title: Neural model of adaptive hand-eye coordination for single postures.
abstract: 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.
authors.affiliation: Biology Department, Wellesley College, MA 02181.
authors.forename: M
authors.initials: M
authors.lastname: Kuperstein
mesh_terms: D006801:Humans; D008953:Models, Anatomic; D008959:Models, Neurological; D008960:Models, Psychological; D011187:Posture; D011597:Psychomotor Performance