Neuron is the basic unit of the biological neural system. The Hodgkin-Huxley (HH) model is one of the most realistic neuron models on the electrophysiological characteristic description of neuron. Hardware implementation of neuron could provide new research ideas to clinical treatment of spinal cord injury, bionics and artificial intelligence. Based on the HH model neuron and the DSP Builder technology, in the present study, a single HH model neuron hardware implementation was completed in Field Programmable Gate Array (FPGA). The neuron implemented in FPGA was stimulated by different types of current, the action potential response characteristics were analyzed, and the correlation coefficient between numerical simulation result and hardware implementation result were calculated. The results showed that neuronal action potential response of FPGA was highly consistent with numerical simulation result. This work lays the foundation for hardware implementation of neural network.
Using the computer to imitate the neural oscillations of the brain is of great significance for the analysis of brain functions. Thalamocortical neural mass model (TNMM) reflects the mechanisms of neural activities by establishing the relationships between the thalamus and the cortex, which contributes to the understanding of some specific cognitive functions of the brain and the neural oscillations of electroencephalogram (EEG) rhythms. With the increasing complexity and scale of neural mass model, the performance of conventional computer system can not achieve rapid and large-scale model simulation. In order to solve this problem, we proposed a computing method based on Field Programmable Gate Array (FPGA) hardware in this study. The Altera's DSP Builder module combined with MATLAB/Simulink was used to achieve the construction of complex neural mass model algorithm, which is transplanted to the FPGA hardware platform. This method takes full advantage of the ability of parallel computing of FPGA to realize fast simulation of large-scale and complex neural mass models, which provides new solutions and ideas for computer implementation of neural mass models.