Probabilistic metaplasticity for continual learning with memristors in spiking networks

Abstract Edge devices operating in dynamic environments critically need the ability to continually learn without catastrophic forgetting. The strict resource constraints in these devices pose a major challenge to achieve this, as continual learning entails memory and computational overhead. Crossbar...

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Autors principals:	Fatima Tuz Zohora, Vedant Karia, Nicholas Soures, Dhireesha Kudithipudi
Format:	Artigo
Idioma:	Inglês
Publicat:	Nature Portfolio 2024-11-01
Col·lecció:	Scientific Reports
Matèries:	Metaplasticity Continual learning Memristor Spiking neural network
Accés en línia:	https://doi.org/10.1038/s41598-024-78290-w
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