Nicolas Schweighofer, PhD
Assistant Professor
Dept. Biokinesiology & Physical Therapy

 
Phone: (323) 442 - 1838   
Email: schweigh(at)usc.edu  
Office: CHP-147C 
Dept. Mailing Address: 1540 Alcazar St. CHP 155 Los Angeles, CA 90089-9006

Education:
Post-doc, Human information science Group, ATR, Kyoto Japan
PhD, University of Southern California, Los Angeles.
M.S., Ecole Nationale Superieure de Mecanique, Nantes, France
"Mathematiques Speciales P' " , Lycee Descartes, Tours, France

Postdoctoral Research Fellowship:


Started at USC:

Research Topics:

Research Description

Nicolas Schweighofer has conducted computational studies addressing the way in which the cerebellum complements the roles of the basal ganglia and the motor cortex in eye and arm movements. He then paid particular attention to the processes by which error signals reaching the cerebellum could be useful for efficient motor learning. He has also studied how learning rules provide the bridge between system behavior and the neurochemistry of synaptic and cellular change, with a special emphasis on the role of neuromodulators. His current projects include: 1) developing a neuro-computational theory of sustained motivation to learn motor skills, 2) studying the roles of neuromodulation in synaptic plasticity in health and disease, and 3) developing optimal schedules of motor skill learning in stroke patients.



Role in ISNSR:


Selected Publications

Schweighofer N., Doya K., and Kuroda S. (2004) Cerebellar aminergic neuromodulation: towards a functional understanding . Brain Research Reviews 44: 103-106

Schweighofer N., Doya K., Chiron J.V., Fukai H., Furukawa T., and Kawato M. (2004) Chaotic resonance induced by electrical coupling enhance information transmission in a model of inferior olive neurons. Proc. Natl. Acad. Sci. USA, 101: 4655-4660

Schweighofer N. and Doya K. (2003) Meta-learning in reinforcement learning. Neural Networks, 16:5-9

Kuroda S., Schweighofer N., and Kawato M. (2001) Exploration and prediction of signal transduction pathways in cerebellar long-term depression by kinetic simulation. Journal of Neuroscience 21:5693-702

Schweighofer N., Doya K., and Lay F. (2001) Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control. Neuroscience. 103:35-50




The Center is funded as part of the National Institutes of Health Roadmap Initiative, grant number P20 RR20700-01. NIH Program Administrator: Dr. Greg Faber