Contradiction Resolution with Dependent Input Neuron Selection for Self-Organizing Maps

In this paper, we propose a new type of information-theoretic method called "dependent input neuron selection" to improve contradiction resolution. Contradiction resolution has been previously introduced to realize self-organizing maps by supposing two types of evaluation, namely, self and outer-evaluation. In self-evaluation, a neuron\´s firing rate is determined by itself, while in outer-evaluation, the firing rate is determined by other neurons. Outer-evaluation corresponds to cooperation between neurons in the self-organizing maps. Dependent input neuron selection aims to use a small number of input neurons which are forced to respond to different input patterns. Our method was applied to the prediction of dollar-yen exchange rates. Experimental results confirmed that prediction performance was improved by choosing the appropriate number of winning input neurons. The improved performance can be attributed to the fact that connection weights were condensed into several groups and winning input neurons tended to respond to different time lags.