Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain.
Laboratorio Cajal de Circuitos Corticales, Universidad Politécnica de Madrid and Instituto Cajal (CSIC), Pozuelo de Alarcón, 28223, Spain.
Sci Data. 2019 Oct 22;6(1):221. doi: 10.1038/s41597-019-0246-8.
There is currently no unique catalog of cortical GABAergic interneuron types. In 2013, we asked 48 leading neuroscientists to classify 320 interneurons by inspecting images of their morphology. That study was the first to quantify the degree of agreement among neuroscientists in morphology-based interneuron classification, showing high agreement for the chandelier and Martinotti types, yet low agreement for most of the remaining types considered. Here we present the dataset containing the classification choices by the neuroscientists according to interneuron type as well as to five prominent morphological features. These data can be used as crisp or soft training labels for learning supervised machine learning interneuron classifiers, while further analyses can try to pinpoint anatomical characteristics that make an interneuron especially difficult or especially easy to classify.
目前尚无皮质 GABA 能中间神经元类型的独特目录。2013 年,我们要求 48 位领先的神经科学家通过检查其形态的图像来对 320 个中间神经元进行分类。这项研究首次量化了基于形态的中间神经元分类中神经科学家之间的一致性程度,表明在 Chandelier 和 Martinotti 类型上具有高度一致性,但在大多数其余被认为的类型上一致性较低。这里我们提供了包含神经科学家根据中间神经元类型以及五个突出形态特征进行分类选择的数据。这些数据可用于学习监督机器学习中间神经元分类器的清晰或软训练标签,而进一步的分析可以尝试找出使中间神经元特别难以或特别容易分类的解剖特征。