The representation of uncertainty in the sensorimotor system

描述（由申请人提供）：在我们对不确定性如何在到达运动中表现的理解中存在根本性的差距。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;一般来说，手和目标的相对位置存在广泛的不确定性。神经元如何为感觉和运动任务编码这种不确定性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Cosyne会议上组织了一个关于这个主题的研讨会，有100多名科学家参加。这项研究的长期目标是了解神经系统如何在接触过程中整合信息，并利用这些知识加速神经运动疾病的康复。这个特定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。核心假设是，理论上提出的模型之一或这些模型的组合将占不确定性的神经表征。这项研究的基本原理是，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。在初步数据的指导下，通过追求三个具体目标来显示我们执行拟议研究的能力：1）我们将分析随着时间的推移获得的一般情况的不确定性，称为“先验”。2)我们将分析当前反馈的不确定性（称为“可能性”）是如何表示的。3)我们将分析神经系统如何学习不确定性。该方法是创新的，因为它利用了高度集成的神经科学方法，其中先进的建模和新的数据分析直接与实验设计相结合。拟议的研究是重要的，因为它有望纵向推进我们对不确定性表示的理解，并允许竞争和广泛持有的假设之间的明确区分。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年龄的增长和各种疾病的增加而增加，因此更好地了解不确定性的神经基础有助于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.