Classic studies have shown that place cells are organized along the dorsoventral axis of the hippocampus according to their field size, with dorsal hippocampal place cells having smaller field sizes than ventral place cells. Studies have also suggested that dorsal place cells are primarily involved in spatial navigation, while ventral place cells are primarily involved in context and emotional encoding. Additionally, recent work has shown that the entire longitudinal axis of the hippocampus may be involved in navigation. Based on the latter, in this paper we present a spatial cognition reinforcement learning model inspired by the multiscale organization of the dorsal-ventral axis of the hippocampus. The model analyzes possible benefits of a multiscale architecture in terms of the learning speed, the path optimality, and the number of cells in the context of spatial navigation. The model is evaluated in a goal-oriented task where simulated rats need to learn a path to the goal from multiple starting locations in various open-field maze configurations. The results show that smaller scales of representation are useful for improving path optimality, whereas larger scales are useful for reducing learning time and the number of cells required. The results also show that combining scales can enhance the performance of the multiscale model, with a trade-off between path optimality and learning time.

中文翻译：

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结合背侧和腹侧海马位置场图的空间认知计算模型：多尺度导航。

经典研究表明，位置细胞根据其视野大小沿海马背腹轴组织，背侧海马位置细胞的视野大小小于腹侧位置细胞。研究还表明，背位细胞主要参与空间导航，而腹位细胞主要参与背景和情感编码。另外，最近的工作表明海马的整个纵轴可能参与导航。基于后者，我们在本文中提出了一种空间认知增强学习模型，该模型受海马背腹轴多尺度组织的启发。该模型从学习速度，路径最优性，以及空间导航中的像元数。在面向目标的任务中评估该模型，在该任务中，模拟大鼠需要从各种开放式迷宫配置中的多个起始位置学习到目标的路径。结果表明，较小的表示比例有助于提高路径的最优性，而较大的表示比例则有助于减少学习时间和所需单元格的数量。结果还表明，结合尺度可以增强多尺度模型的性能，并在路径最优性和学习时间之间进行权衡。结果表明，较小的表示比例可用于改善路径最优性，而较大的表示比例可用于减少学习时间和所需的单元数。结果还表明，结合尺度可以提高多尺度模型的性能，并在路径最优性和学习时间之间进行权衡。结果表明，较小的表示比例可用于改善路径最优性，而较大的表示比例可用于减少学习时间和所需的单元数。结果还表明，结合尺度可以增强多尺度模型的性能，并在路径最优性和学习时间之间进行权衡。