Collective Activations to Generate Self-Organizing Maps

摘要

In this paper, we propose a new method called collective activations to realize self-organizing maps. We suppose that all neurons collectively respond to input stimuli, and this collectiveness is represented by the sum of all neurons' activations. Learning consists of imitating these collective activations as much as possible. We applied the method to artificial data and a broadband survey problem. In all these problems, we could obtain self-organizing maps similar or, in some cases, superior to those obtained by conventional SOM. Thus, the present study is considered to be the first step toward more realistic self-organizing maps.