Understanding the functionalities of the motor cortex of the human brain has been a major focus of research in the field of neuroscience. While neuroscientific research regarding the motor cortex has shown evidence of topological preservation and self organization of motor output signals, models presented in preliminary works fail to depict such aspect and its integration into trial-and- error learning. Our research proposes a model which attempts to tackle such problem by utilizing Kohonen’s self organizing map and a reinforcement learning framework.
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