Neural Processing Letters

, Volume 13, Issue 3, pp 237–251 | Cite as

Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison

  • Markus Varsta
  • Jukka Heikkonen
  • Jouko Lampinen
  • José Del R. Millán


This paper compares two Self-Organizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal context of the input signals. The learning and the convergence properties of the TKM and RSOM are studied and we show analytically that the RSOM is a significant improvement over the TKM, because the RSOM allows simple derivation of a consistent learning rule. The results of the analysis are demonstrated with experiments.

convergence analysis self-organizing maps temporal sequence processing 


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Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Markus Varsta
    • 1
  • Jukka Heikkonen
    • 1
  • Jouko Lampinen
    • 1
  • José Del R. Millán
    • 2
  1. 1.Laboratory of Computational EngineeringHelsinki University of TechnologyFinland
  2. 2.Institute for Systems, Informatics and SafetyEuropean Commission, Joint Research CentreIspra (VA)Italy

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