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Miguel Nicolelis, M.D., Ph.D.

Photo of Miguel Nicolelis

Phone: (919) 684-4580

Department of Neurobiology
Box 3209
Duke University Medical Center
327E Bryan Research Building
Research Drive, Durham, NC 27710

Email: nicoleli AT neuro DOT duke DOT edu

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Duke School of Medicine Professor in Neuroscience; Co-Director, Center for Neuroengineering

Neurobiology, School of Medicine

DIBS Faculty

Research Description

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.

Education

Postdoctoral Fellow, Hahnemann University, Physiology and Biophysics, 1989-1992

Ph.D., University of Sao Paulo (Brazil), Physiology, Institute of Biomedical Science, 1989

M.D., University of Sao Paulo Medical School (Brazil), 1984

Recent Publications

Hanson T, Omarsson B, O'Doherty J, Peikon I, Lebedev M, Nicolelis M. High-side digitally current controlled biphasic bipolar microstimulator. IEEE Trans Neural Syst Rehabil Eng. 2012 Feb 7. [Epub ahead of print]

O'Doherty JE, Lebedev MA, Ifft PJ, Zhuang KZ, Shokur S, Bleuler H, Nicolelis MAL. Active tactile exploration enabled by a brain-machine-brain interface. Nature doi:10.1038/nature10489, 2011.

Li, Z, O’Doherty, JE, Lebedev, MA, Nicolelis, MAL. Adaptive decoding for brain-machine interfaces through Bayesian parameter updates. Neural Comput. 23: 1–43, 2011.

Oliveira-Maia AJ, Roberts C, Walker QD, Luo B, Kuhn C, Simon SA, Nicolelis MA. Intravascular Food Reward. PLoS One 6(9): e24992. doi:10.1371/journal.pone.0024992, 2011.

Research Areas

Research Topics

  • Neuroengineering
  • Multi-electrode recording
  • brain-machine interface