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Learning and generation of goal-directed arm reaching from scratch.
- Source :
-
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2009 May; Vol. 22 (4), pp. 348-61. Date of Electronic Publication: 2008 Nov 30. - Publication Year :
- 2009
-
Abstract
- In this paper, we propose a computational model for arm reaching control and learning. Our model describes not only the mechanism of motor control but also that of learning. Although several motor control models have been proposed to explain the control mechanism underlying well-trained arm reaching movements, it has not been fully considered how the central nervous system (CNS) learns to control our body. One of the great abilities of the CNS is that it can learn by itself how to control our body to execute required tasks. Our model is designed to improve the performance of control in a trial-and-error manner which is commonly seen in human's motor skill learning. In this paper, we focus on a reaching task in the sagittal plane and show that our model can learn and generate accurate reaching toward various target points without prior knowledge of arm dynamics. Furthermore, by comparing the movement trajectories with those made by human subjects, we show that our model can reproduce human-like reaching motions without specifying desired trajectories.
- Subjects :
- Algorithms
Arm innervation
Biomechanical Phenomena
Computer Simulation
Cues
Feedback physiology
Humans
Joints physiology
Mathematics
Orientation physiology
Proprioception physiology
Psychomotor Performance physiology
Range of Motion, Articular physiology
Space Perception physiology
Arm physiology
Goals
Learning physiology
Motor Skills physiology
Movement physiology
Volition physiology
Subjects
Details
- Language :
- English
- ISSN :
- 1879-2782
- Volume :
- 22
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Neural networks : the official journal of the International Neural Network Society
- Publication Type :
- Academic Journal
- Accession number :
- 19121565
- Full Text :
- https://doi.org/10.1016/j.neunet.2008.11.004