Kalman filter mixture model for spike sorting of non-stationary data
Kalman filter mixture model for spike sorting of non-stationary data
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DOI：
10.1016/j.jneumeth.2010.12.002
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发表时间：
2011-03-15
影响因子：
3
通讯作者：
Paninski, Liam
中科院分区：
文献类型：
--
作者：
Calabrese, Ana;Paninski, Liam
细胞外记录中的非平稳性可能会在体内实验期间出现一个主要问题。在本文中，我们提出了跟踪随时间变化的尖峰形状的自动方法。我们的算法基于计算高效的卡尔曼滤波器模型：该模型的递归性质允许该方法在线实现。可以使用标准期望最大化方法来估算模型参数。此外，可以通过密切相关的隐藏马尔可夫模型技术结合难治效应。我们对模拟和真实数据的算法性能进行了分析。 （c）2010 Elsevier B.V.保留所有权利。
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.