Nicolas Schweighofer | WRHI – Tokyo Tech World Research Hub Initiative

経歴
受賞歴
主な論文

2019-present	Specially Appointed Associate Professor, Institute of Innovative Research, Tokyo Institute of Technology
2011-Present	Associate Professor (with Tenure), Division of Biokinesiology & Physical Therapy, University of Southern California
2011-Present	Joint Appointment (courtesy), Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California
2004-Present	Joint Appointment (courtesy), Department of Computer Science, Viterbi School of Engineering, University of Southern California
2004-Present	Joint Appointment (courtesy), Neuroscience Graduate Program, University of Southern California
2004-2010	Assistant Professor (Tenure track), Division of Biokinesiology & Physical Therapy, University of Southern California
2002-2003	Researcher, Computational Neuroscience Group, CREST, Kyoto, Japan
2000-2001	Director of R&D, Cerego Inc., Tokyo, Japan
1997-1999	Researcher, Exploratory Research Advanced Technology Organization, Kyoto, Japan

2014-2016	Awards to organize European Computational Motor Control Summer School from Labex NUMEV, Montpellier, France
2004	Best Paper Award, Japanese Neural Network Society

2017	Park H. and Schweighofer N. (2017) Nonlinear mixed-effects model reveals a distinction between learning and performance in intensive reach training post-stroke. Journal of NeuroEngineering and Rehabilitation 14 (1), 21
2017	Bakhti, K. K. A., Mottet, D., Schweighofer, N., Froger, J., & Laffont, I. (2017). Proximal arm non-use when reaching after a stroke. Neuroscience Letters, 657, 91-96
2016	Wang, C., Xiao Y, Burdet E., Gordon, J., and Schweighofer N. (2016). The duration of reaching movement is longer than predicted by minimum variance, Journal of Neurophysiology. 116 (5), 2342-2345
2016	Reinkensmeyer D. J., Burdet E., Casadio M., Krakauer J.W., Kwakkel G., Lang C. E., Swinnen S., Ward N., and Schweighofer, N. (2016) Computational neurorehabilitation: Modeling plasticity and learning to predict recovery, Journal of Neuroengineering and Rehabilitation, 13, 1
2016	Lee J-Y, Oh Y., Scheidt R., and Schweighofer N . (2016) Optimal Schedules in Multitask Motor Learning, Neural Computation, 28, 667–685
2016	Park H. , Kim S. , Winstein C., Gordon J., and Schweighofer N. (2016) Short-Duration and Intensive Training Improves Long-Term Reaching Performance in Individuals with Chronic Stroke , Neural Rehabilitation and Repair, 30, 551-561
2015	Kim, S.S., Ogawa K., Lv. J., Schweighofer N . and Imamizu H.(2015) Neural Substrates Related to Motor Memory with Multiple Timescales in Motor Adaptation PLoS Biology, 13(12): e1002312. doi:10.1371/journal.pbio.1002312. (NS: corresponding author)
2015	Kim S.S, Oh Y, Schweighofer N . (2015) Between-Trial Forgetting Due to Interference and Time in Motor Adaptation. PLoS ONE 10(11): e0142963. doi:10.1371/journal.pone.0142963
2015	Gueugneau N., Schweighofer N., and Papaxanthis C., (2015) Daily update of motor predictions by physical activity. Scientific reports, 5, doi:10.1038/srep17933
2015	Schweighofer N., Xiao Y., Gordon, J., and Osu R. (2015). Effort, Success, and Non-use Determine Arm Choice Journal of Neurophysiology, 2015 Jul;114(1):551-9.