Forward models for physiological motor control
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
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
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Handbook of Neural Network Signal Processing
Handbook of Neural Network Signal Processing
Multiple model-based reinforcement learning
Neural Computation
TOPS (Task Optimization in the Presence of Signal-Dependent Noise) model
Systems and Computers in Japan
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
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In this study, a hierarchical structure is proposed to model human movement control during sit-to-stand transfer. At the highest level the desired movement is planned. Then, the task to be performed is decomposed to its constitutive sub-tasks. To decompose the sit-to-stand movement, the spatial trajectory of the body center of mass is automatically approximated by partially linearized trajectories. Each linearized part defines a sub-task. At the second level, corresponding to each sub-task a module is developed that learns to control the movement during the performance of that sub-task. Since the procedure of decomposition is performed automatically, the number of modules and assessment of suitable data to train the modules are also determined automatically. This feature is one of the main differences between the proposed structure and the MOdular Selection And Identification for Control (MOSAIC) structure [M. Haruno, D.M. Wolpert, M. Kawato, MOSAIC model for sensorimotor learning and control, Neural Computation 13 (2001) 2201-2220.]. Our proposed model is in conformity with the recent physiological and neurobehavioral findings and provides a framework for examining a given movement under different conditions.