Somatosensory inputs for arm control

用于手臂控制的体感输入

• 批准号: RGPIN-2022-04421
• 负责人: Pruszynski, Andrew
• 金额: $ 3.42万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=763353
• 关键词: Somatosensory; inputs; arm; control

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.

生物运动控制的理论通常从运动行为倾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。沿着这些路线的一个有影响力的计划是基于最优反馈控制。简单地说，当应用于生物运动控制时，该思想是基于身体的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运动命令（例如，达到目标同时惩罚能量消耗）。最终结果是，神经系统通过随时间调整反馈控制回路的增益并作为行为任务的函数来创建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的控制至关重要。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，这些反馈回路将来自肌肉和皮肤的感官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攻击这个重要的长期目标，包括理论工作，行为研究和人类和非人类灵长类动物的详细生理调查。本提案中提出的工作重点是建立最优反馈控制框架下的核心成本函数的性质，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（由成本函数定义）的错误才应该被纠正。不应该纠正最终不会影响任务成功的错误，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）详细描述在人体手臂姿势控制的背景下适用于体感反馈回路的成本函数和潜在的神经机制，特别是通过脊髓中的电路产生的最快反应，以及（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，因为确定上肢牵张反射反应是如何调整任务的需求和修改的经验是知之甚少，相对于相关的过程中的背景下，自愿运动控制。这可能是非常高的影响，因为我们最近的研究结果表明，在这方面，我们现在将是第一个详细阐述脊髓回路迄今未知的能力。