A blind deconvolution method for neural spike identification
A blind deconvolution method for neural spike identification
复制标题
一种神经尖峰识别的盲反卷积方法
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Eero P. Simoncelli
中科院分区：
文献类型：
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作者：
Chaitanya Ekanadham;D. Tranchina;Eero P. Simoncelli
我们考虑了从细胞外电压记录中估计神经尖峰的问题。大多数当前的方法都是基于聚类的，这需要大量的人工监督，并且系统地错误地处理临时重叠的尖峰。我们将问题描述为一个统计推断问题，其中记录的电压是每个神经元的尖峰序列与其相关的尖峰波形卷积的噪声和。因此，波形和尖峰的联合最大后验概率(MAP)估计是一个系数稀疏的盲反卷积问题。基于我们最近发展的连续基寻踪方法，我们发展了一种块坐标下降法来逼近MAP解。我们在模拟数据和真实数据上验证了我们的方法，这些数据通过同时的细胞内记录获得了地面真实情况。在这两种情况下，与标准的聚类算法相比，我们的方法大大减少了遗漏尖峰和误报的数量，主要是通过恢复重叠的尖峰。该方法为聚类方法提供了一种完全自动化的替代方法，不太容易受到系统误差的影响。
We consider the problem of estimating neural spikes from extracellular voltage recordings. Most current methods are based on clustering, which requires substantial human supervision and systematically mishandles temporally overlapping spikes. We formulate the problem as one of statistical inference, in which the recorded voltage is a noisy sum of the spike trains of each neuron convolved with its associated spike waveform. Joint maximum-a-posteriori (MAP) estimation of the waveforms and spikes is then a blind deconvolution problem in which the coefficients are sparse. We develop a block-coordinate descent procedure to approximate the MAP solution, based on our recently developed continuous basis pursuit method. We validate our method on simulated data as well as real data for which ground truth is available via simultaneous intracellular recordings. In both cases, our method substantially reduces the number of missed spikes and false positives when compared to a standard clustering algorithm, primarily by recovering overlapping spikes. The method offers a fully automated alternative to clustering methods that is less susceptible to systematic errors.
DOI：
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发表时间：
2008
期刊：
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影响因子：
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作者：
Jonathan W. Pillow;Jonathon Shlens;Liam Paninski;A. Sher;A. Litke;E. Chichilnisky;E. Simoncelli
通讯作者：
Jonathan W. Pillow;Jonathon Shlens;Liam Paninski;A. Sher;A. Litke;E. Chichilnisky;E. Simoncelli
影响因子：
2.5
作者：
Harris, KD;Henze, DA;Buzsáki, G
通讯作者：
Buzsáki, G