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Ken Takiyama

Showing results (1-10 of 40) with videos related to

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Scientific Reports|April 30, 2015
Sensorimotor transformation via sparse codingKen Takiyama
Neural Networks : the Official Journal of the International Neural Network Society|June 3, 2017
Bayesian geodesic path for human motor controlKen Takiyama
Frontiers in Computational Neuroscience|February 21, 2015
Context-dependent memory decay is evidence of effort minimization in motor learning: a computational studyKen Takiyama
Physical Review. E|June 15, 2016
Bayesian estimation inherent in a Mexican-hat-type neural networkKen Takiyama
Neural Computation|February 9, 2011
Detection of hidden structures in nonstationary spike trainsKen Takiyama, Masato Okada
Scientific Reports|February 14, 2020
A data-driven approach to decompose motion data into task-relevant and task-irrelevant components in categorical outcomeDaisuke Furuki, Ken Takiyama
Frontiers in Sports and Active Living|January 19, 2024
Speed-dependent modulations of asymmetric center of body mass trajectory in the gait of above-knee amputee subjectsKen Takiyama, Hikaru Yokoyama
Scientific Reports|March 31, 2016
Balanced motor primitive can explain generalization of motor learning effects between unimanual and bimanual movementsKen Takiyama, Yutaka Sakai
Plos One|June 28, 2016
Development of a Portable Motor Learning Laboratory (PoMLab)Ken Takiyama, Masahiro Shinya
Neural Networks : the Official Journal of the International Neural Network Society|November 28, 2016
A balanced motor primitive framework can simultaneously explain motor learning in unimanual and bimanual movementsKen Takiyama, Yutaka Sakai
Pageof 4

Showing results (1-10 of 40) with videos related to

Sort By:
Pageof 4
Scientific Reports|April 30, 2015
Sensorimotor transformation via sparse codingKen Takiyama
Neural Networks : the Official Journal of the International Neural Network Society|June 3, 2017
Bayesian geodesic path for human motor controlKen Takiyama
Frontiers in Computational Neuroscience|February 21, 2015
Context-dependent memory decay is evidence of effort minimization in motor learning: a computational studyKen Takiyama
Physical Review. E|June 15, 2016
Bayesian estimation inherent in a Mexican-hat-type neural networkKen Takiyama
Neural Computation|February 9, 2011
Detection of hidden structures in nonstationary spike trainsKen Takiyama, Masato Okada
Scientific Reports|February 14, 2020
A data-driven approach to decompose motion data into task-relevant and task-irrelevant components in categorical outcomeDaisuke Furuki, Ken Takiyama
Frontiers in Sports and Active Living|January 19, 2024
Speed-dependent modulations of asymmetric center of body mass trajectory in the gait of above-knee amputee subjectsKen Takiyama, Hikaru Yokoyama
Scientific Reports|March 31, 2016
Balanced motor primitive can explain generalization of motor learning effects between unimanual and bimanual movementsKen Takiyama, Yutaka Sakai
Plos One|June 28, 2016
Development of a Portable Motor Learning Laboratory (PoMLab)Ken Takiyama, Masahiro Shinya
Neural Networks : the Official Journal of the International Neural Network Society|November 28, 2016
A balanced motor primitive framework can simultaneously explain motor learning in unimanual and bimanual movementsKen Takiyama, Yutaka Sakai
Pageof 4