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For Miguel Nicolelis, a professor of neuroscience at Duke University Medical Center, the backbone of mind-machine interfaces is the ability to analyze neural activity. Sure, the system demonstrated at Pitt in May accessed information from 100 neurons at once. But Nicolelis’s lab has managed five times that amount, with data coming from up to 10 different brain structures. "We're able to look at brain dynamics on a scale that no one else has been able to," he says. "You're transferring information into motion. When more neurons are recorded, it allows you to extract many more parameters from the brain, to look for more elaborate output." The result is more fine-tuned movement for devices-and more data recorded from a given subject-to help researchers analyze the relationship between brain signals and physical activity.