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Nicolas Schweighofer

Dr. Nicolas Schweighofer, PhD, is Assistant Professor in the Division of Biokinesiology & Physical Therapy and holds joint appointments in Neuroscience and Computer Science at USC. He is Co-Investigator on the NIH-funded Computational Models of Stroke Neuro-Rehabilitation, and previously served as Principal Investigator of the NIH-funded grant Task Practice Schedules to Enhance Recovery after Stroke and Co-Principal Investigator on the NSF-funded grant Skill Acquisition through Interactive Avatars. Dr. Schweighofer was also awarded the Research award from the "Fond Medical pour la Recherche" Foundation in France.

Positions & Honors

Positions
2004-present Assistant Professor, Department of Biokinesiology and Physical Therapy, University of Southern California, Joint Appointments in Neuroscience and Computer Science
2002-2003 Researcher, Computational Neuroscience Group, ATR, Kyoto, Japan
2000-2001 Director of R&D, Cerego Inc., Tokyo, Japan
1996-1999 Researcher, Exploratory Research Advanced Technology Organization, ATR, Kyoto, Japan

Selected Honors & Awards
2004 Best paper award - Japanese Neural Network Society
2003 Research award from the "Fond Medical pour la Recherche" Foundation, France
2003 Invited lecturer at the Systems Neuroscience Spring School, Osaka, Japan
1999 Invited lecturer at Summer School on Neuromorphic Engineering, in Telluride, CO

Education

Human Information Science Group, ATR, Kyoto, Japan Postdoc 1996 Computational Neuroscience
University of Southern California Ph.D. 1995 Neurobiology
Ecole Nationale Superieure de Mecanique, Nantes, France M.S. 1991 Control System Engineering
"Mathematiques Speciales P," Lycee Descartes, Tours, France   1986 Advanced Mathematics and Physics

Current Research Support

NSF IIS, Schaal (PI), 1/1/06 - 12/31/09
Title: Skill acquisition through interactive avatars
We aim at creating a computer-based Interactive Avatar (IA) that can teach humans how to move. The IA will be able to demonstrate movements to its user, monitor the execution of these movements by the user, and suggest corrections in case of inadequate performance.
Role: Co-Principal Investigator

Selected Publications

  1. Choi Y.G., Gordon, J., Kim D., and Schweighofer N. (2009) An Adaptive Automated Robotic Task Practice System for Rehabilitation of Arm Functions after Stroke. IEEE-Transactions in Robotic, In Press
  2. Schweighofer N, Han, C.*, Wolf, Arbib, M.A and Winstein C. (2009) Understanding the Functional Threshold: Predictions from a Computational Model and Supporting Data from the Extremity Constraint-Induced Therapy Evaluation (EXCITE) Trial. Physical Therapy, Accepted pending revisions.
  3. Han, C.E., Arbib, M.A, and Schweighofer N. (2008) Stroke rehabilitation reaches a threshold, PLOS Computational Biology, 4(8): e1000133
  4. Schweighofer N., Bertin, M., Shihida K., Tanaka S., Okamoto Y., Yamawaki S., and Doya K (2008). Serotonin modulation of delayed reward discounting in humans, Journal of Neuroscience, 28:4528-32.
  5. Choi Y.G., Qi F., Gordon J.D, and Schweighofer N. (2008) Adaptive schedules enhance motor learning. Journal of Motor Behavior, 40: 273-280.
  6. Tanaka S., Schweighofer N., Asahi S., Okamoto Y., Yamawaki S., and Doya K. (2007) Serotonin differentially regulates reward prediction in the striatum at short and long time scales, PLOS ONE, 2(12):e1333.
  7. Bertin M., Schweighofer N., and Doya K. (2007) Multiple model-based reinforcement learning explains dopamine neuronal activity, Neural Networks, 20:668-675.
  8. Schweighofer N., Tanaka S., and Doya K. (2007). Serotonin and the Evaluation of Future Rewards: Theory, Experiments, and Possible Neural Mechanisms. Annals of the New York Academy of Science, 1104:289-300 (special issue on “Reward and Decision Making in Cortico-basal Ganglia Networks”).
  9. Callan D. and Schweighofer N. (2007) Positive and negative modulation of word learning by reward anticipation, Human Brain Mapping, 28(5).
  10. Schweighofer N., Shihida K., Tanaka S., Okamoto Y., Yamawaki S., and Doya K. (2006). Humans can adopt optimal discounting strategies under real time constraints. PLoS Computational Biology, 2:1349-1356.