EFRI-M3C: Distributed Brain Dynamics in Human Motor Control

智力优势：该项目旨在将认知和计算神经科学、神经工程和系统识别结合起来，对人类分布式脑动力学与运动活动相互作用的方式进行革命性的理解。在运动控制和适应过程中，将同时记录健康和帕金森患者的三维身体和四肢运动运动学、眼运动和脑电图（EEG）时空数据。S病患者。特别是，改变和现实世界的运动任务将在三维沉浸式虚拟现实技术中模拟，力反馈机器人提供本体感受交互和反馈。认知、行为和运动学数据将限制基于已知基底神经节解剖学和生理学的运动控制和适应的大规模计算模型的设计。神经形态工程将指导移动嵌入式计算系统的设计，用于帕金森的脑-体模型实时仿真和闭环感觉-运动控制。年代的病人。我们预计，用于神经康复的新机器的开发将导致工程学和神经科学之间的三重协同作用：人机交互将改变运动控制的概念，并为研究有益于神经科学的具身认知提供新的背景；反过来，神经科学和运动控制方面的新知识将加速用于康复和/或增强的自适应机器的发展。最后，在神经形态硬件中实现的运动控制的综合和预测数学模型有望导致新的智能神经假肢工具。更广泛的影响：这项研究的结果将有助于人类在现实世界环境中对人机交互、运动学习和控制的系统级理解，并将导致新一代无线大脑和身体活动传感器以及自适应假肢设备的发展。这将提高我们对人机交互的认识，刺激新的脑/身体传感器和执行器的工程，并在人与机器耦合的不同领域产生直接影响，如脑机接口，假肢和远程操作。我们预计，认知和计算神经科学、控制理论和可穿戴、不显眼的生物仪器的融合将为帕金森的治疗和神经康复提供新的非侵入性方法。并可能改变我们对脑/身体相互作用的理解。该项目吸引了来自加州大学圣地亚哥分校NSF时间动力学学习中心（TDLC）和神经计算研究所（INC）的研究生和本科生参与跨学科工程和神经科学方面的大型神经形态系统及其人机界面的设计、优化和训练。通过TDLC和NSF本科生研究经验（REU）支持的校园外展渠道，该计划将积极寻求下一代科学家和工程师更多地参与研究和教育。

Intellectual Merit: This project aims at combining cognitive and computational neuroscience, neuroengineering and system identification towards a transformative understanding of the way distributed brain dynamics interact with motor activity in humans. 3-D body and limbs movement kinematics, eye movements and electroencephalographic (EEG) spatiotemporal brain data will be recorded simultaneously during motor control and adaptation in healthy and Parkinson?s disease patients. In particular, altered and real world motor tasks will be simulated in 3-D immersive virtual reality technology with force feedback robots providing proprioceptive interaction and feedback. Cognitive, behavioral and kinematics data will constrain the design of large-scale computational models of motor control and adaptation based on known anatomy and physiology of the basal ganglia. Neuromorphic engineering will guide the design of mobile embedded computational systems for real-time emulation of the brain-body models and closed-loop sensory-motor control for Parkinson?s patients. We expect that the development of new machines for neuro-rehabilitation will result in a threefold synergetic interaction between engineering and neuroscience: human-machine interactions will transform the notion of movement control and provide new contexts to study embodied cognition that will benefit neuroscience; in turn, new knowledge in neuroscience and motor control will accelerate the development of adaptive machines for rehabilitation and/or enhancement. Finally, comprehensive and predictive mathematical models of motor control implemented in neuromorphic hardware are expected to lead to new intelligent neuroprosthetic tools.Broader Impact: Outcomes of this research will contribute to the system-level understanding of humanmachine interactions and motor learning and control in real world environments for humans, and will lead to the development of a new generation of wireless brain and body activity sensors and adaptive prosthetics devices. This will advance our knowledge of human-machine interactions, stimulate the engineering of new brain/body sensors and actuators, and have a direct influence in diverse areas where humans are coupled with machines, such as brain-machine interfaces, prosthetics and telemanipulation. We anticipate that the confluence of cognitive and computational neuroscience, control theory and wearable, unobtrusive bioinstrumentation will provide novel non-invasive approaches or the treatment and neuro-rehabilitation of Parkinson?s disease and will potentially transform our understanding of brain/body interactions. The project draws graduate and undergraduate students across divisions and in the NSF Temporal Dynamics of Learning Center (TDLC) and Institute of Neural Computation (INC) at UCSD participating in interdisciplinary engineering and neuroscience aspects of the design, optimization, and training of largescale neuromorphic systems and their human interfaces. Through outreach channels on campus supported by the TDLC and the NSF Research Experience for Undergraduates (REU), the program will actively pursue increased participation in research and education of the next generation of scientists and engineers.