An Optimized Digital Implementation of the Hodgkin–Huxley Neuron Model on FPGA: Enhancing Computational Efficiency for Biomedical and Neural Applications

This study introduces an optimized FPGA-based digital implementation of the Hodgkin-Huxley neuron model, aimed at reducing hardware complexity and energy consumption while maintaining biological accuracy. By minimizing nonlinear terms without removing equations, the approach achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.6\times $</tex-math> </inline-formula>faster processing and 60% lower energy use on a Zynq XC7Z010 FPGA compared to the standard model. Validation included software simulations, hardware synthesis, Lyapunov analysis, frequency response, and neural network modeling. The results demonstrate the method’s reliability and effectiveness for real-time neural simulations, with significant improvements over previous works, supporting applications in disease modeling, BCIs, and neuroprosthetics. This framework enables efficient large-scale use in computational neuroscience.