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ランダムなネットワークのフィードバック制御は,タスク間の柔軟なモーター皮質ダイナミクスのモデルとして,タスクの間で機能します
Hari Teja Kalidindi1, Frédéric Crevecoeur2
1Institute of Communication Technologies, Engineering and Applied Mathematics (ICTEAM), UCLouvain, Avenue Georges Lemaitre 4-6, 1348 Louvain-la-Neuve, Belgium; Institute of Neuroscience, UCLouvain, Avenue E. Mounier 53, 1200 Brussels, Belgium; Donders Centre for Cognition, Radboud University, Thomas Van Aquinostraat 4, 6525D Nijmegen, the Netherlands.
Cell reports
|February 14, 2026
まとめ
神経ネットワークモデルは,到達する際の低次元軌道のような運動皮質の活動が,生体力学と結合したランダムなネットワークからどのように生じるかを明らかにします. このフレームワークは,柔軟な運動制御を説明し,神経ダイナミクスを感覚運動のタスクとリンクします.
科学分野:
- 計算神経科学とは
- システム神経科学 システム神経科学
- モーター・コントロール・コントロール
背景:
- 伸縮中のモーター皮質の活動は,しばしば低次元軌道を示し,モーターコマンドのニューラルベースを示唆します.
- これらの神経パターンの起源と,さまざまなタスクにおける柔軟な再編成は,まだ十分に理解されていない.
研究 の 目的:
- 基本的原則から,運動皮質活動の特徴的な特徴がどのように生じるかを調査する.
- 神経ダイナミクスをタスク全体における感覚運動制御と結びつける解釈可能な枠組みを開発する.
主な方法:
- ランダムなニューラルネットワークの線形モデルをバイオメカニカルシステムと組み合わせた.
- タスク依存の感覚フィードバックマッピングを通じて複数の行動を達成するために最適な制御原理を適用します.
- バイオメカニカルフィードバックと低次元ネットワークダイナミクスとの関係を分析的に確立しました.
主要な成果:
- 低次元の軌跡と運動皮質の活動における回転動態は,モデルから自然に発生した.
- このモデルは,実験的な録音と一致する豊富なモーターレパートリーを生み出しました.
- タスク依存フィードバックマッピングは,神経活動の柔軟な再編成を説明した.
結論:
- バイオメカニクスと組み合わせた単純な線形モデルは,複雑なモーター皮質のダイナミクスを説明することができます.
- 感覚フィードバックによる最適な制御は,柔軟な感覚運動適応のためのメカニズムを提供します.
- 分析的フレームワークは,モーターコマンドの生成と制御のニューラル基盤の洞察を提供します.
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