Srinivasan Lakshminarayan, Eden Uri T, Willsky Alan S, Brown Emery N
Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital, Charlestown, MA 02129, USA.
Neural Comput. 2006 Oct;18(10):2465-94. doi: 10.1162/neco.2006.18.10.2465.
The execution of reaching movements involves the coordinated activity of multiple brain regions that relate variously to the desired target and a path of arm states to achieve that target. These arm states may represent positions, velocities, torques, or other quantities. Estimation has been previously applied to neural activity in reconstructing the target separately from the path. However, the target and path are not independent. Because arm movements are limited by finite muscle contractility, knowledge of the target constrains the path of states that leads to the target. In this letter, we derive and illustrate a state equation to capture this basic dependency between target and path. The solution is described for discrete-time linear systems and gaussian increments with known target arrival time. The resulting analysis enables the use of estimation to study how brain regions that relate variously to target and path together specify a trajectory. The corresponding reconstruction procedure may also be useful in brain-driven prosthetic devices to generate control signals for goal-directed movements.
伸手动作的执行涉及多个脑区的协同活动，这些脑区与期望的目标以及实现该目标的手臂状态路径存在不同关联。这些手臂状态可能代表位置、速度、扭矩或其他量。此前，估计已应用于神经活动，以将目标与路径分开重建。然而，目标和路径并非相互独立。由于手臂运动受到有限肌肉收缩力的限制，目标的信息会限制通向目标的状态路径。在这封信中，我们推导并说明了一个状态方程，以捕捉目标与路径之间的这种基本依赖关系。针对具有已知目标到达时间的离散时间线性系统和高斯增量，给出了解决方案。由此产生的分析使得能够利用估计来研究与目标和路径存在不同关联的脑区如何共同确定一条轨迹。相应的重建过程在脑驱动的假肢装置中生成用于目标导向运动的控制信号时可能也很有用。
