Building spatial maps from visual and self-motion inputs

从视觉和自我运动输入构建空间地图

• 批准号: BB/W007878/1
• 负责人: Guifen Chen
• 金额: $ 62.79万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FW007878%2F1
• 关键词: Building; spatial; maps; visual; self

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.

研究图物理环境的背景拥有许多不同的线索，这些提示是我们的感觉系统所感知的。当我们在环境中移动时，我们观察到感觉线索的相应变化。在哺乳动物中，海马及其内侧颞叶中的相邻区域长期以来一直与空间导航和学习有关。在该区域中发现了几种类型的空间神经元，包括位置细胞和网格细胞。这些神经元的活性代表动物的当前位置。尽管发现了空间的内部表示（或“地图”），但尚不清楚大脑如何将环境感官提示（例如视觉地标）与自动移动信息（例如运动或视频流动提示）相结合以形成这些图。因此，该项目的重点是在空间映射过程中解散视觉和自感觉线索对位置细胞和网格细胞的影响。从历史上看，出于三个关键原因，研究成人动物的问题是一项挑战。首先，在现实世界中很难实现视觉和自我运动输入的影响。其次，位置单元格和网格细胞网络是互连的，因此很难独立研究单独的网络。第三，当动物进入新环境时，空间表示几乎立即出现。这表明动物从以前的经验中学习，可能会开发一种通用的代码，使它们能够快速按需构建新的空间表示。我最近开发了二维虚拟现实（2D VR）系统，为老鼠提供了在虚拟世界中导航的沉浸式体验。开创性的发展使我处于一个独特的立场，以回答以前具有挑战性的问题。新的2D VR允许独立操纵2D空间中的视觉和自我运动线索。我的初步数据表明，虚拟世界中的空间表示与现实世界中的空间表示相似，但形式的速度要慢得多。因此，我们第一次有一个延长的窗口，在此期间研究空间表示的形成，尤其是视觉和自我运动提示对编队过程的影响。aim和目标该项目将通过利用新的2D VR系统来研究位置细胞和网格细胞在构建空间表示中的独特作用。目的是了解位置和网格细胞如何相互作用，并结合视觉和自我运动提示以表示空间。我将首先建立成年小鼠2D虚拟空间中空间表示形成的时间表。接下来，我将区分有关形成空间表示的视觉和自我运动信息的贡献。最后，我将测试这些提示如何影响已建立的空间图。潜在的应用程序和受益于该项目在BBSRC的愿景中概述的主要挑战之一 - “了解生命规则”，并且与BBSRC的战略优先级“对生物科学的方法”完全一致。该项目提供了一个新的角度，以了解空间细胞及其在空间学习中的功能之间的相互作用，为在人工智能，机器人导航和衰老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精确的模型，模拟了长期记忆，从而有助于人工智能的发展。其次，这项工作为教学机器人提供了洞察力，如何整合多感官输入，执行复杂的地形导航。第三，这些发现将有助于我们了解正常衰老中记忆加工的神经基础，以及诸如痴呆等神经退行性疾病。