  Reservoir computing is a powerful tool to explain how the brain learns
temporal sequences, such as movements, but existing learning schemes are either
biologically implausible or too inefficient to explain animal performance. We
show that a network can learn complicated sequences with a reward-modulated
Hebbian learning rule if the network of reservoir neurons is combined with a
second network that serves as a dynamic working memory and provides a
spatio-temporal backbone signal to the reservoir. In combination with the
working memory, reward-modulated Hebbian learning of the readout neurons
performs as well as FORCE learning, but with the advantage of a biologically
plausible interpretation of both the learning rule and the learning paradigm.
