Yang Shuangming, Wang Jiang, Li Shunan, Deng Bin, Wei Xile, Yu Haitao, Li Huiyan
School of Electrical Engineering and Automation, Tianjin University, 300072, PR China.
School of Electrical Engineering and Automation, Tianjin University, 300072, PR China.
Neural Netw. 2015 Nov;71:62-75. doi: 10.1016/j.neunet.2015.07.017. Epub 2015 Aug 10.
The basal ganglia (BG) comprise multiple subcortical nuclei, which are responsible for cognition and other functions. Developing a brain-machine interface (BMI) demands a suitable solution for the real-time implementation of a portable BG. In this study, we used a digital hardware implementation of a BG network containing 256 modified Izhikevich neurons and 2048 synapses to reliably reproduce the biological characteristics of BG on a single field programmable gate array (FPGA) core. We also highlighted the role of Parkinsonian analysis by considering neural dynamics in the design of the hardware-based architecture. Thus, we developed a multi-precision architecture based on a precise analysis using the FPGA-based platform with fixed-point arithmetic. The proposed embedding BG network can be applied to intelligent agents and neurorobotics, as well as in BMI projects with clinical applications. Although we only characterized the BG network with Izhikevich models, the proposed approach can also be extended to more complex neuron models and other types of functional networks.
基底神经节（BG）由多个皮质下核组成，负责认知和其他功能。开发脑机接口（BMI）需要一种适用于便携式BG实时实现的解决方案。在本研究中，我们使用了一个包含256个经过修改的Izhikevich神经元和2048个突触的BG网络的数字硬件实现，以在单个现场可编程门阵列（FPGA）内核上可靠地再现BG的生物学特性。我们还通过在基于硬件的架构设计中考虑神经动力学，突出了帕金森病分析的作用。因此，我们基于使用定点算法的基于FPGA的平台进行精确分析，开发了一种多精度架构。所提出的嵌入式BG网络可应用于智能代理和神经机器人技术，以及具有临床应用的BMI项目。虽然我们仅用Izhikevich模型对BG网络进行了表征，但所提出的方法也可扩展到更复杂的神经元模型和其他类型的功能网络。
