Ferdinando A. Mussa-Ivaldi focuses on Motor control, Neuroscience, Artificial intelligence, Control theory and Communication. His studies in Motor control integrate themes in fields like Degrees of freedom problem, Inverse dynamics, Anatomy and Set. In the subject of general Neuroscience, his work in Central nervous system, Spinal cord, Motor cortex and Neurophysiology is often linked to Modular design, thereby combining diverse domains of study.
His research integrates issues of Basis function, Biomechanical Phenomena, Vector field and Computer vision in his study of Artificial intelligence. Ferdinando A. Mussa-Ivaldi combines subjects such as Applied mathematics and Motor learning with his study of Control theory. Ferdinando A. Mussa-Ivaldi works mostly in the field of Communication, limiting it down to topics relating to Horizontal plane and, in certain cases, Orientation.
His primary areas of study are Artificial intelligence, Control theory, Motor control, Motor learning and Physical medicine and rehabilitation. His research investigates the connection with Artificial intelligence and areas like Computer vision which intersect with concerns in Proprioception and Representation. His work is dedicated to discovering how Control theory, Simulation are connected with Kalman filter and Motion control and other disciplines.
Motor control is a primary field of his research addressed under Neuroscience. In his study, Hand movements and Visual feedback is inextricably linked to Communication, which falls within the broad field of Motor learning. The Physical medicine and rehabilitation study combines topics in areas such as Rehabilitation, Cursor, Spinal cord injury and Motor skill.
His main research concerns Physical medicine and rehabilitation, Perception, Rehabilitation, Adaptation and Artificial intelligence. The concepts of his Physical medicine and rehabilitation study are interwoven with issues in Spinal cord injury and Model predictive control. His Adaptation research also works with subjects such as
His Motor control study deals with the bigger picture of Control theory. Ferdinando A. Mussa-Ivaldi has included themes like Dimension, Computer vision and Motor learning in his Artificial intelligence study. Ferdinando A. Mussa-Ivaldi has researched Motor learning in several fields, including Sequence and State.
Ferdinando A. Mussa-Ivaldi mainly focuses on Artificial intelligence, Physical medicine and rehabilitation, Motor learning, Electromyography and Model predictive control. His Artificial intelligence research is multidisciplinary, relying on both Dimension and Computer vision. His study in Physical medicine and rehabilitation is interdisciplinary in nature, drawing from both Pathological and Activities of daily living.
His Motor learning study combines topics in areas such as Sequence and State. His research in Electromyography intersects with topics in Rehabilitation, Motor control and Set. His Model predictive control research incorporates elements of Perception, Grip force, Sensory processing, Anticipation and Illusion.
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Adaptive representation of dynamics during learning of a motor task
Reza Shadmehr;Ferdinando A. Mussa-Ivaldi.
The Journal of Neuroscience (1994)
Neural, mechanical, and geometric factors subserving arm posture in humans
FA Mussa-Ivaldi;N Hogan;E Bizzi.
The Journal of Neuroscience (1985)
Computations underlying the execution of movement: a biological perspective
Emilio Bizzi;Ferdinando A. Mussa-Ivaldi;Simon Giszter.
Convergent force fields organized in the frog's spinal cord
SF Giszter;FA Mussa-Ivaldi;E Bizzi.
The Journal of Neuroscience (1993)
Does the nervous system use equilibrium-point control to guide single and multiple joint movements?
E. Bizzi;N. Hogan;F. A. Mussa-Ivaldi;S. Giszter.
Behavioral and Brain Sciences (1992)
Motor learning by field approximation
F Gandolfo;F A Mussa-Ivaldi;E Bizzi.
Proceedings of the National Academy of Sciences of the United States of America (1996)
The Motor System Does Not Learn the Dynamics of the Arm by Rote Memorization of Past Experience
Michael A. Conditt;Francesca Gandolfo;Ferdinando A. Mussa-Ivaldi.
Journal of Neurophysiology (1997)
Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors.
James L. Patton;Mary Ellen Stoykov;Mark Kovic;Ferdinando A. Mussa-Ivaldi.
Experimental Brain Research (2006)
Linear combinations of primitives in vertebrate motor control
F A Mussa-Ivaldi;S F Giszter;E Bizzi.
Proceedings of the National Academy of Sciences of the United States of America (1994)
Learning to move amid uncertainty.
Robert A. Scheidt;Jonathan B. Dingwell;Ferdinando A. Mussa-Ivaldi.
Journal of Neurophysiology (2001)
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