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.