A Computational Study of Robotic Therapy for Stroke Rehabilitation Based on Population Coding
We evaluated the efficiency of robotic therapy for stroke survivors by using a computational approach in motor theory with a stroke rehabilitation model. In computational neuroscience, hand movement can be represented by population coding of neuronal preferred directions (PDs) in the motor cortex. We modeled the recovery processes of arm movement in conventional and robotic therapies as reoptimization of PDs in different learning rules, and compared the efficiencies after stroke. Conventional therapy did not induce complete recovery of stroke lesions, and the neuronal state depended on the training direction. However, robotic therapy reoptimized the PDs uniformly regardless of the training direction. These observations suggest that robotic therapy may be effective for recovery and not have a negative effect on motor performance depending the training direction. Furthermore, this study provides computational evidence to promote robotic therapy for stroke rehabilitation.
KeywordsMotor learning Motor cortex Preferred direction Reinforcement learning Reaching
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