Artificial neural networks are prone to a troublesome glitch known, evocatively, as catastrophic forgetting. These seemingly tireless networks can keep learning tasks day and night. But sometimes, once a new task is learned, any recollection of an old task vanishes. It's as if you learned to play tennis decently well, but after being taught to play water polo, you suddenly had no recollection of how to swing a racket. This apparent network overload put an idea in the head of Maxim Bazhenov, a professor who studies computational neuroscience and sleep at the University of California San Diego School of Medicine. Perhaps the spiking neural networks he was working with simply needed a rest.