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

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 F...

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Bibliographic Details
Published in:Biological cybernetics 1992-05, Vol.67 (1), p.47-55
Main Authors: ERWIN, E, OBERMAYER, K, SCHULTEN, K
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Mathematics
Models, Neurological
Stochastic Processes
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Summary: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 one- and multi-dimensional case, and show that the energy functions given by Tolat (1990) are an approximation which is no longer valid in the case of highly disordered maps or steep neighborhood functions.
ISSN:0340-1200
1432-0770
DOI:10.1007/BF00201801