Humans regularly perform new learning without losing memory for previous information, but neural network models suffer from the phenomenon of catastrophic forgetting in which new learning impairs prior function. A recent article presents an algorithm that spares learning at synapses important for previously learned function, reducing catastrophic forgetting.