Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
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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 the information content in the 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 neighboring neurons behave similarly. These two processes are unified and controlled in the framework of cooperative information control. We applied the new method to political data analyses. 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.