worst-case and average-case accuracy conditions are given, and a sufficient worst-case bound on the number of bits required to store the network parameters for the case of binary input signals is derived. Methods for optimizing the robustness of DTCNN parameters for certain regions of the parameter space are discussed