States that resemble sleep-like cycles in simulated neural networks quell the instability that comes with uninterrupted self-learning in artificial analogs of brains
Watkins and her research team found that the network simulations became unstable after continuous periods of unsupervised learning. When they exposed the networks to states that are analogous to the waves that living brains experience during sleep, stability was restored. “It was as though we were giving the neural networks the equivalent of a good night’s rest,” said Watkins.
2020-06-08 James Riordon