Multiplierless Implementation of Fitz-Hugh Nagumo (FHN) Modeling Using CORDIC Approach

The study, simulation, and implementation of neural behavior in the human brain are central goals of neuromorphic engineering. By integrating various scientific fields, we present a hardware solution based on neuronal cell mechanisms that can emulate such a nature-inspired system. This article presents a Fitz-Hugh Nagumo (FHN) neuron implemented using COordinate Rotation DIgital Computer (CORDIC), which accurately reproduces various patterns of the original FHN neuron model. We propose a modification to the original nonlinear term using a CORDIC IP-Core, resulting in high matching accuracy and low computational error. The proposed model is validated through time domain and dynamic analysis, which demonstrates its high accuracy and low error in reproducing all features of the FHN model. For large scale neuron implementations, we present an efficient digital hardware solution based on the resource sharing techniques. The hardware is implemented on Field-Programmable Gate Array (FPGA) using Hardware Description Language (HDL), as a proof of concept. The results from the hardware implementation show that the proposed model uses only 1% of the resources available on a Virtex 4 FPGA board. Additionally, the static timing analysis shows that the circuit can operate at a maximum frequency of 320 MHz.

Automated summary

This study presents a hardware solution for emulating neural behavior in the human brain using neuronal cell mechanisms. The proposed model shows high accuracy and low error in reproducing the features of the original neuron model.