Neural processing for network representation of spatial map

2023 Fiscal Year Final Research Report

• Project/Area Number: 19K16292
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 46030:Function of nervous system-related
• Research Institution: Kyoto University
• Principal Investigator: Suzuki Yusuke 京都大学, 生命科学研究科, 助教 (90723669)
• Project Period (FY): 2019-04-01 – 2024-03-31
• Keywords: 空間表象 / ネットワーク

Outline of Final Research Achievements

Representing places as a network is empirically known to make spatial navigation efficient. However, none of the standard experimental paradigms to test this has not been established. In this study, we report that the network structure of spatial exploration in the Barnes maze, a spatial learning paradigm for mice, is modulated by scale space. In the Barnes maze, each mouse generates a network where exploration points are nodes and transitions between them are links. In a 1-meter diameter Barnes maze, betweenness centrality of the network decreases with learning, whereas it remains constant in a larger 3-meter diameter Barnes maze. This pattern is maintained even in post-learning spatial exploration behavior.

Free Research Field

心理学

Academic Significance and Societal Importance of the Research Achievements

場所をネットワークによって表象することは，場所同士の接続関係を維持したまま，場所の持つ物理的・幾何学的な特徴を要約することであり，これによって所望の場所への経路検索を容易にする．場所のネットワーク表象を探る研究によって，従来の認知地図モデルのアップデートが期待される．
本研究は，ネットワーク表象が空間スケールによって異なることを示唆した．これらの結果は，生物が長い距離や広大な空間の中で，どのようにナビゲーションを可能にしているかについての洞察を与える．