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Biography |
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Dr. Miguel Nicolelis is the Anne W. Deane Professor of Neuroscience and Professor of Neurobiology, Biomedical Engineering and Psychology at Duke University. He is also Co-Director of Duke Center for Neuroengineering; and Co-Founder and Scientific Director of the Edmond and Lily Safra International Institute for Neuroscience of Natal. Dr. Nicolelis is a native of Sao Paulo, Brazil where he received his M.D. and Ph.D. in Neurophysiology from the University of Sao Paulo. Although for the past decade, Dr. Nicolelis is best known for his pioneering studies of Brain Machine Interfaces (BMI) and Neuroprosthetics in human patients and non-human primates, he has also developed an integrative approach to studying neurological and psychiatric disorders including Parkinson’s disease, Epilepsy, Schizophrenia and Attention Deficit Disorder. He has also made fundamental contributions in the fields of sensory plasticity, gustation, sleep, reward and learning. Dr. Nicolelis believes that this approach will allow the integration of molecular, cellular, systems, and behavioral data in the same animal, producing a more complete understanding of the nature of the neurophysiological alterations associated with these disorders.
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Abstract |
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Principles of Neural Ensemble Physiology
In this talk, I will review a series of recent experiments demonstrating the possibility of using real-time computational models to investigate how ensembles of neurons encode motor information. These experiments have revealed that brain-machine interfaces can be used not only to study fundamental aspects of neural ensemble physiology, but they can also serve as an experimental paradigm aimed at testing the design of modern neuroprosthetic devices. I will also describe evidence indicating that continuous operation of a closed-loop brain machine interface, which utilizes a robotic arm as its main actuator, can induce significant changes in the physiological properties of neurons located in multiple motor and sensory cortical areas. This raises the hypothesis of whether the properties of a robot arm, or any other tool, can be assimilated by neuronal representations as if they were simple extensions of the subject's own body. |
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