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Dadarlat MC, O'Doherty JE, Sabes PN (2015) A learning-based approach to artificial sensory feedback leads to optimal integration. Nature Neuroscience, 18(1):138-144    
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In this study, the authors show that monkeys can learn to use non-biomimetic proprioceptive feedback, delivered via electrical microstimulation of somatosensory cortex, to guide motor movements. The monkeys also integrated this artificial feedback with vision to optimize motor performance. The results suggest new learning-based approaches both to providing sensory feedback for brain–machine interfaces and to studying the neural mechanisms of adaptive sensory integration
Abstract
Proprioception—the sense of the body's position in space—is important to natural movement planning and execution and will likewise be necessary for successful motor prostheses and brain–machine interfaces (BMIs). Here we demonstrate that monkeys were able to learn to use an initially unfamiliar multichannel intracortical microstimulation signal, which provided continuous information about hand position relative to an unseen target, to complete accurate reaches. Furthermore, monkeys combined this artificial signal with vision to form an optimal, minimum-variance estimate of relative hand position. These results demonstrate that a learning-based approach can be used to provide a rich artificial sensory feedback signal, suggesting a new strategy for restoring proprioception to patients using BMIs, as well as a powerful new tool for studying the adaptive mechanisms of sensory integration