Computational studies of spatial motion estimation and motor planning in the primate brain

灵长类大脑空间运动估计和运动规划的计算研究

• 批准号: 376236-2010
• 负责人: Green, Andrea
• 金额: $ 2.19万
• 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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572870
• 关键词: Computational; studies; spatial; motion; estimation

To execute accurate movements, maintain balance, navigate in space and perceive a visually stable world we rely critically on estimates of our self-motion and orientation. As we move, the central nervous system (CNS) integrates multiple sensory signals to estimate our spatial motion, combining and interpreting these signals as a function of the context in which they are sensed and how they will be used. My research investigates how the CNS creates these different self-motion representations and how they are used behaviorally. I address these questions using an interdisciplinary approach that combines experimental and computational techniques. In this application I am requesting funds to support my research program in computational neuroscience, the long-term goal of which is to develop hypotheses (in the form of computational models) for how the CNS creates and uses self-motion estimates for perception and action. Its aim is to generate testable predictions that inspire future experimental studies (in many labs, including my own). Recently, I developed a model to explore how a brainstem-cerebellar network resolves a fundamental problem associated with interpreting ambiguous vestibular signals. Several of its key predictions were verified through behavioral and neural recording studies. This model provides the starting point for a first set of projects that include: 1) extending the modeling framework to explore the general strategies brainstem-cerebellar circuits use to estimate self-motion and orientation in 3D; 2) developing a physiologically realistic model implementation to investigate how neural populations actually perform the required computations; 3) investigating how visual and proprioceptive cues are integrated with vestibular signals to compute different motion representations. Future work will explore how such subcortical motion representations are further processed in the cortex for various motor and perceptual tasks. Ultimately, this research will provide new insights into brain mechanisms that allow us to perceive and 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.

为了执行准确的运动，保持平衡，在空间中导航并感知视觉上稳定的世界，我们严格依赖于对自我运动和方向的估计。当我们移动时，中枢神经系统（CNS）整合多个感觉信号来估计我们的空间运动，将这些信号组合和解释为它们被感知的上下文以及它们将如何被使用的函数。我的研究调查了中枢神经系统如何创造这些不同的自我运动表征，以及它们如何在行为上被使用。我解决这些问题使用跨学科的方法，结合实验和计算技术。 在这个应用程序中，我要求资金支持我的研究计划在计算神经科学，其长期目标是发展假设（以计算模型的形式）的中枢神经系统如何创建和使用感知和行动的自我运动估计。它的目标是产生可测试的预测，激发未来的实验研究（在许多实验室，包括我自己的实验室）。 最近，我开发了一个模型来探索脑干-小脑网络如何解决与解释模糊的前庭信号相关的基本问题。它的几个关键预测通过行为和神经记录研究得到了验证。该模型为第一组项目提供了起点，这些项目包括：1）扩展建模框架以探索脑干-小脑回路用于估计3D中的自我运动和方向的一般策略; 2）开发生理学上现实的模型实现以研究神经群体实际上如何执行所需的计算;（3）研究视觉和本体感受线索如何与前庭信号整合以计算不同的运动表征。未来的工作将探讨如何在皮层下的运动表征进一步处理各种运动和知觉任务。最终，这项研究将为大脑机制提供新的见解，使我们能够在移动时感知环境并与之互动。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。