A Parameter Fitting Method for Digital Spiking Silicon Neuron Model

Abstract

Silicon neuronal networks have been gathering increasing attention as a promising platform for the next generation of artificial intelligence, because they can realize neuronal information processing with low power consumption, on account of their parallel and distributed structures. We have studied about the Digital Spiking Silicon Neuron model that is designed to reproduce a wide variety of neuronal activities with less necessary circuit resources. In this paper, we briefly introduce the DSSN model, especially about its parameter tuning method.