Biological Cybernetics

, Volume 64, Issue 2, pp 95–105 | Cite as

Self-organization of associative memory and pattern classification: recurrent signal processing on topological feature maps

  • P. Tavan
  • H. Grubmüller
  • H. Kühnel
Article

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.

Keywords

Neural Network Cluster Analysis Signal Processing Probability Density Feature Vector 

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Copyright information

© Springer-Verlag 1990

Authors and Affiliations

  • P. Tavan
    • 1
  • H. Grubmüller
    • 1
  • H. Kühnel
    • 1
  1. 1.Physik-DepartmentTechnische Universität MünchenGarchingFederal Republic of Germany

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