Fast Nonnegative Deconvolution for Spike Train Inference From Population Calcium Imaging
Fast Nonnegative Deconvolution for Spike Train Inference From Population Calcium Imaging
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DOI：
10.1152/jn.01073.2009
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发表时间：
2010-12-01
影响因子：
2.5
通讯作者：
Paninski, Liam
中科院分区：
文献类型：
--
作者：
Vogelstein, Joshua T.;Packer, Adam M.;Paninski, Liam
[10]张文辉，张文辉，张文辉，B文辉，张文辉.快速非负反卷积用于群体钙成像的锋电位序列推断。J Neurophysiol 104：3691-3704，2010.首次发表于2010年6月16日; doi：10.1152/jn.01073.2009。荧光钙指示剂作为观察大型神经元群体的尖峰活动的手段正变得越来越受欢迎。不幸的是，从原始荧光电影中提取每个神经元的尖峰序列是一个不平凡的问题。这项工作提出了一个快速的非负反卷积滤波器来推断每个神经元的近似最可能的尖峰序列，给定的荧光观察。该算法优于最佳线性反卷积（维纳滤波）的模拟和生物数据。与维纳滤波器不同，性能增益来自于将推断的尖峰序列限制为正（使用相邻点方法）。该算法以线性时间运行，并且足够快，即使当同时成像>100个神经元时，也可以比真实的时间更快地对所有观察到的迹线的集合执行推断。对图像执行最佳空间滤波进一步细化推断的尖峰序列估计。重要的是，可以仅使用荧光数据来估计执行推断所需的所有参数，从而避免了执行联合电生理学和成像校准实验的需要。
Vogelstein JT, Packer AM, Machado TA, Sippy T, Babadi B, Yuste R, Paninski L. Fast nonnegative deconvolution for spike train inference from population calcium imaging. J Neurophysiol 104: 3691-3704, 2010. First published June 16, 2010; doi:10.1152/jn.01073.2009. Fluorescent calcium indicators are becoming increasingly popular as a means for observing the spiking activity of large neuronal populations. Unfortunately, extracting the spike train of each neuron from a raw fluorescence movie is a nontrivial problem. This work presents a fast nonnegative deconvolution filter to infer the approximately most likely spike train of each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm runs in linear time, and is fast enough that even when simultaneously imaging >100 neurons, inference can be performed on the set of all observed traces faster than real time. Performing optimal spatial filtering on the images further refines the inferred spike train estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments.