RESEARCHarXiv CS.AI·4/17/2026
Mistake gating leads to energy and memory efficient continual learning
This paper proposes 'memorized mistake-gated learning,' a biologically plausible plasticity rule where synaptic updates are strictly gated by current and past classification errors. This method reduces network updates by 50-80%, significantly enhancing energy and memory efficiency in continual and online learning scenarios.
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