Hippocampal formation contains several classes of neurons thought to be involved in navigational processes, in particular place cells and grid cells. Place cells have been associated with a topological strategy for navigation, while grid cells have been suggested to support metric vector navigation. Grid cell-based vector navigation can support novel shortcuts across unexplored territory by providing the direction toward the goal. However, this strategy is insufficient in natural environments cluttered with obstacles. Here, we show how navigation in complex environments can be supported by integrating a grid cell-based vector navigation mechanism with local obstacle avoidance mediated by border cells and place cells whose interconnections form an experience-dependent topological graph of the environment. When vector navigation and object avoidance fail (i.e., the agent gets stuck), place cell replay events set closer subgoals for vector navigation. We demonstrate that this combined navigation model can successfully traverse environments cluttered by obstacles and is particularly useful where the environment is underexplored. Finally, we show that the model enables the simulated agent to successfully navigate experimental maze environments from the animal literature on cognitive mapping. The proposed model is sufficiently flexible to support navigation in different environments, and may inform the design of experiments to relate different navigational abilities to place, grid, and border cell firing.

中文翻译：

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使用网格导航并将单元格放置在杂乱的环境中。

海马结构包含几类被认为参与导航过程的神经元，特别是位置细胞和网格细胞。位置单元与导航的拓扑策略相关联，而网格单元已被建议支持度量向量导航。基于网格单元的矢量导航可以通过提供朝向目标的方向来支持跨越未探索领域的新捷径。然而，这种策略在充满障碍的自然环境中是不够的。在这里，我们展示了如何通过将基于网格单元的矢量导航机制与由边界单元和位置单元介导的局部避障相结合来支持复杂环境中的导航，其互连形成环境的经验相关拓扑图。当矢量导航和对象避免失败（即代理卡住）时，将单元重放事件设置为更接近矢量导航的子目标。我们证明了这种组合导航模型可以成功地穿越障碍物杂乱的环境，并且在环境未被充分探索的情况下特别有用。最后，我们表明该模型使模拟代理能够成功地从有关认知映射的动物文献中导航实验迷宫环境。所提出的模型足够灵活，可以支持不同环境中的导航，并且可以为实验设计提供信息，以将不同的导航能力与位置、网格和边界单元格触发联系起来。我们证明了这种组合导航模型可以成功地穿越障碍物杂乱的环境，并且在环境未被充分探索的情况下特别有用。最后，我们表明该模型使模拟代理能够成功地从有关认知映射的动物文献中导航实验迷宫环境。所提出的模型足够灵活，可以支持不同环境中的导航，并且可以为实验设计提供信息，以将不同的导航能力与位置、网格和边界单元格触发联系起来。我们证明了这种组合导航模型可以成功地穿越障碍物杂乱的环境，并且在环境未被充分探索的情况下特别有用。最后，我们表明该模型使模拟代理能够成功地从有关认知映射的动物文献中导航实验迷宫环境。所提出的模型足够灵活，可以支持不同环境中的导航，并且可以为实验设计提供信息，以将不同的导航能力与位置、网格和边界单元格触发联系起来。