title: A neural network model for the acquisition of a spatial body scheme through sensorimotor interaction.
abstract: This letter presents a novel unsupervised sensory matching learning technique for the development of an internal representation of three-dimensional information. The representation is invariant with respect to the sensory modalities involved. Acquisition of the internal representation is demonstrated with a neural network model of a sensorimotor system of a simple model creature, consisting of a tactile-sensitive body and a multiple-degrees-of-freedom arm with proprioceptive sensitivity. Acquisition of the 3D representation as well as a distributed representation of the body scheme, occurs through sensorimotor interactions (i.e., the sensory-motor experience of the creature). Convergence of the learning is demonstrated through computer simulations for the model creature with a 7-DoF arm and a spherical body covered by 20 tactile fields.
authors.affiliation: Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, 117485 Moscow, Russia. vroschin@mail.ru
authors.forename: Vadim Y
authors.initials: VY
authors.lastname: Roschin
authors.forename: Alexander A
authors.initials: AA
authors.lastname: Frolov
authors.forename: Yves
authors.initials: Y
authors.lastname: Burnod
authors.forename: Marc A
authors.initials: MA
authors.lastname: Maier
mesh_terms: D003198:Computer Simulation; D007858:Learning / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D016571:Neural Networks, Computer; D011434:Proprioception / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D013037:Spatial Behavior / Q000502:physiology*