基于FPGA的基底核神经网络的实现

Implementation of base ganglia neural network based on FPGA

提出一种基于FPGA的基底核神经网络的实现方法。采用分段线性逼近法对原始的Izhikevich神经元数学模型进行处理,根据突触耦合原理运用DSP Builder和Simulink搭建基底核神经网络模型并进行软件仿真,运用Quarstus Ⅱ将搭建的基底核神经元网络模型下载到FPGA神经元仿真平台中并对其生物动力学放电特性进行分析。结果表明:采用分段线性逼近法完全能够实现Izhikevich神经元模型的放电特性,并且相对于原始方法节约了大量的逻辑资源;采用FPGA神经元仿真平台能够再现基底核神经网络的生物动力学特性,能够应用于大规模神经元网络的生物动力学特性研究。

This research put forward an implementation method of basal ganglia neural network based on FPGA. The mathematical model of the original Izhikevich neuron was processed by the piecewise linear approximation method. Based on the principle of synaptic coupling, a basal ganglia neural network was constructed by the DSP Builder and Simlink. The network was tested by software simulation and downloaded to the FPGA by the Quaster II to analyze the characteristics of the biodynamics discharge. The verification results show that the discharge characteristics of Izhikevieh neuron model can be fully realized by the pieeewise linear approximation method, and a large amount of logic resources are saved compared with that of the original method. The biological dynamic characteristics of the basal ganglia neural network can be reproduced by the FPGA simulation platform. It is feasible to use FPGA simulation platform to imple- ment the dynamic characteristics of a large-seale neural network.