Interdisciplinary studies of spatial motion estimation and motor planning

空间运动估计和运动规划的跨学科研究

• 批准号: RGPIN-2017-05408
• 负责人: Green, Andrea
• 金额: $ 1.82万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=652390
• 关键词: Interdisciplinary; studies; spatial; motion; estimation

Whether we're running to catch a ball or turning to reach for a coffee cup, our ability to interact with the environment depends critically on knowing our motion and orientation in space. As we move, the central nervous system (CNS) combines information from multiple sensory sources (e.g., vestibular, somatosensory, visual) to construct estimates of our motion that we use to control posture, navigate, perform voluntary actions, and maintain clear visual perception. This poses computational challenges because individual sensors often provide ambiguous motion information and different tasks require different motion representations. These, in turn, contribute to behavior via a range of different mechanisms. The goal of my research program is to study how self-motion estimates are computed and how they are used in the planning and control of motor behavior.******* Our interactions with the environment (e.g., reaching to an object) are often executed with the body in motion. However, we still know surprisingly little about how self-motion estimates contribute to voluntary actions. Similarly, how we compute the types of body- and world-centered motion representations used for tasks such as reaching, postural control and navigation remains poorly understood. In this application, I am requesting the renewal of my NSERC Discovery Grant to continue ongoing human behavioral and computational modeling studies aimed at these questions. Project 1 explores the mechanisms by which vestibular signals contribute to reach planning and execution. One set of experiments addresses how the CNS compensates for both the spatial displacement of the limb and additional forces imposed on it by unexpected body motion. It tests the hypothesis that the processing of vestibular signals for online reach execution takes into account knowledge of limb biomechanics. A second set of experiments studies how the processing of vestibular signals for reaching is influenced by objects in the environment other than the reach goal, which may become obstacles and influence the reach path we choose. Project 2 develops computational models to explore how the CNS integrates sensory signals to create the types of motion estimates required for behaviors such as reaching, postural control and navigation. It builds on our existing theoretical framework for how brainstem-cerebellar circuits compute self-motion estimates to incorporate recent physiological findings and to explore hypotheses for how they create novel body- and world-centered motion representations. The aim is to generate precise predictions for neural response properties that can be tested experimentally.****** Ultimately, this work will provide new insights into brain mechanisms that allow us to interact with our environment as we move. It will have application for treating balance and spatial disorientation problems as well as for robotics, neural prosthetics and space travel.*********

无论我们是跑去接球还是转身去拿咖啡杯，我们与环境互动的能力关键取决于我们在空间中的运动和方向。当我们移动时，中枢神经系统（CNS）将来自多个感官来源的信息（例如，前庭、躯体感觉、视觉）来构建我们的运动估计，我们使用这些估计来控制姿势、导航、执行自主动作和保持清晰的视觉感知。这带来了计算挑战，因为单个传感器通常提供模糊的运动信息，并且不同的任务需要不同的运动表示。这些反过来又通过一系列不同的机制对行为做出贡献。我的研究项目的目标是研究自我运动估计是如何计算的，以及它们如何用于运动行为的规划和控制。 我们与环境的互动（例如，达到一个对象）经常执行与身体在运动中。然而，令人惊讶的是，我们仍然对自我运动估计如何促进自愿行动知之甚少。类似地，我们如何计算用于诸如伸手、姿势控制和导航等任务的以身体和世界为中心的运动表示的类型仍然知之甚少。在这个应用程序中，我请求更新我的NSERC发现补助金，以继续进行针对这些问题的人类行为和计算建模研究。项目1探索前庭信号有助于达成计划和执行的机制。一组实验解决了中枢神经系统如何补偿肢体的空间位移和意外的身体运动对其施加的额外力。它测试的假设，前庭信号的处理，在线达到执行考虑到肢体生物力学的知识。第二组实验研究了前庭信号的处理如何受到环境中的物体而不是到达目标的影响，这些物体可能成为障碍物并影响我们选择的到达路径。项目2开发计算模型，探索中枢神经系统如何整合感觉信号，以创建行为所需的运动估计类型，如达到，姿势控制和导航。它建立在我们现有的理论框架，脑干-小脑回路如何计算自我运动估计，结合最近的生理发现，并探索他们如何创造新的身体和世界为中心的运动表征的假设。其目的是为神经反应特性生成精确的预测，这些预测可以通过实验进行测试。 最终，这项工作将为大脑机制提供新的见解，使我们能够在移动时与环境互动。它将应用于治疗平衡和空间定向障碍问题，以及机器人，神经修复和太空旅行。