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A spike sorting framework using nonparametric detection and incremental clustering
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DOI：10.1016/j.neucom.2005.12.111.png)
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摘要
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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. (c) 2006 Elsevier B.V. All rights reserved.
Keyword:
spike sorting
nonparametric modeling
incremental clustering
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6.5
论文数:
2.5W
被引数:
6.5W
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