University of Southern California

USC Neuroscience

Nicolas Schweighofer

Associate Professor, Department of Biokinesiology and Physical Therapy

Research Topics

1. Computational neural models of motor learning. 2. Optimization of learning via adaptive practice schedules in healthy and stroke.

Research Overview

The goal of the work on neuro-computational models is to understand the neural bases of motor learning. We are notably investigating motor plasticity in the cerebellum, map plasticity and reorganization in the motor cortex, multiple task learning, and adaptive decision-making during motor learning in healthy and lesioned brains. When appropriate, we test our predictions by conducting behavioral and/or brain imaging (fMRI and TMS) experiments either at USC or with our collaborators at ATR in Japan or at INSERM in France.

The goal of the work on learning optimization is to enhance re-learning of motor skills in patients with stroke. Despite great progress in psychology and neuroscience, physical therapists treating patients with stroke rely on unspecific guidelines to determine task practice schedules for functional motor skill re-acquisition. Using algorithms that combine neuroscience-based models and artificial intelligence, we aim at defining and testing adaptive practice schedules, with particular emphasis on the micro-schedules of the practice.

Contact Information

Web Site:
Lab Site
Mailing Address:
Department of Physical Therapy and Biokinesiology
University of Southern California
CHP 155
1540 Alcazar Street
Los Angeles, CA 90089-9006, USA
Office Phone:
(323) 442-2141
(323) 442-1515

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Selected Publications

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Hidaka Y., Han C.E., Wolf S., Winstein C.J., and Schweighofer N. (2012) Use it and improve it, or lose it: interactions between arm use and function during stroke recovery in humans. PLoS Comput. Biol., 8(2)  e1002343 -Link

Schweighofer N, Lee JY, Goh HT, Choi Y, Kim SS, Stewart JC, Lewthwaite R, Winstein CJ. (2011) Mechanisms of the contextual interference effect in individuals post stroke. J Neurophysiol. 106(5):2632-41 -PubMed

Kawato M., Kuroda S., and Schweighofer N. (2011) Cerebellar supervised learning revisited: bioinformatics modeling and degrees-of-freedom control. Current Opinion in Neurobiology, 21:1-10 -PubMed

Lee JY. and Schweighofer N. (2009) Dual adaptation supports a parallel architecture of motor memory, J Neurosci., 29:10396-404 -PubMed

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

Schweighofer N., Bertin, M., Shihida K., Tanaka S., Okamoto Y., Yamawaki S., and Doya K. (2008) Serotonin modulation of delayed reward discounting in humans, J Neurosci., 28:4528-4532. -PubMed -Link

Schweighofer N, Shishida K, Han CE, Okamoto Y, Tanaka SC, Yamawaki S, Doya K. (2006) Humans can adopt optimal discounting strategy under real-time constraints. PLoS Comput Biol. 2:e152 -PubMed

Schaal S, Schweighofer N. (2005) Computational motor control in humans and robots. Curr Opin Neurobiol. 15(6):675-82. -PubMed

Schweighofer N, Doya K, Fukai H, Chiron JV, FurukawaT, Kawato M. (2004) Chaos may enhance information transmission in the inferior olive. Proc Natl Acad Sci U S A. 101(13):4655- -PubMed

Schweighofer N, Ferriol G. (2000) Diffusion of nitric oxide can facilitate cerebellar learning: A simulation study. Proc Natl Acad Sci U S A. 97(19):10661-5.