Haar Shlomi, Givon-Mayo Ronit, Barmack Neal H, Yakhnitsa Vadim, Donchin Opher
Department of Biomedical Engineering, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.
Faculty of Health Science, and Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.
J Neurosci. 2015 Jan 28;35(4):1432-42. doi: 10.1523/JNEUROSCI.5019-13.2015.
The effort to determine morphological and anatomically defined neuronal characteristics from extracellularly recorded physiological signatures has been attempted with varying success in different brain areas. Recent studies have attempted such classification of cerebellar interneurons (CINs) based on statistical measures of spontaneous activity. Previously, such efforts in different brain areas have used supervised clustering methods based on standard parameterizations of spontaneous interspike interval (ISI) histograms. We worried that this might bias researchers toward positive identification results and decided to take a different approach. We recorded CINs from anesthetized cats. We used unsupervised clustering methods applied to a nonparametric representation of the ISI histograms to identify groups of CINs with similar spontaneous activity and then asked how these groups map onto different cell types. Our approach was a fuzzy C-means clustering algorithm applied to the Kullbach-Leibler distances between ISI histograms. We found that there is, in fact, a natural clustering of the spontaneous activity of CINs into six groups but that there was no relationship between this clustering and the standard morphologically defined cell types. These results proved robust when generalization was tested to completely new datasets, including datasets recorded under different anesthesia conditions and in different laboratories and different species (rats). Our results suggest the importance of an unsupervised approach in categorizing neurons according to their extracellular activity. Indeed, a reexamination of such categorization efforts throughout the brain may be necessary. One important open question is that of functional differences of our six spontaneously defined clusters during actual behavior.
通过细胞外记录的生理特征来确定形态学和解剖学定义的神经元特征的努力，在不同脑区取得了不同程度的成功。最近的研究尝试基于自发活动的统计测量对小脑中间神经元（CINs）进行此类分类。此前，在不同脑区的此类努力使用了基于自发峰峰间隔（ISI）直方图标准参数化的监督聚类方法。我们担心这可能会使研究人员偏向于阳性识别结果，因此决定采用不同的方法。我们从麻醉的猫身上记录CINs。我们使用应用于ISI直方图非参数表示的无监督聚类方法来识别具有相似自发活动的CINs组，然后询问这些组如何映射到不同的细胞类型。我们的方法是将模糊C均值聚类算法应用于ISI直方图之间的库尔巴赫 - 莱布勒距离。我们发现，实际上，CINs的自发活动自然地聚类为六组，但这种聚类与标准形态学定义的细胞类型之间没有关系。当对全新数据集进行泛化测试时，包括在不同麻醉条件下、不同实验室和不同物种（大鼠）记录的数据集，这些结果证明是稳健的。我们的结果表明无监督方法在根据神经元的细胞外活动对其进行分类方面的重要性。实际上，可能有必要重新审视整个大脑中的此类分类工作。一个重要的未解决问题是我们六个自发定义的簇在实际行为中的功能差异问题。
