The control of hand equilibrium trajectories in multi-joint arm movements
Biological Cybernetics
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Forward models for physiological motor control
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?
Neural Networks - Special issue on organisation of computation in brain-like systems
TD Models of reward predictive responses in dopamine neurons
Neural Networks - Computational models of neuromodulation
Actor-critic models of the basal ganglia: new anatomical and computational perspectives
Neural Networks - Computational models of neuromodulation
Biological arm motion through reinforcement learning
Biological Cybernetics
TOPS (Task Optimization in the Presence of Signal-Dependent Noise) model
Systems and Computers in Japan
Reinforcement Learning in Continuous Time and Space
Neural Computation
Neural Networks - 2006 Special issue: Neurobiology of decision making
Motor planning and sparse motor command representation
Neurocomputing
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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.