RI: Medium: Collaborative Research: Experimental and Robotics Investigations of Multi-Scale Spatial Memory Consolidation in Complex Environments

在一个日益动态和复杂的世界中，导航技术是日常生活中越来越重要的组成部分。这些技术的一个限制是，它们是针对特定的空间尺度进行优化的。另一个限制是，它们不使用以前导航的知识来计算新的路径，基本上每次调用它们时都从头开始。然而，最近的证据表明，哺乳动物的大脑已经进化到并行使用多个空间导航尺度，并使用空间记忆来改善路径规划。如何使用这些天平，以及使用这些天平能带来什么好处，目前还不得而知。该项目假设多尺度空间导航在大而混乱的环境中至关重要。实验记录了啮齿类动物（一种优秀而高效的空间导航动物）“大脑GPS”系统的神经元，试图阐明基于记忆的多尺度空间导航的基本原理。这些实验将提供新的算法，这些算法将在计算机上实现，模拟复杂的多尺度空间导航，模仿大脑的神经计算。然后，模拟将在具有挑战性的复杂环境中导航的实际自主移动机器人上进行测试和改进。该项目将使用无线高密度神经记录技术，允许对大量单个神经元进行并行记录。光遗传学技术将用于操纵这些神经元的活动，并研究它们对动物行为和空间记忆的影响。神经活动的多尺度模式将用于机械计算模型的开发，该模型将在新的任意模拟环境中进行测试，并生成关于神经系统如何成功或失败的预测。最后，仿真将被移植到移动机器人上，当机器人面临真实的传感器噪声和不可靠的世界特征时，算法可以被测试和改进。

Navigation technologies are an increasingly important component of everyday life in an ever more dynamic and complex world. One limitation of these technologies is that they are optimized for a specific spatial scale. Another limitation is that they do not use the knowledge of previous navigation to compute new paths, essentially starting from scratch every time they are invoked. Recent evidence shows, however, that the mammalian brain has evolved to use multiple spatial navigation scales in parallel, and to use spatial memory to improve path planning. How these scales are used and what advantages such uses provide are still unknown. This project hypothesizes that multiscale spatial navigation is crucial in large and cluttered environments. Experiments recording from the neurons of the "brain GPS" system of the rodent (an excellent and efficient spatial navigator) seek to elucidate the basic principles of memory-based multiscale spatial navigation. These experiments will inform new algorithms that will be implemented on a computer to simulate complex multiscale spatial navigation, mimicking the neural computations of the brain. The simulations will then be tested and improved on actual autonomous mobile robots navigating in challenging complex environments.The project will use wireless high density neural recording technologies allowing for parallel recording of large populations of individual neurons. Optogenetic techniques will be used to manipulate the activity of these neurons and study their impact on the behavior and spatial memory of the animal. The multiscale pattern of neural activity will be used in the development of a mechanistic computational model, which will be tested in new and arbitrary simulated environments, and generate predictions as to how the neural system might succeed or fail. Finally, the simulations will be ported onto a mobile robot, where the algorithms can be tested and improved when the robot is faced with real sensor noise and unreliable world features.