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New 'mistake-gating' learning algorithm cuts neural network updates by 50-80% while mimicking human error-correction biology
arXiv cs.AI · April 17, 2026
AI Summary
- •Researchers propose 'memorized mistake-gated learning,' a biologically plausible plasticity rule inspired by human negativity bias and error-related negativity
- •The method only updates synaptic weights when classification errors occur, rather than updating on every sample, reducing computational overhead significantly
- •Particularly effective for incremental learning (acquiring new knowledge while retaining existing knowledge) and online learning scenarios with memory constraints
- •Reduces storage buffer requirements for data replay, making the approach practical for continual learning tasks with limited energy and memory resources
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