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

这项研究调查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关键特征，没有它，生存是脆弱的。这项提议的研究特别关注大尺度空间的表现，比如帮助你定位自己或房间里的物体，或者在办公楼或城镇里导航。在大脑的一个叫做海马体的区域中，不同类型的空间细胞提供了我们大规模空间知识的基本构建块。空间细胞的一个例子是一种类似指南针的细胞，叫做头部方向细胞，当你的头朝东时，它就会发出信号。另一种细胞是位置细胞。位置细胞在不同的环境背景下在特定的位置放电。一个地点细胞可能在你家厨房门附近发射，但也可能在不同的环境背景下发射，比如在你办公室附近的走廊上。其他位置细胞也会沿着这条走廊开火，包括它的分支部分，其中一个分支直接通往消防出口。有一天，当你的工作场所着火了，所有的东西都冒烟了，这些位置细胞可能会帮助你到达安全出口，即使你看不见。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，后来我发现，我们称之为“边界向量细胞”的细胞。每当边界位于与主题的首选距离和方向上时，边界向量单元就会触发。边界的例子包括房间的墙壁、悬崖、走廊或建筑物的侧面。每个边界向量单元都有自己的首选距离和方向。一个边界向量细胞可能会在物体的南面有一个非常接近的边界时被激活。另一个边界向量细胞可能会在物体东北方向三米外出现边界时触发。这些细胞很可能构成海马体结构网络的一部分，例如，这些网络允许位置细胞在特定环境中可靠地放电，从而允许精确的导航。所提出的研究的基本思想是获得大量的边界向量细胞数据集，并对它们进行详细的测试，一个接一个地作为一个群体，与预测它们发现的现有模型相比较。我们的模型在不同环境下预测BVC放电的效果如何？对于每一个记录的BVC，基于它在一个子集的环境中的激活，我们将得到一个模型来预测细胞在另一个子集的环境中如何激活。我们还将研究它们与其他类型的细胞，如位置细胞的相互作用。我们将测试这个假设，即位置细胞，因为他们学习新的环境，改变他们的放电时间相对于一个突出的振荡称为θ（即大脑中的一种时钟），当他们最初学习，而至少一些边界向量细胞，没有表现出任何学习能力，不会改变他们的放电时间在学习过程中。（这个假设存在的部分原因是，我们知道改变相对于θ波振荡的时间可以增强对所学信息长期记忆的生理过程。）总之，通过记录和建模bvc，我们将建立一个更精确和复杂的海马结构空间表征模型。这将对海马体依赖记忆、学习理论、空间语言学和机器人等不同领域的研究产生重大影响。

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.