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 & HonorsPositions
|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|
|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
- 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
- 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.
- Han, C.E., Arbib, M.A, and Schweighofer N. (2008) Stroke rehabilitation reaches a threshold, PLOS Computational Biology, 4(8): e1000133
- 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.
- Choi Y.G., Qi F., Gordon J.D, and Schweighofer N. (2008) Adaptive schedules enhance motor learning. Journal of Motor Behavior, 40: 273-280.
- 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.
- Bertin M., Schweighofer N., and Doya K. (2007) Multiple model-based reinforcement learning explains dopamine neuronal activity, Neural Networks, 20:668-675.
- 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”).
- Callan D. and Schweighofer N. (2007) Positive and negative modulation of word learning by reward anticipation, Human Brain Mapping, 28(5).
- 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.