格子細胞における集団活動のトロイド形トポロジー
Richard J Gardner1, Erik Hermansen2, Marius Pachitariu3
1Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway. richard.gardner@ntnu.no.
Nature
|January 13, 2022
まとめ
脳のグリッド細胞は 六角形の発射パターンを用いて 位置をマップします 新しい研究により ネットワーク活動が トラスを形成し 空間ナビゲーションの 連続アトラクターネットワークモデルを 支えていることが明らかになりました
科学分野:
- 神経科学
- 計算神経科学
背景:
- 空間ナビゲーションには 中間内皮質 (MEC) が不可欠です
- MEC内のグリッド細胞は,六角的な発火パターンを示し,アロセントリック位置のための集団コードを形成します.
- 継続的なアトラクターネットワーク (CAN) は,観察された環境および行動状態の不変性により,グリッド細胞の活動が根底にあると仮定されています.
研究 の 目的:
- ネットワークセルのネットワークダイナミクスと連続アトラクターネットワーク (CAN) モデルとの関係を調査する.
- グリッド・セル・ネットワークが環境インプットを処理し,空間表現を維持する方法を明確にします.
主な方法:
- 何百ものグリッド・セルからの 記録です
- 神経活動のトポロジカルデータ分析
- 異なる環境と行動状態 (覚醒と睡眠) におけるグリッド細胞活動の分析.
主要な成果:
- モジュール内のグリッドセルの共同活動は,二次元CANと一致するトロイドパネル上に存在することが示されました.
- トロイドマニフォールドの位置は 周囲の環境における動物の位置に 直接対応しています
- 個々のグリッド・セルが 特定の場所での特異的な活動を示した.
- この形状の組織は 異なる環境と 覚醒と睡眠の間に維持されました
結論:
- この研究は,グリッドセルにおける継続的なアトラクターネットワークのダイナミクスの強力な証拠を提供します.
- トロイド多様体の可視化により,グリッドセルネットワークの機能が人口レベルで理解できます.
- 発見は,グリッドセルネットワークの組織と空間的なコーディングのためのフィードフォワードモデルよりも内在的なCANモデルをサポートします.
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