Abstract
In order to intercept a moving target a motor schema causes the hand to aim ahead and to adapt to the target trajectory. During the performance of perception-action-cycles, a pre-programmed prototypical movement trajectory, a motor schema, may highly reduce the control load. From a modelling point of view, a neural network may allow the implementation of a motor schema interacting with feedback control in an iterative manner. A neural population net of the Wilson-Cowan type was allowing the generation of a moving bubble. This activation bubble runs down an eye-centered motor schema and causes a planar arm model to move towards the target. The bubble provides local integration and straightening of the trajectory during repetitive moves. The schema adapts to task demands by learning and serves as a forward controller.
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