Chaotic motif sampler for motif discovery using statistical values of spike time-series

Abstract

One of the most important issues in bioinformatics is to discover a common and conserved pattern, which is called a motif, from biological sequences. We have already proposed a motif extraction method called Chaotic Motif Sampler (CMS) by using chaotic dynamics. Exploring a searching space with avoiding undesirable local minima, the CMS discovers the motifs very effectively. During a searching process, chaotic neurons generate very complicated spike time-series. In the present paper, we analyzed the complexity of the spike time-series observed from each chaotic neuron by using a statistical measure, such as a coefficient of variation and a local variation of interspike intervals, which are frequently used in the field of neuroscience. As a result, if a motif is embedded in a sequence, corresponding spike time-series show characteristic behavior. If we use these characteristics, multiple motifs can be identified. © 2008 Springer-Verlag Berlin Heidelberg.