Creating Self-organization Maps by Cooperative Information Control
This paper proposes a novel information theoretic approach to self-organization called cooperative information control. The method aims to mediate between competition and cooperation among neurons by controlling information content in neurons. Competition is realized by maximizing information content in neurons. In the process of information maximization, only a small number of neurons win the competition, while all the others are inactive. Cooperation is implemented by having neurons behave similarly to their neighbors. These two processes are unified and controlled in the framework of cooperative information control. We applied the new method to a political analysis. In the analysis, experimental results confirmed that competition and cooperation are flexibly controlled. In addition, controlled processes can yield a number of different neuron firing patterns, which can be used to detect macro as well as micro features in input patterns.
Ryotaro Kamimura Taeko Kamimura Osamu Uchida Shohachiro Nakanishi
Future Science and Technology Joint Research Center and Information Science Laboratory, Tokai Univer Department of English, Senshu University, Japan Department of Network Engineering, Kanagawa Institute of Technology, Japan Future Science and Technology Joint Research Center and Department of Human and Information Science,
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
1349-1354
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）