Boundary Vector Cells (BVCs): a novel type of fundamental spatial cell in the hippocampal formation

这项研究研究了一种新型的空间细胞，它可能是我们空间知识的重要组成部分。适当地获取和使用空间知识是大多数动物和人类行为的一个重要特征，没有它，生存就很脆弱。拟议的研究特别着眼于大尺度空间的表示，例如可以帮助您定位自己或房间中的物体，或者在办公楼或城镇中导航。大脑中称为海马结构的区域中不同类型的空间细胞为我们的大规模空间知识提供了基本构建模块。空间细胞的一个例子是一种类似罗盘的细胞，称为头部方向细胞，当你的头朝东时，它就会放电。另一种细胞是位置细胞。将细胞点火放置在不同环境背景下的特定位置。一个地方的细胞可能会在家里厨房的门附近发生火灾，但也会在不同的环境中发生火灾，例如沿着工作办公室附近的走廊发生火灾。其他地方细胞也会沿着走廊开火，包括它分支的部分，其中一个分支直接通向消防出口。有一天，当你的工作发生火灾并且一切都冒烟时，即使你看不到，这些位置细胞也可能会帮助你到达消防出口。先前的研究表明，位置细胞受到环境边界的强烈影响。我和我的合作者提出了一个模型来解释这些位置细胞在不同环境中的一些典型特征。我们预测了并且我随后发现了我们称之为“边界向量细胞”的细胞。只要边界位于距主体的首选距离和方向，边界矢量单元就会触发。边界的示例包括房间墙壁、悬崖以及走廊或建筑物的侧面。每个边界矢量单元都有自己的首选距离和方向。当目标南部有一个非常接近的边界时，一个边界矢量单元可能会以最佳方式触发。当目标东北方约三米处存在边界时，另一个边界矢量单元可能会最佳地触发。这些细胞很可能构成海马结构网络的一部分，使位置细胞能够在特定环境中可靠地放电，从而实现精确导航。所提出的研究的基本思想是获得边界矢量细胞的大型数据集，并根据预测其发现的现有模型，对它们进行逐个和整体的详细测试。我们的模型在不同环境下预测 BVC 射击的能力如何？对于每个记录的 BVC，根据其在一个环境子集中的激发，我们将让模型预测细胞在另一个环境子集中将如何激发。我们还将检查它们与其他类型细胞（例如位置细胞）的相互作用。我们将测试这样的假设：当细胞最初学习时，因为它们了解新的环境，所以改变了它们相对于称为 theta（即大脑中的一种时钟）的显着振荡的放电时间，而至少一些没有表现出任何学习能力的边界向量细胞在学习过程中不会改变它们的放电时间。 （存在这一假设的部分原因是我们知道改变相对于 theta 振荡的时间可以增强学习信息长期记忆的生理过程。）总而言之，通过记录和建模 BVC，我们将在海马结构中建立一个更准确和更复杂的空间表征模型。这将对海马依赖性记忆、学习理论、空间语言学和机器人学等不同领域产生重大影响。

This research investigates a new type of spatial cell, which is likely a crucial building block of our spatial knowledge. Acquiring and using spatial knowledge appropriately is a crucial feature of most animal and human behaviour, without which survival is tenuous. The proposed research looks particularly at the representation of large-scale space, such as would help you to locate yourself or an object in a room, or navigate your way through an office building or town. Different types of spatial cell in a region of the brain called the hippocampal formation provide the basic building blocks of our large-scale spatial knowledge. One example of a spatial cell is a compass-like cell called the head-direction cell that fires, say, whenever your head faces east. Another kind of cell is the place cell. Place cells fire in particular locations in different environmental contexts. One place cell might fire near the door to your kitchen at home, but also in a different environmental context, such as along the corridor near your office at work. Other place cells will fire along that corridor too, including the part where it branches, one branch leading right to the fire exit. One day, when there's a fire in your work and everything is smoky, those place cells might help you to reach the fire exit even though you can't see. Previous work has shown that place cells are strongly influenced by environmental boundaries. My collaborators and I presented a model to explain some of the typical characteristics of these place cells in different environments. We predicted, and I subsequently discovered, cells which we called 'Boundary vector cells'. A boundary vector cell fires whenever a boundary is located at a preferred distance and direction from the subject. Examples of boundaries include room walls, a cliff, and the sides of a corridor or building. Each boundary vector cell has its own preferred distance and direction. One boundary vector cell might optimally fire when there's a very close boundary to the south of the subject. Another boundary vector cell might optimally fire when there's a boundary about three metres away to the north-east of the subject. It is likely that these cells form part of the network in the hippocampal formation that allow place cells, for example, to fire reliably in a particular environment, and thus permit accurate navigation. The basic idea of the proposed research is to obtain a large dataset of boundary vector cells, and to test them in detail, one by one and as a population, against the existing model that predicted their discovery. How well can our model predict BVC firing in different environments? For each recorded BVC, on the basis of its firing in a subset of environments, we will get the model to make a prediction about how the cell will fire in another subset of environments. We will also examine their interaction with other kinds of cells, such as the place cells. We will test the hypothesis that place cells, because they learn about new contexts, change the timing of their firing relative to a prominent oscillation called theta (i.e. a kind of clock in the brain) when they are initially learning, while at least some boundary vector cells which do NOT show any ability to learn, will NOT change the timing of their firing during learning. (This hypothesis exists in part because we know that changing the timing relative to the theta oscillation can enchance the physiological processes underlying the long-term memorability of learned information.) In all, by recording and modelling BVCs, we will build a more accurate and complex model of spatial representation in the hippocampal formation. This will have major implications for diverse fields such as the study of hippocampal-dependent memory, learning theory, spatial linguistics, and robotics.