LAUSR

Significance Unlike humans, artificial neural networks rapidly forget previously learned information when learning something new and must be retrained by interleaving the new and old items; however, interleaving all old items is time-consuming and might be unnecessary. It might be sufficient to interleave only old items having substantial similarity to new ones. We show that training with similarity-weighted interleaving of old items with new ones allows deep networks to learn new items rapidly without forgetting, while using substantially less data. We hypothesize how similarity-weighted interleaving might be implemented in the brain using persistent excitability traces on recently active neurons and attractor dynamics. These findings may advance both neuroscience and machine learning.