Schøyen Vemund, Pettersen Markus Borud, Holzhausen Konstantin, Fyhn Marianne, Malthe-Sørenssen Anders, Lepperød Mikkel Elle
Department of Biosciences, University of Oslo, Oslo 0313, Norway.
Department of Physics, University of Oslo, Oslo 0313, Norway.
iScience. 2023 Sep 30;26(11):108102. doi: 10.1016/j.isci.2023.108102. eCollection 2023 Nov 17.
It is widely believed that grid cells provide cues for path integration, with place cells encoding an animal's location and environmental identity. When entering a new environment, these cells remap concurrently, sparking debates about their causal relationship. Using a continuous attractor recurrent neural network, we study spatial cell dynamics in multiple environments. We investigate grid cell remapping as a function of global remapping in place-like units through random resampling of place cell centers. Dimensionality reduction techniques reveal that a subset of cells manifest a persistent torus across environments. Unexpectedly, these toroidal cells resemble band-like cells rather than high grid score units. Subsequent pruning studies reveal that toroidal cells are crucial for path integration while grid cells are not. As we extend the model to operate across many environments, we delineate its generalization boundaries, revealing challenges with modeling many environments in current models.
人们普遍认为，网格细胞为路径整合提供线索，而位置细胞编码动物的位置和环境特征。当进入一个新环境时，这些细胞会同时重新映射，引发了关于它们因果关系的争论。我们使用连续吸引子递归神经网络来研究多个环境中的空间细胞动力学。我们通过对位置细胞中心进行随机重采样，将网格细胞重新映射作为类位置单元中全局重新映射的函数进行研究。降维技术表明，一部分细胞在不同环境中呈现出持续的环面。出乎意料的是，这些环形细胞类似于带状细胞，而不是高网格分数单元。随后的修剪研究表明，环形细胞对路径整合至关重要，而网格细胞则不然。当我们将模型扩展到在多个环境中运行时，我们划定了它的泛化边界，揭示了当前模型在对多个环境进行建模时面临的挑战。
