Thoroughman K A, Shadmehr R
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205, USA.
Nature. 2000 Oct 12;407(6805):742-7. doi: 10.1038/35037588.
Understanding how the brain constructs movements remains a fundamental challenge in neuroscience. The brain may control complex movements through flexible combination of motor primitives, where each primitive is an element of computation in the sensorimotor map that transforms desired limb trajectories into motor commands. Theoretical studies have shown that a system's ability to learn action depends on the shape of its primitives. Using a time-series analysis of error patterns, here we show that humans learn the dynamics of reaching movements through a flexible combination of primitives that have gaussian-like tuning functions encoding hand velocity. The wide tuning of the inferred primitives predicts limitations on the brain's ability to represent viscous dynamics. We find close agreement between the predicted limitations and the subjects' adaptation to new force fields. The mathematical properties of the derived primitives resemble the tuning curves of Purkinje cells in the cerebellum. The activity of these cells may encode primitives that underlie the learning of dynamics.
理解大脑如何构建运动仍然是神经科学中的一项基本挑战。大脑可能通过运动基元的灵活组合来控制复杂运动，其中每个基元都是感觉运动映射中计算的一个元素，该映射将期望的肢体轨迹转换为运动指令。理论研究表明，系统学习动作的能力取决于其基元的形状。通过对误差模式进行时间序列分析，我们在此表明，人类通过具有编码手部速度的类高斯调谐函数的基元的灵活组合来学习伸手动作的动力学。推断出的基元的广泛调谐预示了大脑表征粘性动力学能力的局限性。我们发现预测的局限性与受试者对新力场的适应性之间有密切的一致性。推导得出的基元的数学特性类似于小脑浦肯野细胞的调谐曲线。这些细胞的活动可能编码了构成动力学学习基础的基元。
