Learning to Represent Space in the Brain

学习在大脑中表示空间

• 批准号: EP/Y024656/1
• 负责人: Aman Saleem
• 金额: $ 274.28万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY024656%2F1
• 关键词: Learning; Represent; Space; Brain

Many actions in our daily lives - getting to work, exploring a city or moving around a room - involve navigation. To navigate we need to sense where we are, for which we predominantly use vision. The goal of this proposal is to understand how brain circuits learn to use visual information to support navigation.Navigational areas of brain are known to represent space, including place cells of the hippocampus and grid cells of the entorhinal cortex. This representation of space is supported by inputs from visual cortical areas. The conventional model for processing a representation of space is a hierarchical model, where visual cortical areas build increasingly elaborate representations of visual features over the visual cortical hierarchy, and then feed these representations to navigational areas. Recent results however find a representation of space even in the primary visual cortex, suggesting a more distributed model of processing. This leads to a mystery as to which of the conflicting models is true: hierarchical or distributed. Based on the gradual appearance of spatial signals in the visual cortex, I hypothesize that it is possible for them to be true at different times, and learning transforms the representation from hierarchical to distributed. We will test this central hypothesis using a combination of virtual reality, large scale recordings from multiple brain regions, and inactivations and recordings from specific feedback projections to the visual cortex. Navigation is an essential cognitive ability common to all animals, and is a cognitive archetype, fusing perception, memory, planning and decision making. Understanding how spatial representations are built in the brain therefore offers the opportunity to understand both navigation itself and the wider mechanisms of general cognitive abilities. It thus has the potential to transform the fields of sensory neuroscience, memory research and neuromorphic machine learning.

我们日常生活中的许多行为-上班，探索城市或在房间里走动-都涉及导航。为了导航，我们需要感觉我们在哪里，我们主要使用视觉。这项计划的目的是了解大脑回路如何学习使用视觉信息来支持导航。已知大脑的导航区域代表空间，包括海马的位置细胞和内嗅皮层的网格细胞。这种空间表征是由视觉皮层区域的输入支持的。用于处理空间表示的传统模型是分层模型，其中视觉皮层区域在视觉皮层层次结构上构建越来越精细的视觉特征表示，然后将这些表示馈送到导航区域。然而，最近的研究结果发现，即使在初级视觉皮层的空间表示，这表明一个更分布式的处理模型。这导致了一个谜，即冲突的模型是正确的：分层或分布式。基于视觉皮层中空间信号的逐渐出现，我假设它们在不同的时间是真实的，学习将表征从分层转变为分布式。我们将使用虚拟现实、来自多个大脑区域的大规模记录以及来自视觉皮层的特定反馈投射的失活和记录来测试这个中心假设。导航是所有动物共同的基本认知能力，是一种认知原型，融合了感知，记忆，计划和决策。因此，理解空间表征是如何在大脑中建立的，为理解导航本身和一般认知能力的更广泛机制提供了机会。因此，它有可能改变感觉神经科学，记忆研究和神经形态机器学习领域。