SELF-ORGANIZED FEATURE MAPS AND INFORMATION-THEORY

We describe new algorithms for self-organizing feature maps based on a generalized information theory. In our model the maximizing of the lo cal information transfer leads to a topologically ordered map whereas the increase of global information fails. The adaptation of the synapt ic weights depends sorely on internal variables which constitute the r epresentation of the signal space. Applications with respect to vector quantization are discussed.