An efficient algorithm for spatiotemporal pattern analysis of multivalued neural networks

Describes an efficient simulation algorithm for the spatiotemporal pattern analysis of multivalued continuous-time neural networks. The multivalued transfer function of the neuron is approximated to the stepwise constant function which is constructed by the sum of the step functions with the different thresholds. By this approximation, the dynamics of the network can be formulated as a stepwise constant linear ordinary differential equation at each timestep and the optimal timestep for the numerical integration can be obtained analytically. Finally, it is shown that the proposed method is much faster than a variety of conventional simulators.