Self-organizing maps: ordering, convergence properties and energy functions

E. Erwin, K. Obermayer, K. Schulten

Research output: Contribution to journalArticlepeer-review

Abstract

We investigate the convergence properties of the self-organizing feature map algorithm for a simple, but very instructive case: the formation of a topographic representation of the unit interval [0,1] by a linear chain of neurons. We extend the proofs of convergence of Kohonen and of Cottrell and Fort to hold in any case where the neighborhood function, which is used to scale the change in the weight values at each neuron, is a monotonically decreasing function of distance from the winner neuron. We prove that the learning dynamics cannot be described by a gradient descent on a single energy function, but may be described using a set of potential functions, one for each neuron, which are independently minimized following a stochastic gradient descent. We derive the correct potential functions for the oneand multi-dimensional case, and show that the energy functions given by Tolat (1990) are an approximation which is no longer valid in the case of highly disordered maps or steep neighborhood functions.

Original languageEnglish (US)
Pages (from-to)47-55
Number of pages9
JournalBiological Cybernetics
Volume67
Issue number1
DOIs
StatePublished - May 1992

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science(all)

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