Partitioning the Firing Patterns of Spike Trains by Community Modularity

Abstract

The traditional clustering method utilized to partition neuronal firing patterns, including K-means and FCM algorithm, require specification of clusters numbers as priori knowledge. A new approach to analyze groups of firing patterns of neuronal spike trains based on community structure partitioning analysis and modularity function Q is examined in this study. This approach is able to automatically identify the optimal number of groups in neuronal firing patterns, realizing the true unsupervised analysis, and identify groups of neurons with similar firing patterns. The method was tested on a surrogate data set and a testing data set with firing patterns known in advance. The method was also applied to multi-electrode recording spike trains with previously unknown patterns. Results indicate this method can effectively self-determine number of pattern groups and locate firing patterns of neuronal populations based on community modularity Q .


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