ON THE MATHEMATICAL FOUNDATIONS OF A THEORY OF NEURAL REPRESENTATION

We develop a theory of neural representation that considers a neural s ystem as an autonomous entity that adapts its internal structure on th e basis of subjective probabilities constructed on the basis of random ly received input signals. The objective probabilities distribution of the signal space is principally unknown to the system, and furthermor e the system's limitations, e.g. structural rigidity and limited capac ity form an integral part of our analysis. These considerations yield the conceptual basis for a new algorithm for a self-organizing feature map.