  The performance of artificial neural networks (ANNs) degrades when training
data are limited or imbalanced. In contrast, the human brain can learn quickly
from just a few examples. Here, we investigated the role of sleep in improving
the performance of ANNs trained with limited data on the MNIST and Fashion
MNIST datasets. Sleep was implemented as an unsupervised phase with local
Hebbian type learning rules. We found a significant boost in accuracy after the
sleep phase for models trained with limited data in the range of 0.5-10% of
total MNIST or Fashion MNIST datasets. When more than 10% of the total data was
used, sleep alone had a slight negative impact on performance, but this was
remedied by fine-tuning on the original data. This study sheds light on a
potential synaptic weight dynamics strategy employed by the brain during sleep
to enhance memory performance when training data are limited or imbalanced.
