Separating the 'what' and 'how' of compositional computation to enable reuse and continual learning

The ability to continually learn new skills, retain, and flexibly deploy them to accomplish goals is a key feature of intelligent and efficient behavior. However, the neural mechanisms facilitating the continual learning and flexible (re-)composition of skills remain elusive. Here, we study continual learning and the compositional reuse of learned computations in recurrent neural network (RNN) models using a novel two-system approach: one system that infers'what' computation to perform, and one that implements'how' to perform it. We focus on a set of compositional cognitive tasks commonly studied in neuroscience. To construct the'what' system, we first show that a large family of tasks can be systematically described by a probabilistic generative model, where compositionality stems from a shared underlying vocabulary of discrete task-epochs.