arXiv cs.AI
· Papers
Mistake gating leads to energy and memory efficient continual learning
arXiv:2604.14336v2 Announce Type: replace Abstract: Synaptic plasticity is metabolically expensive, yet animals continuously update their internal models without exhausting energy reserves. However, when artificial neural networks are trained, the network parameters are typically updated on every sample that is present