Abstract
For the interface between a human neuronal network and a machine circuit, it is critical to establish the neuronal decoding technology. The information processing of the neuronal network is embedded in network dynamics. The dissociated rat hippocampal neurons on multi-electrodes array dish are useful as a simple model of brain information processing system. In this research, we extracted pattern repertory from the spontaneous electrical activity in living neuronal network (LNN). One of the clustering methods, X-means with kkz preprocessing, was applied to the series of feature vectors of numbers of spikes within a time window at 64 electrodes. The width of time window was set to 5 ms, which include only a spike at the most. The X-means method estimates the adequate number of clusters according to Bayesian information criterion, however, the number of clusters in 30 min recorded data was estimated as extremely large, approximately over 1000. Such too sensitive clustering results are considered to be because of the uniformity of the weight of feature vector elements. However, limiting to clusters with at least 1% occurrence, approximately only 15 pattern repertories (clusters) repeatedly occurred. In addition, the cycle of the pattern repertory was approximately 30-40 s.
