Dynamic self-organising map

Authors:
Nicolas Rougier;Yann Boniface
Affiliations:
LORIA/INRIA Nancy - Grand Est Research Centre, 54600 Villers-lès-Nancy, France;LORIA/Université Nancy 2, 54015 Nancy Cedex, France
Venue:
Neurocomputing
Year:
2011

Citing 5
Cited 0

A Self-Organizing Network that Can Follow Non-stationary Distributions

ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Magnification Control in Self-Organizing Maps and Neural Gas

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An incremental network for on-line unsupervised classification and topology learning

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An enhanced self-organizing incremental neural network for online unsupervised learning

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`Neural-gas' network for vector quantization and its application to time-series prediction

IEEE Transactions on Neural Networks

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Abstract

We present in this paper a variation of the self-organising map algorithm where the original time-dependent (learning rate and neighbourhood) learning function is replaced by a time-invariant one. This allows for on-line and continuous learning on both static and dynamic data distributions. One of the property of the newly proposed algorithm is that it does not fit the magnification law and the achieved vector density is not directly proportional to the density of the distribution as found in most vector quantisation algorithms. From a biological point of view, this algorithm sheds light on cortical plasticity seen as a dynamic and tight coupling between the environment and the model.