Associate Professor, Department of Biokinesiology and Physical Therapy
Research Topics1. Computational neural models of motor learning. 2. Optimization of learning via adaptive practice schedules in healthy and stroke.
Research OverviewThe 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.
- Web Site:
- Lab Site
- Mailing Address:
- Department of Physical Therapy and Biokinesiology
University of Southern California
1540 Alcazar Street
Los Angeles, CA 90089-9006, USA
- Office Phone:
- (323) 442-2141
- (323) 442-1515
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, 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.