Neural network simulator for spatiotemporal pattern analysis

This paper describes a fast simulator for spatiotemporal pattern analysis of multivalued continuous-time neural networks, where the multivalued transfer function of a neuron is regarded as a stepwise constant function. Use of stepwise constant method enables one to analyse the state transition of the network without solving explicitly the differential equations. Furthermore, this method also enables one to select the optimal timestep in numerical integration. We have constructed a neural network simulator for the spatiotemporal pattern analysis and compared it with conventional simulators. Finally, it is shown that our simulator is faster and more practical than conventional simulators