| Paper: | MLSP-P6.3 | ||
| Session: | Biomedical and Other Applications | ||
| Time: | Friday, May 19, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Biomedical Applications and Neural Engineering | ||
| Title: | A Mixture Model for Spike Train Ensemble Analysis Using Spectral Clustering | ||
| Authors: | Rong Jin, Michigan State University, United States; Yashir Suhail, The Johns Hopkins University, United States; Karim Oweiss, Michigan State University, United States | ||
| Abstract: | Identifying clusters of neurons with correlated spiking activity in large-size neuronal ensembles recorded with high-density multielectrode array is an emerging problem in computational neuroscience. We propose a nonparametric approach that represents multiple neural spike trains by a mixed point process model. A spectralclustering algorithm is applied to identify the clusters of neurons through their correlated firing activities. The advantage of the proposed technique is its ability to efficiently identify large populations of neurons with correlated spiking activity independent of the temporal scale. We report the clustering performance of the algorithm applied to a complex synthesized data set and compare it to multipleclustering techniques | ||
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