Prof. Dr. David Franklin
Academic Career and Research Areas
Prof. David Franklin investigates the physiological and computational principles of human neuromuscular motor control. His research examines how the nervous system controls the mechanical properties of the body to adapt to our external environment and produce skilful movement. To examine the computations underlying sensorimotor control, he blends computational and experimental approaches including robotics and virtual reality.
Prof. Franklin studied human physiology and was awarded a doctorate in neuroscience in 2005 from the Department of Kinesiology at Simon Fraser University (Canada). He worked as a researcher at the Institute for Advanced Telecommunications Research (Kyoto, Japan) from 1999 to 2006. He then spent 3 years as a research associate in the Department of Engineering at the University of Cambridge (U.K.). In 2010 he was awarded a Wellcome Trust RCD Fellowship and became a research fellow at the University of Cambridge. He has been an associate professor of Neuromuscular Diagnostics at TUM since 2016.
Awards
- Best Paper Award, WeRob Conference (2018)
- Wellcome Trust Research Career Development Fellow (2010)
- Governor General’s Gold Medal, Governor General of Canada (2005)
- Research Prize of Japanese Neural Network Society (2002)
Key Publications (all publications)
Leib R, Howard IS, Millard M, Franklin DW: “Behavioral Motor Performance”. Comprehensive Physiology. 2024; 14(1): 5179-5224.
AbstractČesonis J, Franklin DW: “Contextual cues are not unique for motor learning: Task-dependant switching of feedback controllers”. PLoS Computational Biology. 2022; 18(6), e1010192.
AbstractForano M, Schween R, Taylor JA, Hegele M, Franklin DW: “Direct and indirect cues can enable dual adaptation, but through different learning processes”. Journal of Neurophysiology. 2021; 126(5): 1490-1506.
AbstractLee S, Franklin S, Hassani FA, Yokota T, Nayeem MOG, Wang Y, Leib R, Cheng G, Franklin DW, Someya T: “Nanomesh pressure sensor for monitoring finger manipulation without sensory interference”. Science. 2020; 370(6519): 966-970.
AbstractFranklin DW, Burdet E, Tee KP, Osu R, Chew CM, Milner TE, Kawato M: “CNS learns stable, accurate, and efficient movements using a simple algorithm”. Journal of Neuroscience. 2008; 28(44), 11165-11173.
AbstractIf you wish your profile to be changed or updated please contact Franz Langer.