Hybrid information processing systems to generate self-organizing maps: combining SOM and information maximization for coherent activation patterns

We combine a self-organizing map (SOM) and information maximization to produce coherent competitive unit activation patterns in an artificial system. The new system is composed of a SOM component and an information maximization component. In the SOM component, the conventional SOM is used to cooperate neurons. In the information maximization component, information between input units and competitive units is increased as much as possible. The component plays a role to accentuate activation patterns obtained by the SOM component. We apply the new system to medical data analysis. Experimental results confirm that firing patterns obtained by the conventional SOM are reinforced and become clearer by the information maximization component.