The Study And Simulation Of Brain Dynamics Model Based On FPGA

Computational neuroscience is emerging, cross-cutting interdisciplinary in brain science. It associates with theoretical study and experimental neuroscience by using the concept of physics, math and engineering concepts, then studies the functional properties of the brain. With the rapid development of information technology, vast amounts of experimental data of brain science in theory provided the experimental basis for the further development. Based on theory and model of computational neuroscience and experimental neuroscience, brain dynamics plays a key role in understanding the brain mechanisms. Through theoretical study of different levels of the brain, according to the calculation model and experimental data, enabling in-depth analysis of the cognitive functions of the brain.This paper analyzes the functional activity of the brain in mesoscopic level, and enables in-depth analysis of the thalamus cortex neural mass model based on the classical neural mass model. then the whole brain neural mass model is constructed, to simulate brain function at the system level activity. Specific tasks are as follows:First of all, we analysis the classic neural mass model algorithms. Based on the modeling of a single neural mass model, we adjust the parameters of excitability, and observe the dynamics characteristic of EEG, and analysis the EEG mechanism in mesoscopic level. Secondly, we extended the thalamus cortex neural mass model based on neural mass model. On this basis, by changing the excitability parameters of thalamus cortex neural mass model, to model output signals of various EEG rhythm, and it lays the theory foundation for the analysis of brain functional activity. Finally, we build and implement the hardware simulation platform of brain dynamics on the FPGA. Combined with the DSP Builder tool building the neural algorithm in MATLAB/Simulink and completing the conversion algorithm model to hardware description language, this lays a foundation for hardware implementation of the brain dynamic model.