Digital realization of neuron models, especially implementation on a field programmable gate array (FPGA), is one of the key objectives of neuromorphic research, because the effective hardware realization of the biological neural networks plays a crucial role in implementing the behaviors of the brain for future applications. In this paper, a hybrid FitzHugh Nagumo-Morris Lecar (FNML) neuron model with electromagnetic flux coupling is considered, and two multiplierless piecewise linear (PWL) models, which have similar behaviors to the biological neuron, are presented. A comparison between digital implementation results of the original FNML and PWL models illustrates that, the PWL1 model provides a 65% speed-up with an overall saving (in FPGA resources) of 66.2%, and the PWL2 model yields a 71% speed-up with an overall saving of 78.2%.
