Professor Daniel Wolpert
Professor Daniel Wolpert is pleased to consider applications from prospective PhD students.
http://www.wolpertlab.com (personal home page)
The group uses engineering approaches to understand how the human brain controls movement. The work includes both computational modelling and experimental approaches using robotic and virtual reality interfaces. Research areas include motor planning and optimal control,
probabilistic (Bayesian) models, motor predictive and modular approaches to motor learning.
No direct clinical relevance
No collaborators listed
Körding KP, Wolpert DM (2004), “Bayesian integration in sensorimotor learning” Nature 427:244-247 Details
Wolpert DM, Flanagan JR (2010), “Motor learning.” Curr Biol 20(11):R467-72 Details
Resulaj A, Kiani R, Wolpert DM, Shadlen MN (2009), “Changes of mind in decision-making.” Nature 461(7261):263-6 Details
Faisal AA, Selen LP, Wolpert DM (2008), “Noise in the nervous system.” Nat Rev Neurosci 9(4):292-303 Details