An application of reversible-jump Markov chain Monte Carlo to spike classification of multi-unit extracellular recordings.
An application of reversible-jump Markov chain Monte Carlo to spike classification of multi-unit extracellular recordings.
复制标题
可逆跳跃马尔可夫链蒙特卡罗在多单元细胞外记录尖峰分类中的应用。
DOI：
10.1088/0954-898x/14/1/304
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Brown,EmeryN
中科院分区：
文献类型：
--
作者：
Nguyen,DavidP;Frank,LorenM;Brown,EmeryN
神经组织中的多电极记录包含许多紧密间隔的神经元的动作电位波形。虽然我们可以观察动作电位波形，但我们无法观察哪个神经元是哪个波形的源，也无法观察记录了多少个源神经元。目前的尖峰排序算法通过假设固定数量的源神经元并在给定该固定数量的情况下分配动作电位来解决这个问题。我们的尖峰波形建模为各向异性高斯混合模型，并提出，作为一种替代方案，一个可逆跳马尔可夫链蒙特卡罗（MCMC）算法，同时估计源神经元的数量，并分配每个动作电位的源。我们推导出这种MCMC算法，并说明其应用程序使用模拟的三维数据和真实的四维特征向量提取大鼠内嗅皮层神经元的四极录音。在模拟数据的分析中，我们的算法找到了正确数量的混合成分（源），并以最小的误差对动作电位波形进行分类。在真实的数据的分析，我们的算法识别集群密切类似于以前确定的用户依赖的图形聚类过程。我们的研究结果表明，可逆跳MCMC算法可以提供一个新的策略，设计自动尖峰排序算法。
Multi-electrode recordings in neural tissue contain the action potential waveforms of many closely spaced neurons. While we can observe the action potential waveforms, we cannot observe which neuron is the source for which waveform nor how many source neurons are being recorded. Current spike-sorting algorithms solve this problem by assuming a fixed number of source neurons and assigning the action potentials given this fixed number. We model the spike waveforms as an anisotropic Gaussian mixture model and present, as an alternative, a reversible-jump Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the number of source neurons and to assign each action potential to a source. We derive this MCMC algorithm and illustrate its application using simulated three-dimensional data and real four-dimensional feature vectors extracted from tetrode recordings of rat entorhinal cortex neurons. In the analysis of the simulated data our algorithm finds the correct number of mixture components (sources) and classifies the action potential waveforms with minimal error. In the analysis of real data, our algorithm identifies clusters closely resembling those previously identified by a user-dependent graphical clustering procedure. Our findings suggest that a reversible-jump MCMC algorithm could offer a new strategy for designing automated spike-sorting algorithms.