Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA 91125, USA.
IEEE Trans Biomed Eng. 2009 Nov;56(11):2649-59. doi: 10.1109/TBME.2009.2027604. Epub 2009 Jul 28.
This paper introduces a new, unsupervised method for sorting and tracking the action potentials of individual neurons in multiunit extracellular recordings. Presuming the data are divided into short, sequential recording intervals, the core of our strategy relies upon an extension of a traditional mixture model approach that incorporates clustering results from the preceding interval in a Bayesian manner, while still allowing for signal nonstationarity and changing numbers of recorded neurons. As a natural byproduct of the sorting method, current and prior signal clusters can be matched over time in order to track persisting neurons. We also develop techniques to use prior data to appropriately seed the clustering algorithm and select the model class. We present results in a principal components space; however, the algorithm may be applied in any feature space where the distribution of a neuron's spikes may be modeled as Gaussian. Applications of this signal classification method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results than traditional methods based on expectation-maximization optimization of mixture models. This consistent tracking ability is crucial for intended applications of the method.
本文介绍了一种新的、无监督的方法，用于对多单元细胞外记录中的单个神经元的动作电位进行排序和跟踪。假设数据被分为短的、顺序的记录间隔，我们策略的核心依赖于一种传统的混合模型方法的扩展，该方法以贝叶斯的方式结合了前一个间隔的聚类结果，同时仍然允许信号非平稳和记录的神经元数量的变化。作为排序方法的一个自然副产品，当前和先前的信号簇可以随时间匹配，以便跟踪持续存在的神经元。我们还开发了一些技术，使用先前的数据来适当的为聚类算法和模型类进行种子设置。我们在主成分空间中展示了结果；但是，该算法可以应用于任何特征空间，在该空间中，神经元的尖峰的分布可以建模为高斯分布。该信号分类方法在猕猴顶叶皮层记录中的应用表明，与基于混合模型的期望最大化优化的传统方法相比，它提供了更一致的聚类和跟踪结果。这种一致的跟踪能力对于该方法的预期应用至关重要。
