Miguel Nicolelis, M.D., Ph.D., is the Duke School of Medicine Professor of Neuroscience at Duke University, Professor of Neurobiology, Biomedical Engineering and Psychology and Neuroscience, and founder of Duke’s Center for Neuroengineering. He is also Founder and Scientific Director of the Edmond and Lily Safra International Institute for Neuroscience of Natal. While Dr. Nicolelis is best known for his achievements in developing 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. 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. Dr. Nicolelis is a member of the French and Brazilian Academies of Science and has authored nearly 200 manuscripts, edited numerous books and special journal publications, and holds three US patents.
The title of his talk will be: “Computing with Brain Circuits“. In this talk, he will describe how state-of-the-art research on brain-machine interfaces make it possible for the brains of primates to interact directly and in a bi-directional way with mechanical, computational and virtual devices without any interference of the body muscles or sensory organs.
He will review a series of recent experiments 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 novel neuroprosthetic devices. He 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 neural circuits in multiple motor and sensory cortical areas. This research raises the hypothesis that the properties of a robot arm, or other neurally controlled tools, can be assimilated by brain representations as if they were extensions of the subject’s own body.