FPGA-based hardware simulation of nonlinear autoregressive Volterra model to reconstruct the single neuron spike pattern

Abstract

This study explores the implementation of the nonlinear autoregressive Volterra (NARV) model using a field programmable gate arrays (FPGAs)-based hardware simulation platform and accomplishes the identification process of the Hodgkin–Huxley (HH) model. First, a physiological detailed single-compartment HH model is applied to generate experiment data sets and the electrical behavior of neurons are described by the membrane potential. Then, based on the injected input current and the output membrane potential, a second-order NARV model is constructed and implemented on FPGA-based simulation platforms. The NARV modeling method is data-driven, requiring no accurate physiological information and the FPGA-based hardware simulation can provide a real time and high-performance platform to deal with the drawbacks of software simulation. Therefore, the proposed method in this paper is capable of handling the nonlinearities and uncertainties in nonlinear neural systems and may help promote the development of clinical treatment devices.