Brain-machine interfaces (BMIs) hold great potential to aid large numbers of people with neurological disorders. BMIs also provide a framework for studying cortical dynamics and the neural correlates of learning neuroprosthetic skills, i.e. accurate, readily-recalled control of disembodied actuators irrespective of natural physical movements. In this talk I will postulate that achieving skillful, natural control of a multi-DOF prosthetic device will entail synergizing two different types of adaptation processes: natural (brain plasticity) and artificial (decoder adaptation), as well as providing realistic sensory feedback from the prosthetic device. I will present recent work from our laboratory showing that 1) neuroplasticity facilitates consolidation of neuroprosthetic motor skill in a way that resembles that of natural motor learning; 2) corticostriatal plasticity is necessary for neuroprosthetic skill learning, and 3) closed-loop decoder adaptation (CLDA) techniques can expedite the learning process by adapting the decoder parameters during closed-loop BMI operation (i.e., while the subject is using the BMI). We believe that BMI systems capable of exploiting both neuroplasticity and CLDA will be able to boost learning, generalize well to novel movements and environments, and ultimately achieve a level of control and dexterity comparable to that of natural arm movements.
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