Recursive self-organizing maps

  • Authors:
  • Thomas Voegtlin

  • Affiliations:
  • Institut des Sciences Cognitives, CNRS UMR 5015, 67 boulevard Pinel, 69675 Bron Cedex, France

  • Venue:
  • Neural Networks - New developments in self-organizing maps
  • Year:
  • 2002

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Abstract

This paper explores the combination of self-organizing map (SOM) and feedback, in order to represent sequences of inputs. In general, neural networks with time-delayed feedback represent time implicitly, by combining current inputs and past activities. It has been difficult to apply this approach to SOM, because feedback generates instability during learning. We demonstrate a solution to this problem, based on a nonlinearity. The result is a generalization of SOM that learns to represent sequences recursively. We demonstrate that the resulting representations are adapted to the temporal statistics of the input series.