The hippocampus has long been observed to encode a representation of an animal's position in space. Recent evidence suggests that the nature of this representation is somewhat predictive and can be modeled by learning a successor representation (SR) between distinct positions in an environment. However, this discretization of space is subjective making it difficult to formulate predictions about how some environmental manipulations should impact the hippocampal representation. Here, we present a model of place and grid cell firing as a consequence of learning a SR from a basis set of known neurobiological features—boundary vector cells (BVCs). The model describes place cell firing as the successor features of the SR, with grid cells forming a low‐dimensional representation of these successor features. We show that the place and grid cells generated using the BVC‐SR model provide a good account of biological data for a variety of environmental manipulations, including dimensional stretches, barrier insertions, and the influence of environmental geometry on the hippocampal representation of space.

中文翻译：

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空间导航的神经生物学后继特征。

长期以来，人们一直观察到海马体对动物在空间中的位置进行编码。最近的证据表明，这种表示的性质在某种程度上具有预测性，并且可以通过学习环境中不同位置之间的后继表示 (SR) 来建模。然而，这种空间离散化是主观的，因此很难就某些环境操作应如何影响海马体表示做出预测。在这里，我们提出了一个位置和网格细胞发射模型，这是从一组已知的神经生物学特征——边界向量细胞 (BVC) 中学习 SR 的结果。该模型将位置单元激发描述为 SR 的后继特征，网格单元形成这些后继特征的低维表示。