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.