Multiplierless Implementation of Noisy Izhikevich Neuron With Low-Cost Digital Design.

Abstract

Fast speed and a high accuracy implementation of biological plausible neural networks are vital key objectives to achieve new solutions to model, simulate and cure the brain diseases. Efficient hardware implementation of spiking neural networks is a significant approach in biological neural networks. This paper presents a multiplierless noisy Izhikevich neuron (MNIN) model, which is used for the digital implementation of biological neural networks in large scale. Simulation results show that the MNIN model reproduces the same operations of the original noisy Izhikevich neuron. The proposed model has a low-cost hardware implementation property compared with the original neuron model. The field-programmable gate array realization results demonstrated that the MNIN model follows the different spiking patterns appropriately.