CRCNS: MOVE!-MOdeling of fast Movement for Enhancement via neuroprosthetics

项目摘要 跟踪快速的不可预测的运动是一项宝贵的技能，适用于许多情况。在动物王国里， 上下文包括捕食者追逐猎物的动作，猎物正在高速奔跑和躲避，就像 猎豹追逐瞪羚感觉运动控制系统（SCS）负责这些动作， 表现显然取决于神经元的计算能力，大脑和肌肉之间的延迟，以及 涉及到肌肉的动力学。尽管这些明显的因素限制了动物的追踪速度 一个移动的物体，SCS的跟踪性能及其对神经计算，延迟和肌肉的依赖 动态尚未明确量化。在这个程序中，我们将建立在新的理论开发使用 反馈控制原理和适当简化的SCS模型，以确定神经计算， 延迟和肌肉在快速运动的产生过程中相互作用。如果一个组件是 妥协，我们可以利用其他组件，以恢复运动性能与辅助 神经修复装置。 该计划的目标是首先参数化限制快速运动的主要因素（大脑和身体）， 导出这些参数必须如何相互作用以实现对SCS中的快速运动的跟踪。然后 参数化和量化的相互作用将在受试者中通过操作（i） 神经计算能力，（ii）传输延迟，和（iii）肌肉动力学。如果出现差异， 实验和理论，SCS模型和理论将被修改，以解释观测数据。最后 将利用实现快速运动跟踪所需的相互作用的理论模型来应用 补偿以通过“提升”其他参数来解决一些参数的退化。具体来说，我们将 为神经真实的状态受损的受试者设计辅助神经修复装置， 快速动作的表现。例如，如果初级运动皮层由于疾病或损伤而受损， 我们可以通过增加必要的补偿力量来控制肌肉动力， 性能，更重要的是恢复快速和敏捷的运动。如何补偿将是 我们的SCS模型和理论。

PROJECT SUMMARY Tracking fast unpredictable movements is a valuable skill, applicable in many situations. In the animal kingdom, the context includes the action of a predator chasing its prey that is running and dodging at high speeds, like a cheetah chasing a gazelle. The sensorimotor control system (SCS) is responsible for such actions and its performance clearly depends on the computing power of neurons, delays between brain and muscles, and the dynamics of muscles involved. Despite these obvious factors that set the limits on how fast an animal can track a moving object, tracking performance of the SCS and its dependence on neural computing, delays, and muscle dynamics have not been explicitly quantified. In this program, we will build upon new theory developed using feedback control principles and an appropriately simplified model of the SCS to identify how neural computing, delays, and muscles interact during the generation of fast movements. Therefore if one component is compromised, we can take advantage of the other components to restore motor performance with assistive neuroprosthetic devices. The program objectives are to first parameterize the major factors (brain and body) limiting fast movements and to derive how these parameters must interact to achieve tracking of fast movements in the SCS. Then, the parameterization and quantified interactions will be tested experimentally in subjects through manipulation of (i) neural computing power, (ii) transmission delays, and (iii) muscle dynamics. If discrepancies emerge between experiments and theory, the SCS model and theory will be modified to explain observation data. Finally, the theoretical model of interactions required to achieve tracking of fast movements will be exploited to apply compensation to account for degradation of some parameters by “boosting” others. More specifically, we will design assistive neuroprosthetic devices for subjects having compromised neural real estate to restore performance of fast movements. For example, if primary motor cortex is compromised due to disease or damage, we can manipulate muscle dynamics by adding the necessary compensatory forces to restore motor performance, and more importantly restore fast and agile movements. Just how one should compensate will be informed by our SCS model and theory.