Training artificial neural networks using context-dependent gating with weight stabilization

A computing device may receive a first set of training data for training an ANN to predict output data for a first task, and may train the ANN with the first set of training data by only adjusting values of weights associated with a first subset of neurons, the first subset selected based on an identity of the first task. The computing device may receive a second, different set of training data for training the ANN to predict output data for a second task, and may train the ANN with the second set of training data by only adjusting values of weights associated with a second subset of neurons, the second subset selected based on an identity of the second task. During training, adjusting of the value of any weight may entail weight stabilization that depends on whether there has been any training for one or more previous tasks.