期刊
JOURNAL OF NEUROSCIENCE METHODS
卷 196, 期 1, 页码 159-169出版社
ELSEVIER
DOI: 10.1016/j.jneumeth.2010.12.002
关键词
Spike sorting; Non-stationarity; Kalman filter; Hidden Markov model; Mixture model; EM algorithm
资金
- NSF
- McKnight Scholar award
Nonstationarity in extracellular recordings can present a major problem during in vivo experiments. In this paper we present automatic methods for tracking time-varying spike shapes. Our algorithm is based on a computationally efficient Kalman filter model: the recursive nature of this model allows for on-line implementation of the method. The model parameters can be estimated using a standard expectation-maximization approach. In addition, refractory effects may be incorporated via closely related hidden Markov model techniques. We present an analysis of the algorithm's performance on both simulated and real data. (C) 2010 Elsevier B.V. All rights reserved.
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