Multiplierless Digital Implementation of Time-Varying FitzHugh–Nagumo Model

Low-cost accurate digital realization of the spiking neural networks is crucial to investigate the behaviors of human brain performance. This paper presents a multiplierless burst-mode Fitz-Hugh Nagumo (MBM-FHN) model, which is used for generating the burst-mode of the FHN neuron model. Using extra parameters (time-varying function) in neuron equations and estimating it by linear function, efficient low-cost and high-speed implementation is achieved. The simulation results show that the MBM-FHN model generates the similar bursting patterns of the original FHN neuron. The comparison shows that the MBM-FHN model has a better performance and best cost reduction related to the original neuron model especially in overall saving and speed-up parameters.