A Digital Neuromorphic Realization of the 2-D Wilson Neuron Model

Abstract

This paper presents a piecewise linear approximation of the nonlinear Wilson (NW) neuron model for the realization of an efficient digital circuit implementation. The accuracy of the proposed piecewise Wilson (PW) model is examined by calculating time domain signal shaping errors. Furthermore, bifurcation analyses demonstrate that the approximation follows the same bifurcation pattern as the nonlinear Wilson (NW) model. As a proof of concept, both models are hardware synthesized and implemented on FPGAs, demonstrating that the PW model has a range of neuronal behaviors similar to the NW model with considerably higher computational performance and a lower hardware overhead. This approach can be used in hardware-based large scale biological neural network simulations and behavioral studies. The mean normalized root mean square error (NRMSE) and maximum absolute error (MAE) of the PW model are 6.32% and 0.31%, respectively, as compared to the NW model.