Embedded information enhancement for neuron selection in self-organizing maps

In this paper, we improve the information enhancement method to extract important input variables. The information enhancement method has been developed to detect important components in neural systems. Our previous method focused on the detection of the important components; therefore, it has not fully incorporated the information contained in the components into learning processes. We here embed the information enhancement into the processes of self-organizing. The embedding is realized simply by adding the importance of input neurons to the self-organizing maps (SOM). We applied the method to an artificial data set and a real data set taken in the field of the second language learning (L2 learning). In the artificial data set, we could show that the symmetric properties of the data could be explicitly extracted. In the L2 learning data set, only one important variable could be detected. The detected input variable represented a grammatical item termed “the inanimate subject.” This variable was found to be the one which differentiated Japanese high school students from university students in the level of L2 grammatical competence. It has been claimed that students have difficulty in producing sentences with inanimate subjects in the field of L2 learning. Thus, the finding by the method well corresponds to the knowledge of L2 learning.