APPROACHES TO EFFICIENT SIMULATION WITH SPIKING NEURAL NETWORKS

The distinct computational properties of spiking neural networks are increasingly the focus of research in computational neuroscience. When modelling these networks efficiency issues are critical. In this paper we present several algorithms for the event-driven simulation of spiking neural networks on single processor systems, which facilitate the simulation of large, highly active networks.