A Low-Resource Digital Implementation of the Fitzhugh-Nagumo Neuron

Abstract

Simulation is a particularly significant component of discovery and hypothesis evaluation in neuroscience. Given the typical complexity of the mathematical models involved in neuromorphic modelling, neuromorphic hardware for acceleration is an interesting topic of research. Thus, a novel, high-accuracy digital implementation of the Fitzhugh-Nagumo neuron is realized on FPGA. The proposed system offers substantial hardware resource savings and a higher clock frequency compared to previously proposed implementations. For these reasons, it is an excellent candidate for use in hardware acceleration of neuroscientific simulation. The implemented hardware achieves a normalized RMSE of 0.2451 at a maximum operation frequency of 367.78MHz.