Variation as a neural code

视觉引导触觉在人类和非人类灵长类动物中都是一种非常重要的行为。 伸手是一种高度刻板印象的行为，有着相似的运动路径和速度曲线 在广泛的条件下，跨受试者，甚至跨物种观察。还没有 视觉引导触觉的另一个特点是在运动细节上的试验可变性 以及在运动终点中。这个项目的目标是描述这一现象的性质 变异性及其神经起源，并检验关于变异性在 这样的感应马达电路。有三个具体目标。1)我们将描述这一关系 视觉引导触觉的变异性与猕猴腹侧前运动神经活动之间的关系 大脑皮质(PMV)和初级运动皮质(M1)。我们将通过审判对审判进行详细的分析 运动的可变性，使用了许多新的分析技术。除了产生新的 洞察运动变异性的本质，这一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。我们将在Macaque从PMV和M1进行录制 猴子在执行伸手可及任务时。审判神经活动的审判将被分解 转化为可由行为解释的可变性，以及与 行为变异性。2)我们将通过以下方式测试我们的假设，即到达变异性处于中央控制之下 试图使用移动反馈的秘密操作来改变可变性。与人类 主题，我们将重塑从REACH端点到奖励反馈的映射，或者引入 手位视觉反馈的时变变化以重塑变异性 伸手可及。然后我们将对Macaque使用这些行为技术，同时我们将从 PMV和M1，我们将把沿每个空间轴的运动变异性的变化与 神经活动的解释变异性和残存变异性的变化。3)我们将检验我们的假设 关于神经变异性和学习之间的关系。具体来说，我们建议 解释神经回路中的可变性有助于在该回路中学习，因为它提供了更丰富的 输入-输出空间的采样，而残差变异性不利于学习，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：在接触到变化的视觉反馈后的快速适应。通过 将换班适应的学习速度与行为和神经变异性水平进行比较，我们 将决定这两种类型的变异性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.