Discovering the Multi-neuronal Firing Patterns Based on a New Binless Spike Trains Measure

摘要

In this paper, we proposed a method which presented a new definition of different multi-step interval ISI-distance distribution of single neuronal spike trains and formed a new feature vector to represent the original spike trains. It is a binless spike train's measure method. We used spectral clustering algorithm on new multi-dimensional feature vectors to detect the multiple neuronal firing patterns. We tested this method on standard data set in machine learning, neuronal surrogate data set and in vivo multi-electrode recordings respectively. Results shown that the method proposed in this paper can effectively improve the clustering accuracy in standard data set and detect the firing patterns in neuronal spike trains.