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Many recent studies of the motor system are divided into two distinctapproaches: Those that investigate how motor responses are encoded in corticalneurons' firing rate dynamics and those that study the learning rules by whichmammals and songbirds develop reliable motor responses. Computationally, thefirst approach is encapsulated by reservoir computing models, which can learnintricate motor tasks and produce internal dynamics strikingly similar to thoseof motor cortical neurons, but rely on biologically unrealistic learning rules.The more realistic learning rules developed by the second approach are oftenderived for simplified, discrete tasks in contrast to the intricate dynamicsthat characterize real motor responses. We bridge these two approaches todevelop a biologically realistic learning rule for reservoir computing. Ouralgorithm learns simulated motor tasks on which previous reservoir computingalgorithms fail, and reproduces experimental findings including those thatrelate motor learning to Parkinson's disease and its treatment.

A model of reward-modulated motor learning with parallelcortical and basal ganglia pathways