ROBOT ARM CONTROL USING CORTICAL MULTINEURONAL RECORDING

使用皮质多神经元记录控制机器人手臂

• 批准号: 6194422
• 负责人: JOHN King CHAPIN
• 金额: $ 36.8万
• 依托单位: SUNY DOWNSTATE MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-09 至 2005-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6194422
• 关键词: Aotus; artificial limbs; biofeedback; brain electrical activity; denervation; laboratory rat; limb movement; medical implant science; motor cortex; nervous system prosthesis; neural information processing; paralysis; robotics

This proposal addresses the possibility of utilizing "motor" information extracted from simultaneous neuronal population recordings in the brain to remedy the loss of motor function associated with paralysis, limb amputation and other neurological conditions. This effort is also scientifically significant because it directly addresses the problem of neural population coding in the brain, and the possibility of controlling such coding through biofeedback. We have recently demonstrated in rats and monkeys the feasibility of using simultaneous neuronal population recordings in the motor cortex to control movement of a robot arm. The rats, in particular, were able to utilize their brain activity to accurately position (in one dimension) the robot arm under a water dropper, and then carry the water drop back to their mouths. Moreover, over continued training in this "neuro-robotic" mode, these animals were able progressively decorrelate this neural activity from the overt movements with which they were normally associated. This proposal has three specific aims: I. To utilize chronic neural ensemble recordings in monkeys to directly control multi-directional robot arm movement. The main issue is whether neuro-robotic feasibility be demonstrated for control of movements in multiple directions and under varying load conditions. II. To develop, implement and optimize new methods for transforming neuronal population activity into realtime neuro-robotic control signals. The main issue is whether simple linear neural population coding algorithms can be used to produce optimal neuro-robotic control functions, or whether nonlinear networks will be necessary. III. To investigate the feasibility of neuro-robotic control after sensorimotor denervation. The main question is whether neuro- robotic control is feasible after paralysis. This will be investigated here in rats subject to reversible denervation or amputation of the forelimb.

该提案提出了利用从大脑中同步神经元群记录中提取的“运动”信息来补救与瘫痪、截肢和其他神经系统疾病相关的运动功能丧失的可能性。 这项工作也具有科学意义，因为它直接解决了大脑中神经群体编码的问题，以及通过生物反馈控制此类编码的可能性。 我们最近在大鼠和猴子身上证明了使用运动皮层中同步神经元群记录来控制机器人手臂运动的可行性。 特别是，老鼠能够利用他们的大脑活动将机器人手臂准确地定位（在一维）水滴管下，然后将水滴带回他们的嘴里。 此外，通过这种“神经机器人”模式的持续训练，这些动物能够逐渐将这种神经活动与它们通常相关的明显运动去相关。 该提案有三个具体目标： I. 利用猴子的慢性神经集合记录来直接控制多向机器人手臂运动。 主要问题是神经机器人在控制多个方向和不同负载条件下的运动方面是否具有可行性。 二.开发、实施和优化将神经元群活动转化为实时神经机器人控制信号的新方法。 主要问题是是否可以使用简单的线性神经群体编码算法来产生最佳的神经机器人控制功能，或者是否需要非线性网络。 三．研究感觉运动去神经支配后神经机器人控制的可行性。 主要问题是神经机器人控制在瘫痪后是否可行。 这里将在接受可逆去神经支配或前肢截肢的大鼠中对此进行研究。