Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Latent variable models for neural data analysis
Latent variable models for neural data analysis
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Uncovering the neural code using a rat model during a learning control task
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
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We introduce a statistical computing framework to address two important issues in spike sorting: flexible spike shape modeling and realtime spike clustering. In this framework, spikes are detected based on a nonparametric shape distribution; detected spikes are further grouped by an incremental clustering algorithm involving the second-order statistics-covariance matrix. We performed experiments on both simulated and real signals to study spike detection accuracy and cluster separation.