Neural Network Model for Voluntary Movement and Application to Robotics

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Human motor skills are not innate, but have to be acquired by training from birth. In the beginning, movements are controlled by using feedback signals through visual pathways. With increased skill, the feedback control system is replaced as the main controller by a feedforward control system which means movements can be control led unconsciously. A neural network model which can explain this process is proposed. The model is based on a rule called the feedback-error-learning. The inverse dynamics of the motor system is organized in a three layer neural network according to the back-propagation learning rule.Optimal control of human arm movement is also discussed , and a neural network model which realizes the optimal pathways based on the minimum torque change criterion is proposed. Basic ideas are (1) spatial representation of time, (2) learning of forward dynamics and kinematics model and (3) relaxation computation based on the acquired model. Operations of this network are divided into the learning phase and the pattern-generating phase. In the learning phase, this network acquires a forward model of the multi-degree-of-freedom controlled object while monitoring the actual trajectory as a teaching signal. In the pattern-generating phase, electrical coupling between neurons representing motor commands at neighboring times is activated to guarantee the minimum torque-change criterion. By computer simulation, we show that the model can produce a multi-joint arm trajectory while avoiding obstacles or passing through viapoints.