A model of reward-modulated motor learning with parallelcortical and basal ganglia pathways
This work addresses the problem of integrating realistic biological learning with complex motor control for researchers in computational neuroscience and motor learning, representing an incremental advance by bridging two existing approaches.
The paper tackled the disconnect between biologically realistic learning rules and complex motor tasks by developing a new learning rule for reservoir computing that successfully learns simulated motor tasks where previous methods failed, and reproduces experimental findings related to Parkinson's disease.
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.