Nicolas Schweighofer

情報・人工知能研究

Nicolas Schweighofer

特任准教授

計算論的神経科学

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.