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Title: Application of multi-domain clustering to C. elegans neural network analysis Authors:  Ye Liu - Hong Kong Baptist University (Hong Kong)
Michael Ng - Hong Kong Baptist University (Hong Kong)
Stephen Wu - The Institute of Statistical Mathematics (Japan) [presenting]
Mirai Tanaka - The Institute of Statistical Mathematics (Japan)
Abstract: Whole-brain imaging of C. elegans allows neuroscientist to access the full neural network activity of a single worm under different stimulations, which will be an important step for understanding brain activities. However, the noisy nature of the images makes it difficult to extract meaningful activity patterns using conventional clustering methods. A typical statistical solution is to increase the number of worm samples in order to suppress the influence from the noise. In order to take the full advantage of multiple data sets, we formulate the neural network analysis of multiple worms as a multi-domain clustering problem. We construct an undirected graph for each worm to represent the correlation of the neural activities between neurons. The preliminary results of our multi-domain clustering method show interesting biological meanings that may guide the future experiment of the C. elegans research.