Kamimura, R., Kamimura, T., & Shultz, T. R. (2001). Structural information control for flexible competitive learning. In V. Kurdova, N. C. Steele, R. Neruda, & M. Karny (Eds.), Artificial neural nets and genetic algorithms: Proceedings of the International Conference in Prague, Czech Republic, 2001 (pp. 90-93). Heidelberg, Germany: Springer-Verlag.



In this paper, we propose a new information theoretic method called structural information to overcome fundamental problems inherent in conventional competitive learning such as dead neurons and deciding on the appropriate number of neurons in the competitive layer. Our method is based on defining and controlling several kinds of information, thus generating particular neuron firing patterns. For one firing pattern, some neurons are completely inactive, meaning that some dead neurons are generated. For another firing pattern, all neurons are active, that is, there are no dead neurons. This means that we can control the number of dead neurons and choose the appropriate number of neurons by controlling information content. We applied this method to simple pattern classification to show that information can be controlled, and that different neuron firing patterns can be generated. 


Copyright notice

Abstracts, papers, chapters, and other documents are posted on this site as an efficient way to distribute reprints. The respective authors and publishers of these works retain all of the copyrights to this material. Anyone copying, downloading, bookmarking, or printing any of these materials agrees to comply with all of the copyright terms. Other than having an electronic or printed copy for fair personal use, none of these works may be reposted, reprinted, or redistributed without the explicit permission of the relevant copyright holders.


To obtain a PDF reprint of this particular article, signal your agreement with these copyright terms by clicking on the statement below.


I agree with all of these copyright terms PDF 94KB