The representation of uncertainty in the sensorimotor system

描述(由申请者提供)：在我们对如何在伸展运动中表现不确定性的理解上存在一个根本的差距。我们看到手和潜在目标的能力不断变化：只有当我们看到一个照明良好的物体时，我们才能准确地知道它在哪里；通常情况下，手和目标的相对位置存在着广泛的不确定性。神经元如何对感觉和运动任务中的这种不确定性进行编码的问题可能是计算神经科学中最活跃的研究领域，Lengyel和其他人在2010年COSOTE会议上组织了一个关于这一主题的研讨会，100多名科学家参加了研讨会。这项拟议研究的长期目标是了解神经系统如何在触觉过程中整合信息，并利用这些知识加速神经运动疾病的恢复。这一特殊应用的目的是量化不确定性如何影响感觉运动通路中的神经活动。中心假设是，理论上提出的模型之一或这些模型的组合将解释不确定性的神经表示。这项拟议研究的基本原理是，更好地理解神经系统代表不确定性的方式，并有望改善神经运动疾病的康复，因为不确定性已被证明可以调节学习速度。以初步数据为指导，这些数据表明我们有能力通过追求三个具体目标来执行拟议的研究：1)我们将分析关于一般情况的不确定性是如何表现的，这些不确定性是随着时间的推移而获得的，称为“先前”。2)我们将分析有关当前反馈的不确定性是如何表示的，称为“可能性”。3)我们将分析神经系统是如何学习不确定性的。这种方法是创新的，因为它利用了神经科学的高度集成方法，其中先进的建模和新的数据分析直接与实验设计相结合。这项拟议的研究意义重大，因为它有望垂直推进我们对不确定性表征的理解，并允许明确区分相互竞争的假说和广泛持有的假说。最终，这些知识有可能为物理康复疗法的发展提供信息。由于感觉运动的不确定性随着我们年龄的增长和各种疾病的增加而增加，更好地理解不确定性的神经基础有望帮助减少人口老龄化带来的日益严重的问题。

DESCRIPTION (provided by applicant): There is a fundamental gap in our understanding of how uncertainty is represented during reaching movements. Our ability to see our hand and the potential targets of our reach constantly changes: only when we foveate a well illuminated object can we know precisely where it is; in general there is broadly varying uncertainty about relative location of hand and target. The issue of how neurons encode such kinds of uncertainty for sensory and motor tasks is possibly the most active research area in computational neuroscience and a workshop on this topic organized by Lengyel and others at the 2010 Cosyne meeting was attended by more than 100 scientists. The long term goal of the proposed research is to understand how the nervous system integrates information during reaching and to use this knowledge to accelerate recovery from neuromotor diseases. The objective of this particular application is to quantify how uncertainty affects neural activities in the sensorimotor pathway. The central hypothesis is that one of the theoretically proposed models or a combination of these models will account for the neural representation of uncertainty. The rationale for the proposed research is that a better understanding of the way the nervous system represents uncertainty and promises to improve rehabilitation from neuromotor diseases because uncertainty has been shown to modulate learning speeds. Guided by preliminary data that shows our ability to perform the proposed research by pursuing three specific aims: 1) We will analyze how uncertainty about the general situation, acquired over time and called "prior" is represented. 2) We will analyze how uncertainty about the current feedback, called "likelihood" is represented. 3) We will analyze how the nervous system learns about uncertainty. The approach is innovative because it utilizes a highly integrated approach to neuroscience where advanced modeling and new data analysis is directly integrated with experiment design. The proposed research is significant, because it is expected to vertically advance our understanding of the representation of uncertainty and allows a clear distinction between competing and widely held hypotheses. Ultimately, such knowledge has the potential to inform the development of physical rehabilitation therapies. Since sensorimotor uncertainty increases as we age and with a wide range of diseases, a better understanding of the neural basis of uncertainty promises to help reduce the growing problems of an aging population.