摘要
小脑对运动的调控和对环境的适应性是人体完成快速精准运动的关键,模拟并研究小脑的运行机制将为控制复杂多变的机器人模型提供更有效的方法.鉴于此,遵循神经元数量的真实生物比率,构建大规模小脑脉冲神经网络模型,模拟大脑中小脑的真实结构、信息传递方式和学习机制,实现对机械臂的误差纠正控制,同时依据系统在不同控制任务下的控制结果,得到不同突触可塑性对小脑网络控制效果的影响规律.为了进一步增加小脑控制系统的生物真实性,以更贴近人脑的并行运算方式在现场可编程门阵列(field programmable gate array, FPGA)平台上实现所构建的模型,并进行相应的资源优化,增加可实现的网络规模. FPGA实现结果显示,系统能够成功完成基于小脑误差纠正功能的自适应类脑机械臂控制,可以验证小脑的真实细胞动力学和大规模颗粒层提供的高容错性,并提供兼顾小脑应用功能实现和理论研究的平台.
The movement control of the cerebellum and its adaptability to the environment are the keys to complete rapid and precise movement for humans. Simulating and studying the operating mechanism of the cerebellum will provide a better way to control complex and changeable robot models. Therefore, by following the real biological ratio of different types of cerebellar neurons, a large-scale spiking neural network model of the cerebellum is built and the realistic structure,information transmission method, and learning mechanism of the cerebellum are simulated. We also complete the error correction control of a simulated robotic arm, and clarify the influence of different synaptic plasticities on the control effect of the cerebellar network with the control results of the system under different control tasks. In order to further increase the biological authenticity of the cerebellum control system, the model is implemented on a field programmable gate array(FPGA) platform in a parallel operation approach closer to the human brain, and corresponding resource optimization methods are proposed so that the achievable network scale is increased. The FPGA implementation results show that the system successfully simulates the adaptive brain-inspired robotic arm control based on the cerebellar error correction ability. The cell dynamics of the cerebellum can also be reproduced on the system and the high fault tolerance from large-scale granule cells is proven. This work provides a platform that takes into account both the realization of cerebellar application functions and the theoretical research.
作者
郝新宇
王江
邓斌
于海涛
伊国胜
HAO Xin-yu;WANG Jiang;DENG Bin;YU Hai-tao;YI Guo-sheng(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)
出处
《控制与决策》
EI
CSCD
北大核心
2023年第3期631-644,共14页
Control and Decision
基金
国家自然科学基金项目(62071324)。
关键词
小脑
脉冲神经元网络
现场可编程门阵列
突触可塑性
类脑控制
cerebellum
spiking neural network
field programmable gate array
synaptic plasticity
brain-like control
