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About

Tyler Cluff’s research sits at the intersection of motor control, computational neuroscience, and neurorehabilitation, with a focus on how the nervous system learns and adapts movement after stroke. His most cited work (102 citations) demonstrated that feedforward and feedback control of reaching share an internal model of the arm’s dynamics, revealing how the brain integrates predictive and reactive mechanisms for skilled movement. This foundational insight has shaped our understanding of motor learning in both health and disease. Cluff has made significant contributions to characterizing sensorimotor impairments post-stroke, particularly in proprioception and visuomotor adaptation. His 2022 paper (37 citations) assessed how stroke affects the ability to adapt movements using visual feedback, while his 2023 work (22 citations) pioneered the use of machine learning and deep learning to objectively quantify proprioceptive deficits from robotic kinematic data—a methodological advance that moves beyond traditional clinical scales. More recently, his 2024 study (12 citations) established that proprioceptive impairments and visuomotor adaptation deficits are independent post-stroke, with important implications for personalized rehabilitation. Cluff’s work has garnered over 190 citations, with key papers published in high-impact venues like the *Journal of Neurophysiology* and *Neurorehabilitation and Neural Repair*. His research is notable for bridging basic motor control theory with clinically actionable insights, offering new tools and frameworks for assessing and treating movement disorders after neurological injury.