Neurophysiological Mechanisms of Motor Skills Learning

描述（由申请人提供）：为了将手从一个点移动到另一个点，大脑依靠神经结构来控制手臂的运动，神经结构可以估计任务的物理动力学并将所需的运动转化为运动命令。如果手握着一个物体或与环境发生其他物理上的相互作用，这些结构会考虑到手臂动力学的变化，从而改变运动命令。这表明，在产生运动指令时，大脑依赖于预测手臂和外部世界的物理动态的内部模型。这些内部模型是自适应的，通过实践学习，似乎是自主运动控制的基本组成部分。然而，对于哪些神经结构参与了这些内部模型的形成，以及它们是如何学习表征这些模型的，我们知之甚少。这里的目的是结合行为，神经生理学和数学工具来推断内部模型是如何在大脑皮层中编码的，通过考虑到达运动的物理动力学被改变的任务。工作假设是，受试者学习和推广运动技能的能力取决于不同的运动学参数（例如，方向，位置）如何在皮质神经元的全局调节函数中编码，这反过来又决定了错误依赖的学习机制如何适应任务动态的内部模型。这一假设将通过将观察到的运动皮质神经元的调节功能与受试者在各种任务中的运动表现联系起来来部分验证。为了研究适应过程本身，具体项目将询问在给定动作中经历的错误如何影响后续动作。这些观察结果允许定义泛化函数，以数学方式描述大脑如何适应内部模型以响应错误。该函数的形状预测了参与表示内部模型的元素的运动运动学的调谐特性。我们将在手臂位置和速度的参数空间中研究泛化函数，并将这些推断的表征与运动皮层神经元的试对试反应可变性进行比较。进一步的项目将扩展学习，从将任务动力学与手臂运动学联系起来的程序层面，到将动力学与任意线索（如空间位置或视觉刺激的颜色）联系起来的认知层面。

DESCRIPTION (provided by applicant): To move the hand from one point to another, the brain controls the arm's movement by relying on neural structures that estimate physical dynamics of the task and transform the desired motion into motor commands. If the hand is holding an object or otherwise interacting physically with the environment, the resulting changes in arm dynamics are taken into account by these structures, resulting in altered motor commands. This suggests that in generating motor commands, the brain relies on internal models that predict the physical dynamics of the arm and the external world. These internal models are adaptive, learned with practice, and appear to be a fundamental part of voluntary motor control. However, very little is known about which neural structures are involved in formation of these internal models or how they learn to represent them. The aim here is to combine behavioral, neurophysiological and mathematical tools to infer how internal models are encoded in the cerebral cortex, by considering tasks in which the physical dynamics of reaching movements are altered. The working hypothesis is that the ability of subjects to learn and generalize motor skills is determined by how different kinematics parameters (e.g., direction, position) are encoded in the global tuning functions of cortical neurons, which in turn determines how error-dependent learning mechanisms adapt the internal models for task dynamics. This hypothesis will be tested in part by relating the observed tuning functions of motor cortical neurons to motor performance of subjects in a variety of tasks. To study the adaptation process itself, specific projects will ask how an error experienced in a given movement affects subsequent movements. These observations permit the definition of generalization functions that describe mathematically how the brain adapts the internal model in response to an error. The shape of this function predicts the tuning properties for movement kinematics of the elements that take part in representing the internal model. The generalization functions will be studied in the parameter spaces of arm position and velocity, and these inferred representations will be compared with the trial-to-trial response variability of motor cortex neurons. Further projects will extend learning from the procedural level of associating task dynamics with arm kinematics, to the cognitive level of linking dynamics to arbitrary cues such as the spatial location or color of visual stimuli.