Somatosensory inputs for arm control

生物运动控制理论通常从运动行为趋向于最佳性能的观点出发，一般来说，这种优化标准在再现各种各样的发现方面非常成功。其中一个有影响力的方案是基于最优反馈控制。简而言之，应用于生物运动控制的思想是，运动命令是基于身体状态和描述正在执行的行为的性能标准的成本函数来计算的（例如，在惩罚能量消耗的同时达到目标）。最终的结果是，神经系统通过随着时间的推移调整反馈控制回路的增益，并作为行为任务的功能，创造出目标导向的运动行为。因此，在这个框架下，理解神经系统如何处理感觉反馈对于理解运动控制至关重要。我们的长期目标是确定最快反馈回路（即通常称为拉伸反射）的功能能力和神经组织，该回路将来自肌肉和皮肤的感觉输入与现实世界的行为（如伸手，抓握和物体操作）联系起来。我的实验室从各个方面着手实现这一重要的长期目标，包括理论工作、行为研究以及对人类和非人类灵长类动物的详细生理调查。本提案提出的工作重点是建立最优反馈控制框架下成本函数中心的性质，特别是最小干预原则，这是最优反馈控制的基本预测。最小干预原则指出，只有最终影响任务成功的错误（由成本函数定义）才应该被纠正。不影响任务成功的错误不应该被纠正，因为在存在噪声的情况下这样做实际上可能会引入影响任务成功的错误。因此，我们的短期目标是：(1)详细说明在人体手臂姿势控制的背景下，应用于体感反馈回路的成本函数和潜在的神经机制，特别是那些通过脊髓回路产生的最快反应；(2)确定当暴露于新的成本函数时，体感反馈回路如何随着经验和学习而变化。这项工作是重要的，因为确定上肢拉伸反射反应是如何根据任务需求进行调整并随经验进行修改的，相对于自主运动控制的相关过程，我们知之甚少。这可能会产生非常大的影响，因为我们最近的发现表明，在这方面，脊髓回路具有迄今为止未知的能力，我们现在将是第一个详细阐述的人。

Theories of biological motor control often start from the view that motor behaviour tends towards optimal performance and, in general, such optimization criteria are extremely successful at reproducing a wide variety of findings. One influential scheme along these lines is based on optimal feedback control. Briefly, as applied to biological motor control the idea is that motor commands are computed based on the state of the body and a cost-function that describes the performance criteria of the behaviour being executed (e.g. reach the target while penalizing energy expenditure). The net result is that the nervous system creates goal-directed motor behaviour by adjusting the gains of feedback control loops over time and as a function of the behavioural task. Thus, under this framework, understanding how the nervous system processes sensory feedback is critical to understanding the control of movement. Our long-term objective is determining the functional capacity and neural organization of the fastest feedback loops (i.e. usually called stretch reflexes) that link sensory inputs from the muscles and skin to real-world behaviours like reaching, grasping, and object manipulation. My lab attacks this important long-term objective from all fronts including theoretical work, behavioural studies and detailed physiological investigations in humans and non-human primates. The work proposed in this proposal focuses on establishing the nature of the cost functions central under the optimal feedback control framework and specifically the minimum intervention principle, which is a fundamental prediction of optimal feedback control. The minimum intervention principle states that only errors that ultimately influence task success (as defined by the cost function) should be corrected. Errors that don't ultimately influence task success should not be corrected because doing so in the presence of noise may actually introduce errors that do influence task success. Thus, our short-term objectives, are to (1) detail the cost functions and underlying neural mechanisms that apply to somatosensory feedback loops in the context of postural control of the human arm, especially those fastest responses generated via circuits in the spinal cord, and (2) determine how the somatosensory feedback loops change with experience and learning when exposed to new cost functions. This work is important because determining how upper-limb stretch reflex responses are adjusted to task-demands and modified with experience is poorly understood relative to related processes in the context of voluntary motor control. It is likely to be of very high impact because our most recent findings suggest a hitherto unknown capacity of spinal circuits in this regard that we will now be the first to elaborate in detail.