SELF-ORGANIZATION OF ASSOCIATIVE MEMORY AND PATTERN-CLASSIFICATION - RECURRENT SIGNAL-PROCESSING ON TOPOLOGICAL FEATURE MAPS

We extend the neural concepts of topological feature maps towards self-organization of auto-associative memory and hierarchical pattern classification. As is well-known, topological maps for statistical data sets store information on the associated probability densities. To extract that information we introduce a recurrent dynamics of signal processing. We show that the dynamics converts a topological map into an auto-associative memory for real-valued feature vectors which is capable to perform a cluster analysis. The neural network scheme thus developed represents a generalization of non-linear matrix-type associative memories. The results naturally lead to the concept of a feature atlas and an associated scheme of self-organized, hierarchical pattern classification. © 1990 Springer-Verlag.