A computational model for spatial cognition combining dorsal and ventral hippocampal place field maps: multiscale navigation
A computational model for spatial cognition combining dorsal and ventral hippocampal place field maps: multiscale navigation
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DOI：
10.1007/s00422-019-00812-x
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发表时间：
2020-01
影响因子：
1.9
通讯作者：
Pablo Scleidorovich;Martín Llofriu;J. Fellous;A. Weitzenfeld
中科院分区：
文献类型：
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作者：
Pablo Scleidorovich;Martín Llofriu;J. Fellous;A. Weitzenfeld
经典的研究表明，位置细胞是根据它们的场大小沿着海马的背腹轴组织的，背侧海马位置细胞的场大小比腹侧位置细胞小。研究还表明，背侧位置细胞主要参与空间导航，而腹侧位置细胞主要参与上下文和情感编码。此外，最近的研究表明，海马体的整个纵轴可能参与导航。基于后者，本文提出了一个受海马背腹轴多尺度组织启发的空间认知强化学习模型。该模型分析了多尺度架构在学习速度、路径最优性和空间导航背景下的细胞数量方面可能带来的好处。该模型在目标导向的任务中进行评估，其中模拟大鼠需要在各种旷场迷宫配置中从多个起始位置学习到目标的路径。结果表明，较小的表示尺度有助于提高路径最优性，而较大的尺度有助于减少学习时间和所需的细胞数量。结果还表明，结合尺度可以提高多尺度模型的性能，在路径最优性和学习时间之间进行权衡。
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.