Building spatial maps from visual and self-motion inputs

研究背景我们的物理环境拥有许多不同的线索，这些线索被我们的感官系统感知。当我们在环境中移动时，我们观察到感官线索的相应变化。在哺乳动物中，海马及其邻近的内侧颞叶区域长期以来与空间导航和学习有关。在这个区域已经发现了几种类型的空间神经元，包括位置细胞和网格细胞。这些神经元的活动代表了动物的当前位置。尽管发现了空间的内部表征（或“地图”），但仍然不清楚大脑如何将环境感觉线索（例如视觉地标）与自我运动信息（例如运动或光流线索）结合起来以形成这些地图。因此，本项目的重点是解开空间映射过程中视觉和自我运动线索对位置细胞和网格细胞的影响。从历史上看，在成年动物中研究这个问题具有挑战性，原因有三个。首先，在真实的世界中，很难分离视觉和自我运动输入的效果。其次，位置细胞和网格细胞网络是相互关联的，因此很难独立地研究单独的网络。第三，当动物进入一个新的环境时，空间表征几乎是瞬间出现的。这表明动物从以前的经验中学习，可能发展出一种通用的代码，使它们能够根据需要快速构建新的空间表示。我最近开发了一个二维虚拟现实（2D VR）系统，为老鼠提供沉浸式的虚拟世界导航体验。开创性的发展使我处于一个独特的位置来回答这个以前具有挑战性的问题。新的2D VR允许在2D空间中独立操纵视觉和自我运动提示。我的初步数据显示，虚拟世界中的空间表征与真实的世界中的空间表征相似，但形成的速度要慢得多。因此，我们第一次有一个长时间的窗口，在此期间，研究空间表征的形成，特别是视觉和自我运动线索对形成过程的影响。目的和目标该项目将研究的独特作用的地方细胞和网格细胞在建立空间表征，利用新的2D VR系统。其目的是了解地方和网格细胞如何相互作用，并联合收割机视觉和自我运动的线索，以代表空间。我将首先建立成年小鼠在2D虚拟空间中形成空间表征的时间轴。接下来，我将区分视觉和自我运动信息对形成空间表征的贡献。最后，我将测试如何改变这些线索影响建立空间地图。该项目解决了BBSRC愿景中概述的关键挑战之一-“理解生命规则”，并与BBSRC的战略优先事项“生物科学的系统方法”完全一致。该项目为理解空间细胞之间的相互作用及其在空间学习中的功能提供了一个新的角度，为人工智能，机器人导航和老龄化领域的应用提供了基础。首先，这些发现将使计算神经科学家能够创建越来越准确的模型来模拟长期记忆，从而为人工智能的发展做出贡献。其次，这项工作为教机器人如何整合多感官输入，执行复杂地形导航提供了见解。第三，这些发现将帮助我们了解正常衰老过程中记忆处理的神经基础，以及痴呆症等神经退行性疾病。

Context of researchOur physical environment possesses many different cues that are perceived by our sensory systems. As we move through the environment, we observe a corresponding change in the sensory cues. In mammals, the hippocampus and its adjacent areas in the medial temporal lobe, have long been implicated in spatial navigation and learning. Several types of spatial neurons have been discovered in this area, including place cells and grid cells. The activity of these neurons represents an animal's current location. Despite this discovery of an internal representation (or "map") of space, it remains unclear how the brain combines environmental sensory cues (e.g. visual landmarks) with self-motion information (e.g. locomotor or optic flow cues) in order to form these maps. Hence, the focus of this project is to disentangle the effects of visual and self-motion cues on place cells and grid cells during spatial mapping.Historically, it was challenging to study the question in adult animals for three key reasons. First, separating the effects of visual and self-motion inputs is difficult to achieve in the real world. Second, place cell and grid cell networks are interconnected, hence it is difficult to study the separate networks independently. Third, spatial representations appear almost instantaneously when an animal enters a new environment. This suggests that animals learn from previous experience, possibly developing a generalized code that enables them to quickly construct new spatial representations on demand.I have recently developed a two-dimensional virtual reality (2D VR) system, providing mice with an immersive experience of navigating in a virtual world. The pioneering development places me in a unique position to answer the question which was previously challenging. The new 2D VR allows independent manipulations of visual and self-motion cues in 2D space. My preliminary data show that spatial representations in a virtual world are similar to those in the real world, but form at a much slower pace. Thus, for the first time, we have a prolonged window during which to study the formation of spatial representations, and in particular the effects of visual and self-motion cues on the formation process.Aim and objectives The project will study the distinct roles of place cells and grid cells in building spatial representations, by taking advantage of the new 2D VR system. The aim is to understand how place and grid cells interact and combine visual and self-motion cues to represent space. I will first establish the timeline of the formation of spatial representations in 2D virtual space in adult mice. Next, I will differentiate the contributions of visual and self-motion information on forming spatial representations. Finally, I will test how varying these cues affects established spatial maps. Potential applications and benefitsThe project tackles one of the key challenges outlined in the BBSRC's vision - "Understanding the rules of life", and is perfectly aligned with the BBSRC's strategic priority "Systems approaches to the bioscience". The project offers a new angle for understanding the interaction between spatial cells and their functions in spatial learning, providing the foundation for the applications in the fields of artificial intelligence, robotic navigation and ageing. First, the findings will allow computational neuroscientists to create increasingly accurate models simulating long-term memory, contributing to the development of artificial intelligence. Second, the work offers insight into teaching robots how to integrate multisensory inputs, perform complex terrain navigation. Third, the findings will help us understand the neural basis of memory processing in normal ageing, as well as neurodegenerative diseases such as dementia.