Neural model of adaptive hand-eye coordination for single postures

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Science  11 Mar 1988:
Vol. 239, Issue 4845, pp. 1308-1311
DOI: 10.1126/science.3344437


A neural network model has been developed that achieves adaptive visual-motor coordination of a multijoint arm, without a teacher. The model learns to position an arm so that it reaches a cylinder arbitrarily positioned in space. The model uses a new neural architecture and a new algorithm for modifying neural-connection strengths. Computer simulations show that the model performs with an average position error of 4% of the arm's length and with an average orientation error of 4 degrees. The model is designed to be generalized for coordinating any number of topographic sensory inputs with limbs of any number of joints.