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Neurons with Continuous Varying Activation in Self-Organizing Maps

By Josef Göppert and Wolfgang Rosenstiel

Abstract

A new training and recall method for self-organizing maps (SOM) is developed by comparison of SOM to the human information processing system. As neurons and cortical columns do not change their activity instantly, it is increased or decreased in a smooth way. This fact is introduced in SOM-neurons. In a same way, recognition of objects is supposed to be a task of analysing complete sets of feature vectors and finding the region in the SOM which represents the current inputs best. This method especially allows the evalutation of ambiguous feature vectors and of objects which are decomposed in sets of basic feature vectors or which are aquired in a continuous temporal flow. 1 Introduction The SOM takes into account cooperative aspects of neighbouring neurons by using individual activation for all neurons. The activation reflects the distance of one unit to the winner in the grid of neurons and is used for the calculation of the adaptation strength. So, similar adaptation of neighbouring..

Publisher: Springer-Verlag
Year: 1995
DOI identifier: 10.1007/3-540-59497-3_204
OAI identifier: oai:CiteSeerX.psu:10.1.1.47.3970
Provided by: CiteSeerX
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