Wang, C.,* D’Argenio D., Winstein, C., Schweighofer, N. (2020) The Efficiency, Efficacy, and Retention of Task Practice in Chronic Stroke. Neurorehabilitation and Neural Repair. doi: 10.1177/1545968320948609

Varghese R., Kutch J., Schweighofer, N and Winstein C.J. (2020) The Probability of Choosing Both Hands Depends on an Interaction Between Motor Capacity and Limb-Specific Control in Chronic Stroke In Press, Experimental Brain Research  doi: 10.1007/s00221-020-05909-5

Hoang, H., Lang, E.J., Hirata, Y., Tokuda, I.T., Aihara, K., Toyama, K., Kawato, M. and Schweighofer, N., (2020). Electrical coupling controls dimensionality and chaotic firing of inferior olive neuronsPLOS Computational Biology, 16(7), p.e1008075.

Barradas V.,* Kutch J., Kawase T., Koike Y., Schweighofer N.  (2020) When 90% is not enough: impact of discarded muscle synergies on motor controlJournal of Neurophysiology, 123, 2180-2190

Liew, S.LZavaliangos-Petropulu A., … Schweighofer N., … Steven C. Cramer S., & Thompson P.M. (2020) The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after strokeHuman Brain Mapping, 2020, 1–20


Oh Y*, and Schweighofer N. (2019) Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor AdaptationJournal of Neuroscience, 39 (46), 9237-9250

Kim A., Schweighofer N., Finley J.M(2019) Locomotor skill acquisition in virtual reality shows sustained transfer to the real worldJournal of Neuroengineering and Rehabilitation, 16(1), 1-10.

Winstein C., Kim B., Kim, S., Martinez C., and Schweighofer N.  (2019) Dosage Matters: A Phase IIb Randomized Controlled Trial of Motor Therapy in the Chronic Phase after Stroke, Stroke, 50(7):1831-1837. doi: 10.1161/STROKEAHA

Lefebvre S., Jann K, Schmiesing A., Ito K., Jog M., Qiao Y., Cabeen R., Shi Y., Schweighofer N., Wang D.J., Liew S.L(2019) Differences in high-definition transcranial direct current stimulation over the motor hotspot versus the premotor cortex on motor network excitabilityScientific Reports, 9, 17605,


Lee K.*, Oh Y.*, Izawa J. #, Schweighofer N.# (2018) Sensory prediction errors, not performance errors, update memories in visuomotor adaptationScientific reports, 8: 16483

Kim S.*, Park H.*, Winstein C., and Schweighofer N. (2018) Measuring habitual arm use post-strokeFrontiers in Neurology, 9, 883

Schweighofer, N., Wang, C., Mottet, D.; Laffont I., Bakhti K., Reinkensmeyer, D; and  Remy-Neris O. (2018) Dissociating motor learning from recovery in exoskeleton training post-stroke  Journal of NeuroEngineering and Rehabilitation, 15(1):89. doi: 10.1186/s12984-018-0428-1.

Kim, B., Fisher, B.E., Schweighofer, N., Leahy, R.M., Haldar, J.P., Choi, S., Kay, D.B., Gordon, J. and Winstein, C.J., (2018) A comparison of seven different DTI-derived estimates of corticospinal tract structural characteristics in chronic stroke survivors. Journal of neuroscience methods, 304, 66-75.


Bakhti KKA, Mottet D, Schweighofer N, Froger J, Laffont I. (2017) Proximal arm non-use when reaching after a stroke Neuroscience Letters. 657, 91-96

Park H, 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, 21


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. DOI: 10.1152/jn.00148.2016

Park H., Kim S., Winstein C.J., Gordon J., and Schweighofer N. (2016)
Short-duration and intensive training improves long-term reaching performance in individuals with chronic stroke Neurorehabilitation and Neural Repair. 30(6), 551-561

Reinkensmeyer D.J., Burdet E., Casadio M., Krakauer J.W., Kwakkel G., Lang C.E., Swinnen S.P., Ward N.S. and Schweighofer N. (2016) Computational neurorehabilitation: modeling plasticity and learning to predict recovery Journal of Neuroengineering and Neural Repair. 13(1):42

Lee J.Y., Oh Y., Kim S.S., Scheidt R.A., and Schweighofer N. (2016) Optimal schedules in multitask motor learning Neural Computation. 28(4):667-685

Lang E.J., Apps R., Bengtsson F., Cerminara N.L., De Zeeuw C.I., Ebner T.J., Heck D.H., Jaeger D., Jörntell H., Kawato M., Otis T.S., Ozyildirim O., Popa L.S., Reeves A.M., Schweighofer N., Sugihara I., and Xiao J. (2016) The roles of the olivocerebellar pathway in motor learning and motor control. A consensus paper The Cerebellum.


Kim S.S., Ogawa K., Lv. J., Schweighofer N.* and Imamizu H.* (2015) Neural substrates related to motor memory with multiple timescales in sensorimotor adaptation PLoS Biology. 13(12):e1002312

Gueugneau N., Schweighofer N. and Papaxanthis C. (2015) Daily update of motor predictions by physical activity Scientific Reports. 5:1-9

Kim S.S., Oh Y., and Schweighofer N. (2015) Between trial forgetting due to interference and time in motor adaptation PLoS One. 10(11):e0142963

Bains A.S. and Schweighofer N. (2015) Robust use-dependent learning in arm movements Translational and Computational Motor Control.

Schweighofer N., Xiao Y., Kim S., Yoshioka T., Gordon J., and Osu R. (2015)
Effort, success, and non-use determine arm choice Journal of Neurophysiology. 114(1):551-559


Bains A.S. and Schweighofer N. (2014) Time-sensitive reorganization of the somatosensory cortex poststroke depends on interaction between Hebbian and homeoplasticity: a simulation study Journal of Neurophysiology. 112(12):3240-3250 

Sargent B., Schweighofer N., Kubo M., and Fetters L. (2014) Infant exploratory learning: influence on leg joint coordination PLoS One. 9(3):e91500

Winstein C.J., Wolf S.L., and Schweighofer N. (2014) Task oriented training to promote upper extremity recovery In Stein et al. (Eds.), Stroke Recovery and Rehabilitation, 2nd Ed. New York, NY: Demos Medical.


Kim S.S., Ogawa K., Lv J., Schweighofer N., and Imamizu, H. (2013)
Neural correlates of motor memory with multiple time scales in sensorimotor adaptation Translational and Computational Motor Control.

Onizuka M., Hoang H., Kawato M., Tokuda I.T., Schweighofer N., Katori Y., Aihara K., Lang E.J., and Toyama K. (2013) Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from the spike trains by network model simulation Neural Networks. 47:51-63

Han C.E., Kim S., Chen S., Lai Y.H., Lee J.-Y., Osu R., Winstein C.J., and Schweighofer N. (2013) Quantifying arm nonuse in individuals poststroke
Neurorehabilitation and Neural Repair. 27(5):439-447

Tokuda I.T., Hoang H., Schweighofer N., and Kawato M. (2013) Adaptive coupling of inferior olive neurons in cerebellar learning Neural Networks. 47:42-50


Schweighofer N., Choi Y., Winstein C.J., and Gordon, J. (2012)
Task-oriented rehabilitation robotics American Journal of Physical Medicine & Rehabilitation. 91(11)

Hidaka Y., Han C.E., Wolf S.L., Winstein C.J.,and Schweighofer N. (2012) Use it and improve it or lose it: Interactions between arm function and use in humans post-stroke PLoS Computational Biology. 8(2):e1002343 


Qi F. and Schweighofer N. (2011) Including prior knowledge for accurate and fast motor threshold estimation Brain Stimulation. 4(1):60-61

Schweighofer N., Lee J.-Y., Goh H.T., Choi Y., Kim S., Stewart J.C., Lewthwaite R.,and Winstein C.J. (2011) Mechanisms of the contextual interference effect in individuals post-stroke Journal of Neurophysiology. 106(5):2632-2641

Choi Y., Gordon J., Park H.,and Schweighofer N. (2011) Feasibility of the Adaptive and Automatic Presentation of Tasks (ADAPT) system for rehabilitation of upper extremity function post-stroke Journal of Neuroengineering and Rehabilitation. 8(1):42 **This will need to be reuploaded. 

Kawato M., Kuroda S., and Schweighofer N. (2011) Cerebellar supervised learning revisited:biophysical modeling and degrees-of-freedom control Current Opinion in Neurobiology. 21:791-800

Frey S.H., Fogassi L., Grafton S., Picard N., Rothwell J.C., Schweighofer N., Corbetta M.,and Fitzpatrick S.M. (2011) Neurological principles and rehabilitation of action disorders: Computation, Anatomy, and Physiology (CAP)model Neurorehabilitation and Neural Repair. 25(5Suppl):6S-20S

Abe M., Schambra H., Wassermann E.M., Luckenbaugh D., Schweighofer N.,and Cohen L.G. (2011) Reward improves long-term retention of a motor memory through induction of offline memory gains Current Biology. 21:557-562

Qi F., Wu A., and Schweighofer N. (2011) Fast estimation of TMS motor threshold
Brain Stimulation. 4:50-57


Tokuda I., Han C.E., Aihara K., Kawato M.,and Schweighofer N. (2010) Role of chaotic resonance in cerebellar learning Neural Networks. 23(7):836-842

Gentili R., Han C.E., Schweighofer N.,and Papaxanthis C. (2010) Motor learning without doing: Trial-by-trial improvement in motor performance during mental training Journal of Neurophysiology. 104(2):774-783

Callan D. and Schweighofer N. (2010) Neural correlates of the spacing effect in explicit verbal semantic encoding support the deficient-processing theory Human Brain Mapping. 31(4):645-659


Tanaka S., Shishida K., Schweighofer N.,Okamoto Y., Yamawaki S., and Doya K.(2009) Serotonin affects association of aversive outcomes to past actions Journal of Neuroscience. 29(50):15669-15674

Schweighofer N., Han C.E., Wolf S.L., Arbib, M.A.and Winstein C. (2009) 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-Induced Therapy Evaluation (EXCITE) Trial Physical Therapy. 89(12):1327-1336

Lee J.-Y. and Schweighofer N. (2009) Dual-adaptation supports a parallel architecture of motor memory Journal of Neuroscience. 29:10396-10404

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 On Robotics. 24:556-568 


Han C.E., Arbib M.A., and Schweighofer N.(2008) Stroke rehabilitation reaches a threshold PLoS Computational Biology. 4(8):e1000133

Choi Y.G., Qi F., Gordon J.,and Schweighofer N. (2008) Performance-based adaptive schedules enhance motor learning Journal of Motor Behavior. 40:273-280

Schweighofer N., Bertin M., Shishida K.,Okamoto Y., Tanaka S.C., Yamawaki S.,and Doya K. (2008) Low-serotonin levels increase delayed reward discounting in humans Journal of Neuroscience. 28:4528-4532

Callan D. and Schweighofer N. (2008) Positive and negative modulation of word learning by reward anticipation Human Brain Mapping. 29:237-249


Tanaka S.C., Schweighofer N., Asahi S., Shishida K., Okamoto Y., Yamawaki S.,and Doya K. (2007) Serotonin differentially regulates short- and long-term prediction of rewards in the ventral and dorsal striatum 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

2006 and before

Schweighofer N., Shishida K., Han C.E., Okamoto Y., Tanaka S.C., Yamawaki S.,and Doya K. (2006) Humans can adopt optimal discounting strategy under real-time constraints PLoS Computational Biology. 11:1349-1356

Pozzo T., Papaxanthis C., Petit J.L., Schweighofer N., and Stucchi N.(2006). Kinematic features of movement tunes perception and action coupling Behav Brain Research. 169:75-82

Schaal S. and Schweighofer N. (2005) Computational motor control in humans and robots Current Opinion in Neurobiology. 25:1-8

Schweighofer N., Doya K., ChironJ.V., Fukai H., Furukawa T., and Kawato M.(2004)
Chaos may enhance information transmission in the inferior olive Proceedings of the National Academy of Sciences. 101:4655-4660

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

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 of signal transduction pathways in cerebellar long-term depression by kinetic simulation Journal of Neuroscience. 21:5693-5702

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

Schweighofer N. and Ferriol G.(2000) Diffusion of nitric oxide canfacilitate cerebellar learning: A simulation study Proceedings of the National Academy of Sciences. 97:10661-5

Spoelstra J., Schweighofer N.,and Arbib M.A. (2000) Cerebellar learning of accurate predictive control for fast reaching movements Biological Cybernetics. 82:321-333

Schweighofer N., Doya K., and Kawato M.(1999) Electrophysiological properties of the inferior olive neurons: A compartmental model Journal of Neurophysiology. 82:804-817

Spoelstra J., Arbib M.A.,and Schweighofer N. (1999) Cerebellar adaptive control of a biomimetic manipulator Neurocomputing. 82:804-817

Schweighofer N. (1998) A model of activity-dependent formation of cerebellar microzones Biological Cybernetics. 79:97-107

Schweighofer N., Arbib, M.A. and Kawato, M.(1998) Role of the cerebellum in reaching movements in humans. I. Distributed inverse dynamics control European Journal of Neuroscience. 10:86-94

Schweighofer N., Spoelstra J., Arbib M.A,and Kawato M. (1998) Role of the cerebellum in reaching movements in humans. II. A neural model of the intermediate cerebellum European Journal of Neuroscience. 10:95-105

Schweighofer N. and Arbib M.A.(1998) A model of cerebellar meta plasticity
Learning & Memory. 4:421-428

Schweighofer N., Arbib M.A.,and Dominey P.F. (1996) A model of the cerebellum in adaptive control of saccadic gain. I. The model and its biological substrate
Biological Cybernetics. 75:19-28

Schweighofer N., Arbib M.A.,and Dominey P.F. (1996) A model of the cerebellum in adaptive control of saccadic gain. II. Simulation results Biological Cybernetics. 75:29-36