Neural computation with ideal approximation elements

We describe an analytical framework for the adaptations of neural systems that adapt their internal structure on the basis of subjective probabilities constructed via computation of randomly received input signals. A principled approach is provided that has the key property that it defines a probability density model that allows to study the convergence of the adaptation process. Certain neural network models (e. g. topological feature maps and associative networks) can be derived from our approach.