A real-time FPGA implementation for cerebellar Purkinje cell model

This paper presents a novel approach that utilizes Field-Programmable Gate Array (FPGA) to perform high-precision modeling and real-time simulation of Purkinje cells. Purkinje cells, as the most dominant inhibitory neurons in the cerebellum, play a crucial role in motor control, learning, and memory. However, due to their complex structure and dynamic characteristics, traditional simulation methods struggle to simultaneously meet the requirements of efficiency and accuracy. To address this challenge issue, (1) we designs and implements a Purkinje cell model based on the FPGA platform, which can operate at speeds close to those in vivo and exhibits good scalability. According to Purkinje cells ion channels and synaptic plasticity mechanisms, a systematic analysis of their electrophysiological characteristics and dynamic response behaviors is conducted. Furthermore, a Hodgkin-Huxley computational model is constructed and optimized, enabling real-time computation of cell firing patterns, signal integration, and synaptic weight changes on the FPGA platform. (2) Combining Hodgkin-Huxley-type equations, parameter fitting is performed for major currents such as sodium, potassium, and calcium ions, analyzing the impact of different channel conductance changes on firing patterns. The results show that the model can effectively exhibit new firing patterns of simple and complex spikes, not only accurately verifying the generation mechanisms of simple and complex spikes but also enhancing modeling speed and scalability. This work provides a computational foundation for understanding the information processing mechanisms of the cerebellum, offers a reference for analyzing pathological neural activities, and has positive implications for the development of brain-inspired computing chips.