A team at Stanford University developed an algorithm called ReFit that significantly improved the speed and accuracy of neural prostheses operated by rhesus monkeys to control computer cursors. Unlike earlier versions, ReFIT analyzed and implemented visual feedback gathered in real time, interpreted neural signals about the cursor's position and velocity at the same time, and performance did not degrade for as long as four years. "These findings could lead to greatly improved prosthetic system performance and robustness in paralyzed people," said Stanford professor Krishna Shenoy, who wrote the paper published in the journal Nature Neuroscience.
ReFIT algorithm takes neural prosthetics to a new level
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