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Updated: Jun 15, 2025

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
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深い継続的な学習における可塑性の喪失

Shibhansh Dohare1, J Fernando Hernandez-Garcia2, Qingfeng Lan2

  • 1Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. dohare@ualberta.ca.

Nature
|August 21, 2024
PubMed
まとめ

標準的なディープラーニングは 継続的な学習環境では失敗し 時間が経つにつれて可塑性を失います ランダムな多様性を注入することで,新しい継続的な逆伝播アルゴリズムは可塑性を維持し,グラデント降下だけでは持続的なディープラーニングには不十分であることを示唆しています.

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