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The brain has an incredible capacity to learn how to control various effectors, ranging from those endogenous the body to those that are artificially implanted. Moreover, changes to these effectors can be readily adapted by the brain, albeit on varying timescales depending on the amount of perturbation. For example, many studies have shown adaptation to force perturbations or visual-motor rotations to occur within minutes, attributing the adaptation to inputs from the cerebellum. This flexible adaptation of the brain makes the use of brain-machine interfaces (BMIs) an attractive rehabilitative option for a variety of motor pathologies such as stroke and amyotrophic lateral sclerosis. BMIs allow users to interact with their environments by using signals recorded directly from the brain and transforming them into actions taken on by an external effector such as a computer cursor or robotic arm. Past work has shown neurons to adapt to decoders over time, suggesting BMIs as an exciting paradigm for neural rehabilitation. Modern decoding methods involve using closed-loop decoder adaptation (CLDA) allowing for decoder weights to converge quickly, and users to gain high levels of control with very little training. Furthermore, these weights can be updated over learning in the event there are changes in the neural population. While great for assistive devices, it is unclear how these decoders impact neural adaptation over time. To answer this question, we conducted four studies examining the interplay between decoder adaptation and changes in neural activity over learning. Previous studies in the field have shown neural activity to fire in increasingly correlated patterns, converging onto low-dimensional spaces as performance with a BMI improves, indicating consolidation of coordinated firing patterns. However, these studies were conducted with static decoders. To examine how neurons adapt alongside CLDA, we used nonhuman primate models to observe how neurons in motor corte

The brain has an incredible capacity to learn how to control various effectors, ranging from those endogenous the body to those that are artificially implanted. Moreover, changes to these effectors can be readily adapted by the brain, albeit on varying timescales depending on the amount of perturbation. For example, many studies have shown adaptation to force perturbations or visual-motor rotations to occur within minutes, attributing the adaptation to inputs from the cerebellum. This flexible adaptation of the brain makes the use of brain-machine interfaces (BMIs) an attractive rehabilitative option for a variety of motor pathologies such as stroke and amyotrophic lateral sclerosis. BMIs allow users to interact with their environments by using signals recorded directly from the brain and transforming them into actions taken on by an external effector such as a computer cursor or robotic arm. Past work has shown neurons to adapt to decoders over time, suggesting BMIs as an exciting paradigm for neural rehabilitation. Modern decoding methods involve using closed-loop decoder adaptation (CLDA) allowing for decoder weights to converge quickly, and users to gain high levels of control with very little training. Furthermore, these weights can be updated over learning in the event there are changes in the neural population. While great for assistive devices, it is unclear how these decoders impact neural adaptation over time. To answer this question, we conducted four studies examining the interplay between decoder adaptation and changes in neural activity over learning. Previous studies in the field have shown neural activity to fire in increasingly correlated patterns, converging onto low-dimensional spaces as performance with a BMI improves, indicating consolidation of coordinated firing patterns. However, these studies were conducted with static decoders. To examine how neurons adapt alongside CLDA, we used nonhuman primate models to observe how neurons in motor corte