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

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，上下文包括捕食者追逐猎物的动作，猎物在高处奔跑和躲避。 速度，就像猎豹追逐瞪羚。感觉运动控制系统（SCS）负责这种 动作及其性能显然取决于神经元的计算能力，大脑和 肌肉和肌肉的动力学。尽管这些明显的因素限制了 一个动物可以跟踪一个移动的物体，跟踪性能的SCS和它的依赖神经 计算、延迟和肌肉动力学还没有被明确地量化。在这个项目中，我们将建立 基于使用反馈控制原理和适当简化的模型开发的新理论， SCS用于识别神经计算、延迟和肌肉在快速运动过程中的相互作用。 因此，如果一个组件受损，我们可以利用其他组件来恢复 运动性能与辅助神经假体设备。 该计划的目标是首先参数化的主要因素（大脑和身体）限制快速运动 并且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进行实验测试， 操纵（i）神经计算能力，（ii）传输延迟，和（iii）肌肉动力学。如果 实验和理论之间出现差异，SCS模型和理论将被修改以解释 观测数据最后，实现快速运动跟踪所需的交互理论模型 将被利用来应用补偿，以通过“提升”其他参数来解释某些参数的退化。 更具体地说，我们将为神经功能受损的受试者设计辅助神经假体装置。 真实的地产表现为快速恢复运动。例如，如果初级运动皮层受损 由于疾病或损伤，我们可以通过增加必要的补偿来操纵肌肉动力学， 力量恢复运动性能，更重要的是恢复快速和敏捷的运动。只是如何一个 我们的SCS模型和理论将告诉我们应该补偿什么。

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.