Department of Computer Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.
Institute of Cognitive Neuroscience, University College London, Alexandra House, 17 Queen Square, WC1N 3AZ London, UK.
Hippocampus. 2020 Mar;30(3):220-232. doi: 10.1002/hipo.23147. Epub 2019 Aug 13.
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.
海马结构包含几类神经元，这些神经元被认为与导航过程有关，特别是位置细胞和网格细胞。位置细胞与导航的拓扑策略有关，而网格细胞则被认为支持度量向量导航。基于网格细胞的向量导航可以通过提供朝向目标的方向，支持在未探索的区域中进行新的捷径。然而，这种策略在充满障碍物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细胞的向量导航机制与边界细胞和位置细胞介导的局部障碍物回避相结合来支持复杂环境中的导航，边界细胞和位置细胞的连接形成了环境的经验依赖拓扑图。当向量导航和物体回避失败（即，代理被卡住）时，位置细胞回放事件为向量导航设置更近的子目标。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，特别是在环境未被充分探索的情况下非常有用。最后，我们表明该模型使模拟代理能够成功地从动物认知图文献中的实验迷宫环境中进行导航。所提出的模型足够灵活，可以支持不同环境中的导航，并可能为设计实验提供信息，以将不同的导航能力与位置、网格和边界细胞的发射相关联。
