Ergodic sequential logic spiking neural network: reproductions of biologically plausible spatio-temporal phenomena and low-power implementation towards neural prosthesis

Abstract

A spiking neural network of ergodic sequential logic neuron models is presented. It is shown that the presented network is capable of reproducing various biologically plausible spatio-temporal phenomena (e.g., basic synchronization and complicated chimera phenomenon) observed in the brain. Moreover, it is revealed that the presented network is able to operate with lower power compared to a standard digital-processor-based spiking neural network. It is then discussed that the presented network will be a useful building block in a brain prosthetic device.