K. Holthausen, EVOLUTION OF INTERNAL REPRESENTATIONS GENERATED BY UNSUPERVISED SELF-REFERENTIAL NETWORKS, THEORY IN BIOSCIENCES, 117(1), 1998, pp. 18-31
A novel learning algorithm for unsupervised topological clustering is
introduced that generates mappings of arbitrary randomly received inpu
t signals. The concept of a self-referential adaptation is defined; th
is leads to a dynamical assignment of equivalence classes. The optimiz
ed topological representation allows to distinguish between even very
similar input vectors. The performance of the algorithm is analysed st
atistically and conclusions for the mathematical description of self-r
eferential biological systems are derived.