Abstract
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.
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Tavan, P., Grubmüller, H. & Kühnel, H. Self-organization of associative memory and pattern classification: recurrent signal processing on topological feature maps. Biol. Cybern. 64, 95–105 (1990). https://doi.org/10.1007/BF02331338
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DOI: https://doi.org/10.1007/BF02331338