The ability to improve in speed and accuracy as a result of repeating some task is an important hallmark of intelligent biological systems. We model the progression from a counting-based strategy for addition to a recall-based strategy. The model consists of two networks working in parallel: a slower basal ganglia loop, and a faster cortical network. The slow network methodically computes the count from one digit given another, corresponding to the addition of two digits, while the fast network gradually "memorizes" the output from the slow network. The faster network eventually learns how to add the same digits that initially drove the behaviour of the slower network. Performance of this model is demonstrated by simulating a fully spiking neural network that includes basal ganglia, thalamus and various cortical areas.