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

项目总结 跟踪快速不可预测的动作是一项宝贵的技能，适用于许多情况。在动物王国里， 上下文包括捕食者追逐其正在高速奔跑和躲避的猎物的动作，如 猎豹在追赶瞪羚。感应器运动控制系统(SCS)负责这些动作及其 性能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 所涉及的肌肉的动力学。尽管这些明显的因素限制了动物追踪的速度 运动目标、SCS的跟踪性能及其对神经计算、延迟和肌肉的依赖 动力学还没有被明确量化。在这个节目中，我们将建立在使用 反馈控制原理和适当简化的SCS模型，以识别神经计算如何， 延迟，肌肉在产生快速动作的过程中相互作用。因此，如果有一个组件 如果受到损害，我们可以利用其他组件通过辅助来恢复运动性能 神经假体装置。 该计划的目标是首先对限制快速运动的主要因素(大脑和身体)进行参数化，并 以推导出这些参数必须如何相互作用才能实现对SCS中快速运动的跟踪。那么， 将通过操作(I)在受试者身上进行实验测试参数化和量化的相互作用 神经计算能力，(Ii)传输延迟，和(Iii)肌肉动力学。如果在以下方面出现差异 实验和理论，SCS模型和理论将被修正以解释观测数据。最后， 将利用实现快速运动跟踪所需的交互作用的理论模型来应用 补偿，通过“提升”其他参数来解释某些参数的退化。更确切地说，我们将 为神经功能受损的受试者设计辅助神经假体装置 快速动作的表演。例如，如果初级运动皮质因疾病或损伤而受损， 我们可以通过添加必要的补偿力来控制肌肉动力学，以恢复运动 性能，更重要的是恢复快速和敏捷的动作。一个人应该如何补偿将是 由我们的SCS模型和理论提供信息。

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.