Generalization of the self-organizing map: From artificial neural networks to artificial cortexes

Abstract

This paper presents a generalized framework of a self-organizing map (SOM) applicable to more extended data classes rather than vector data. A modular structure is adopted to realize such generalization; thus, it is called a modular network SOM (mnSOM), in which each reference vector unit of a conventional SOM is replaced by a functional module. Since users can choose the functional module from any trainable architecture such as neural networks, the mnSOM has a lot of flexibility as well as high data processing ability. In this paper, the essential idea is first introduced and then its theory is described. © Springer-Verlag Berlin Heidelberg 2006.