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The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|
October 17, 2008
Composition and decomposition in bimanual dynamic learning
Ian S Howard, James N Ingram, Daniel M Wolpert
Current Biology : CB
|
January 13, 2015
The value of the follow-through derives from motor learning depending on future actions
Ian S Howard, Daniel M Wolpert, David W Franklin
Journal of Neuroscience Methods
|
May 20, 2009
A modular planar robotic manipulandum with end-point torque control
Ian S Howard, James N Ingram, Daniel M Wolpert
Journal of Neurophysiology
|
April 18, 2025
The impact of dwell time on the contextual effect of visual and passive lead-in movements
Laura Alvarez-Hidalgo, David W Franklin, Ian S Howard
Journal of Neurophysiology
|
August 6, 2010
Context-dependent partitioning of motor learning in bimanual movements
Ian S Howard, James N Ingram, Daniel M Wolpert
Scientific Reports
|
August 12, 2017
Active lead-in variability affects motor memory formation and slows motor learning
Ian S Howard, Christopher Ford, Angelo Cangelosi, et al.
The Science of the Total Environment
|
June 2, 2023
In silico prediction of acute chemical toxicity of biocides in marine crustaceans using machine learning
Rama Krishnan, Ian S Howard, Sean Comber, et al.
Frontiers in Aging Neuroscience
|
January 28, 2026
Investigating age-related decline in sensorimotor control using robotic tasks
Laura Alvarez-Hidalgo, Ellie Edlmann, Gunnar Schmidtmann, et al.
Comprehensive Physiology
|
December 29, 2023
Behavioral Motor Performance
Raz Leib, Ian S Howard, Matthew Millard, et al.
Plos Computational Biology
|
October 8, 2011
A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics
James N Ingram, Ian S Howard, J Randall Flanagan, et al.
Page
of 4
Search research articles
Search
Showing results (11-20 of 32) with videos related to
Sort By:
Page
of 4
The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|
October 17, 2008
Composition and decomposition in bimanual dynamic learning
Ian S Howard, James N Ingram, Daniel M Wolpert
Current Biology : CB
|
January 13, 2015
The value of the follow-through derives from motor learning depending on future actions
Ian S Howard, Daniel M Wolpert, David W Franklin
Journal of Neuroscience Methods
|
May 20, 2009
A modular planar robotic manipulandum with end-point torque control
Ian S Howard, James N Ingram, Daniel M Wolpert
Journal of Neurophysiology
|
April 18, 2025
The impact of dwell time on the contextual effect of visual and passive lead-in movements
Laura Alvarez-Hidalgo, David W Franklin, Ian S Howard
Journal of Neurophysiology
|
August 6, 2010
Context-dependent partitioning of motor learning in bimanual movements
Ian S Howard, James N Ingram, Daniel M Wolpert
Scientific Reports
|
August 12, 2017
Active lead-in variability affects motor memory formation and slows motor learning
Ian S Howard, Christopher Ford, Angelo Cangelosi, et al.
The Science of the Total Environment
|
June 2, 2023
In silico prediction of acute chemical toxicity of biocides in marine crustaceans using machine learning
Rama Krishnan, Ian S Howard, Sean Comber, et al.
Frontiers in Aging Neuroscience
|
January 28, 2026
Investigating age-related decline in sensorimotor control using robotic tasks
Laura Alvarez-Hidalgo, Ellie Edlmann, Gunnar Schmidtmann, et al.
Comprehensive Physiology
|
December 29, 2023
Behavioral Motor Performance
Raz Leib, Ian S Howard, Matthew Millard, et al.
Plos Computational Biology
|
October 8, 2011
A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics
James N Ingram, Ian S Howard, J Randall Flanagan, et al.
Page
of 4