In recent years, the implementation of spiking neural models to understand how spiking neural networks interact is focused in many neuroscience papers. The main purpose in this file is finding the solution to behave different neurological diseases. This implementation should be examined and analyzed in two perspectives: model presentation and hardware implementation. In this paper, Morris–Lecar model is selected based on including more biological parameters to match the spiking behaviors of neural system. Due to the nonlinear nature of the differential equations of Morris–Lecar model including hyperbolic functions and multiplicative calculations, linear approximation of the equations is proposed to achieve the low-cost and high-speed realization. The approach of linear fragmentation of Morris–Lecar model leads to an efficient Field-Programmable Gate Arrays (FPGA) implementation with higher frequency and simpler structure. In addition, hyperbolic function is modeled by the suggested 2X module and the multiplicative calculations are done based on simple arithmetic operations and logical shift which causes more improvements in the final realization.
