On approximation algorithms for local multiple alignment
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Chaotic Motif Sampler for Motif Discovery Using Statistical Values of Spike Time-Series
Neural Information Processing
Chaotic Search for Traveling Salesman Problems by Using 2-opt and Or-opt Algorithms
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Effect of refractoriness on learning performance of a pattern sequence
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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To discover a common and conserved pattern, or motif, from DNA sequences is an important step to analyze DNA sequences because the patterns are acknowledged to reflect biological important information. However, it is difficult to discover unknown motifs from DNA sequences because of its huge number of combination. We have already proposed a new effective method to extract the motifs using a chaotic search, which combines a heuristic algorithm and a chaotic dynamics. To realize the chaotic search, we used a chaotic neural network. The chaotic search exhibits higher performance than conventional methods. Although we have indicated that the refractory effects realized by the chaotic neural network have an essential role, we did not clarify why the refractory effects are important to search optimal solutions. In this paper, we further investigate this issue and reveal the validity of the refractory effects of the chaotic dynamics using surrogate refractory effects. As a result, we discovered that it is important for searching optimal solutions to increase strength of the refractory effects after a firing of neurons.