  Many recent studies of the motor system are divided into two distinct
approaches: Those that investigate how motor responses are encoded in cortical
neurons' firing rate dynamics and those that study the learning rules by which
mammals and songbirds develop reliable motor responses. Computationally, the
first approach is encapsulated by reservoir computing models, which can learn
intricate motor tasks and produce internal dynamics strikingly similar to those
of motor cortical neurons, but rely on biologically unrealistic learning rules.
The more realistic learning rules developed by the second approach are often
derived for simplified, discrete tasks in contrast to the intricate dynamics
that characterize real motor responses. We bridge these two approaches to
develop a biologically realistic learning rule for reservoir computing. Our
algorithm learns simulated motor tasks on which previous reservoir computing
algorithms fail, and reproduces experimental findings including those that
relate motor learning to Parkinson's disease and its treatment.
