Neurophysiological Mechanisms of Motor Skills Learning

描述(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，这些神经结构估计任务的物理动力学，并将所需的运动转换为运动命令。如果手握着对象或以其他方式与环境进行物理交互，这些结构会考虑手臂动力学的结果变化，从而导致运动命令的改变。这表明，在产生运动指令时，大脑依赖于内部模型，这些模型预测手臂和外部世界的物理动力学。这些内部模型是自适应的，通过实践学习，似乎是自愿运动控制的基本部分。然而，关于哪些神经结构参与了这些内部模型的形成，或者它们是如何学习表示它们的，人们知之甚少。这里的目的是结合行为学、神经生理学和数学工具，通过考虑伸展运动的物理动力学发生变化的任务，推断大脑皮质中内部模型是如何编码的。工作假说是，受试者学习和概括运动技能的能力取决于不同的运动学参数(如方向、位置)是如何在皮质神经元的全局调节函数中编码的，这反过来又决定了错误相关的学习机制如何适应任务动力学的内部模型。通过将观察到的运动皮质神经元的调谐功能与受试者在各种任务中的运动表现联系起来，这一假设将得到部分验证。为了研究适应过程本身，具体的项目将询问在给定动作中经历的错误如何影响随后的动作。这些观察结果允许定义泛化函数，这些泛化函数在数学上描述大脑如何适应内部模型以响应错误。此函数的形状预测参与表示内部模型的元素的运动运动学的调整特性。泛化函数将在手臂位置和速度的参数空间中进行研究，并将这些推断的表示与运动皮质神经元的尝试到尝试反应的变异性进行比较。进一步的项目将把学习从将任务动力学与手臂运动学联系起来的程序层面扩展到将动力学与任意线索(如视觉刺激的空间位置或颜色)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.