Generalized neural model for adaptive sensory-motor control of single postures

A neural-network model has been developed that achieves adaptive visual-motor coordination of a multijoint arm, without a teacher. The model has been applied to adaptively positioning an arm so that it reaches a cylinder arbitrarily positioned in space. The model uses a neural architecture and an 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°. The model is designed to be generalized for coordinating any number of topographic sensory inputs with limbs of any number of joints. The general scheme of the neural model is proposed