Digital Multiplierless Realization of Coupled Wilson Neuron Model

Abstract

neurons with a switching speed of about 1 ms. Studying spiking neural networks, including the modeling, simulation, and implementation of the biological neuron models, helps us to learn about the brain and the related diseases, or to design more efficient bio-mimic processors and smarter robots. Such applications have made this part of neuromorphic research works very popular. In this paper, the Wilson neuron model has been implemented as an approximation of the Hodgkin-Huxley biological model that is adjusted for the efficient digital realization on the platforms. Results show that the proposed model can adequately reproduce neuron dynamical behaviors. The hardware implementation on the field-programmable gate array (FPGA) shows that our modifications on the Wilson original model imitate the biological behavior of neurons, besides using feasibility, targeting a low cost and high efficiency. The modifications raised a 15% speed-up compared with the original model. The mean normalized root-mean-square error, root-mean-square error, and the mean absolute error parameters are 6.43, 0.44, and 0.31, respectively.