Variation as a neural code

视觉引导伸手是人类和非人类灵长类动物的一种重要行为。 伸手是一种高度刻板的行为，具有相似的运动路径和速度曲线 在广泛的条件下观察，跨学科，甚至跨物种。然而 视觉引导到达的另一个标志是在运动细节上的试验可变性 和运动终点。这个项目的目标是描述这种 变异性及其神经起源，并测试两个假设的作用，变异性在 这种感觉运动回路。有三个具体目标。1)我们将描述这种关系 猕猴腹侧前运动区视觉引导伸手的变异性与神经活动之间的关系 皮质（PMv）和初级运动皮质（M1）。我们将逐一对试验进行详细分析 运动变异性，使用一些新的分析技术。除了产生新的 深入了解运动变异性的本质，这种分析将提供一套丰富的行为 可以与神经活动相比较的变量。我们将从猕猴的PMv和M1记录 猴子在执行一项伸手可及的任务时。一次又一次的神经活动会被分解 可以用行为解释的变异性和与行为无关的剩余变异性， 行为变异性2)我们将测试我们的假设，达到可变性是在中央控制下， 试图用运动反馈的超感知操纵来改变可变性。与人类 主题，我们将重塑从到达端点到奖励反馈的映射，或者引入 时变变化的视觉反馈的手的位置，以重塑的变化， 到达。然后，我们将采用这些行为技术与猕猴，而我们记录从 PMv和M1，并且我们将沿着沿着每个空间轴的运动变异性的变化与 神经活动的解释和剩余变异性的变化。3)我们将测试我们的假设 神经变异性和学习之间的关系。具体而言，我们建议， 解释神经回路中的可变性有助于该回路中的学习，因为它提供了更丰富的 采样的输入输出空间，而残差变异是有害的学习，因为它 降低了误差信号的质量。我们将通过研究一种特殊形式的 感觉运动学习（sensorimotor learning）：暴露于变化的视觉反馈后的快速适应。通过 通过比较轮班适应的学习率与行为和神经变异的水平， 将决定任何一种变异是否会影响感觉运动学习。

Visually guided reaching is a centrally important behavior in both human and non-human primates. Reaching is a highly stereotyped behavior, with similar movement paths and velocity profiles observed across a wide range of conditions, across subjects, and even across species. Yet another hallmark of visually guided reaching is trial by trial variability in the details of the movement and in the movement endpoint. The goal of this project is to characterize the nature of this variability and its neural origins and to test two hypotheses about the role that variability plays in such sensorimotor circuits. There are three specific aims. 1) We will characterize the relationship between variability in visually guided reaching and neural activity in Macaque ventral premotor cortex (PMv) and primary motor cortex (M1). We will perform a detailed analysis of the trial by trial movement variability, using a number of novel analysis techniques. In addition to yielding new insight into the nature of movement variability, this analysis will provide a rich set of behavioral variables that can be compared to neural activity. We will record from PMv and M1 in Macaque monkeys while they perform a reaching task. The trial by trial neural activity will be decomposed into variability that can be explained by the behavior and a residual variability that unrelated to behavioral variability. 2) We will test our hypothesis that reach variability is under central control by attempting to alter variability using surreptitious manipulations of movement feedback. With human subjects, we will either reshape the mapping from reach endpoint to reward feedback or introduce time-varying changes in the visual feedback of hand position in order to reshape the variability of reaching. We will then employ these behavioral techniques with Macaque while we record from PMv and M1, and we will correlate the changes in movement variability along each spatial axis with changes in the explained and residual variability of neural activity. 3) We will test our hypothesis about the relationship between neural variability and learning. Specifically, we propose that explained variability in a neural circuit is aids learning in that circuit, since it provides a richer sampling of the input-output space, while residual variability is detrimental for learning, since it degrades the quality of the error signal. We will test this hypothesis by studying a particular form of sensorimotor learning: the rapid adaptation that follows exposure to shifted visual feedback. By comparing the learning rate of shift adaptation to the level of behavioral and neural variability, we will determine whether either type of variability affects sensorimotor learning.