Nicolas Schweighofer

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Carolee J. WinsteinProfessor of Biokinesiology, University of Southern CaliforniaVerified email at usc.edu
Kenji DoyaOkinawa Institute of Science and TechnologyVerified email at oist.jp
Mitsuo KawatoATRVerified email at atr.jp
Michael ArbibUniversity of California at San DiegoVerified email at usc.edu
Cheol E. HanProfessor, Dept of Electronics and Information Engineering, Korea University, SejongVerified email at korea.ac.kr
James GordonProfessor of Biokinesiology & Physical TherapyVerified email at usc.edu
Sujin KimJeonju UniversityVerified email at jj.ac.kr
David ReinkensmeyerProfessor, Engineering and Neuroscience, University of California at IrvineVerified email at uci.edu
Youngmin OhGachon UniversityVerified email at gachon.ac.kr
Sungshin KimHanyang UniversityVerified email at hanyang.ac.kr
Denis MottetEuroMov Digital Health in Motion, Univ Montpellier IMT Mines Alès, Montpellier, FranceVerified email at umontpellier.fr
James FinleyAssociate Professor of Biokinesiology and Physical Therapy, University of Southern CaliforniaVerified email at usc.edu
Steven WolfEmory University School of MedicineVerified email at emory.edu
Eric J. LangAssociate Professor of Neuroscience & Physiology, New York University, School of MedicineVerified email at nyumc.org
Laffont IsabellePRM Department, Montpellier University Hospital, Euromov Digital Health in Motion, MontpellierVerified email at umontpellier.fr
Sook-Lei LiewUniversity of Southern CaliforniaVerified email at usc.edu
Leonardo G CohenHuman Cortical Physiology and Neurorehabilitation Section, National Institutes of Health, NIHVerified email at ninds.nih.gov
Daniel CallanChief Researcher, Brain Information Communication Research Laboratories, ATRVerified email at atr.jp
Victor R. BarradasTokyo Institute of TechnologyVerified email at m.titech.ac.jp
Peter Ford DomineyCNRS Research Director, INSERM U1093Verified email at inserm.fr

Nicolas Schweighofer

Professor, University of Southern California

Verified email at usc.edu - Homepage

Computational Neuroscience Neurorehabilitation Motor Control and Learning Statistical Learning


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Reward improves long-term retention of a motor memory through induction of offline memory gains M Abe, H Schambra, EM Wassermann, D Luckenbaugh, N Schweighofer, ... Current Biology 21 (7), 557-562, 2011	402	2011
Meta-learning in reinforcement learning N Schweighofer, K Doya Neural Networks 16 (1), 5-9, 2003	329	2003
Low-serotonin levels increase delayed reward discounting in humans N Schweighofer, M Bertin, K Shishida, Y Okamoto, SC Tanaka, ... Journal of Neuroscience 28 (17), 4528-4532, 2008	324	2008
Role of the cerebellum in reaching movements in humans. I. Distributed inverse dynamics control N Schweighofer, MA Arbib, M Kawato European Journal of Neuroscience 10 (1), 86-94, 1998	306	1998
Motor learning without doing: trial-by-trial improvement in motor performance during mental training R Gentili, CE Han, N Schweighofer, C Papaxanthis Journal of neurophysiology 104 (2), 774-783, 2010	257	2010
Dual adaptation supports a parallel architecture of motor memory JY Lee, N Schweighofer Journal of Neuroscience 29 (33), 10396-10404, 2009	231	2009
Computational neurorehabilitation: modeling plasticity and learning to predict recovery DJ Reinkensmeyer, E Burdet, M Casadio, JW Krakauer, G Kwakkel, ... Journal of neuroengineering and rehabilitation 13, 1-25, 2016	208	2016
Serotonin differentially regulates short-and long-term prediction of rewards in the ventral and dorsal striatum SC Tanaka, N Schweighofer, S Asahi, K Shishida, Y Okamoto, ... PloS one 2 (12), e1333, 2007	203	2007
Stroke rehabilitation reaches a threshold CE Han, MA Arbib, N Schweighofer PLoS computational biology 4 (8), e1000133, 2008	200	2008
Role of the cerebellum in reaching movements in humans. II. A neural model of the intermediate cerebellum N Schweighofer, J Spoelstra, MA Arbib, M Kawato European Journal of Neuroscience 10 (1), 95-105, 1998	193	1998
Cerebellar aminergic neuromodulation: towards a functional understanding N Schweighofer, K Doya, S Kuroda Brain Research Reviews 44 (2-3), 103-116, 2004	171	2004
Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control N Schweighofer, K Doya, F Lay Neuroscience 103 (1), 35-50, 2001	154	2001
Computational motor control in humans and robots S Schaal, N Schweighofer Current Opinion in Neurobiology 15 (6), 675-682, 2005	146	2005
Chaos may enhance information transmission in the inferior olive N Schweighofer, K Doya, H Fukai, JV Chiron, T Furukawa, M Kawato Proceedings of the National Academy of Sciences 101 (13), 4655-4660, 2004	143	2004
Exploration of signal transduction pathways in cerebellar long-term depression by kinetic simulation S Kuroda, N Schweighofer, M Kawato Journal of Neuroscience 21 (15), 5693-5702, 2001	140	2001
Electrophysiological properties of inferior olive neurons: a compartmental model N Schweighofer, K Doya, M Kawato Journal of neurophysiology 82 (2), 804-817, 1999	140	1999
A functional threshold for long-term use of hand and arm function can be determined: predictions from a computational model and supporting data from the Extremity Constraint … N Schweighofer, CE Han, SL Wolf, MA Arbib, CJ Winstein Physical therapy 89 (12), 1327-1336, 2009	137	2009
Task-oriented training to promote upper extremity recovery CJ Winstein, SL Wolf, N Schweighofer Stroke recovery and rehabilitation 2, 320-343, 2008	124	2008
The roles of the olivocerebellar pathway in motor learning and motor control. A consensus paper EJ Lang, R Apps, F Bengtsson, NL Cerminara, CI De Zeeuw, TJ Ebner, ... The Cerebellum 16 (1), 230-252, 2017	119	2017
System, apparatus and method for maximizing effectiveness and efficiency of learning, retaining and retrieving knowledge and skills G Ferriol, N Schweighofer, AS Lewis US Patent App. 10/012,521, 2003	118	2003

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