Mihaljević Bojan, Larrañaga Pedro, Bielza Concha
Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Boadilla del Monte, Spain.
Front Neuroinform. 2021 Feb 18;15:580873. doi: 10.3389/fninf.2021.580873. eCollection 2021.
Pyramidal neurons are the most common neurons in the cerebral cortex. Understanding how they differ between species is a key challenge in neuroscience. We compared human temporal cortex and mouse visual cortex pyramidal neurons from the Allen Cell Types Database in terms of their electrophysiology and dendritic morphology. We found that, among other differences, human pyramidal neurons had a higher action potential threshold voltage, a lower input resistance, and larger dendritic arbors. We learned Gaussian Bayesian networks from the data in order to identify correlations and conditional independencies between the variables and compare them between the species. We found strong correlations between electrophysiological and morphological variables in both species. In human cells, electrophysiological variables were correlated even with morphological variables that are not directly related to dendritic arbor size or diameter, such as mean bifurcation angle and mean branch tortuosity. Cortical depth was correlated with both electrophysiological and morphological variables in both species, and its effect on electrophysiology could not be explained in terms of the morphological variables. For some variables, the effect of cortical depth was opposite in the two species. Overall, the correlations among the variables differed strikingly between human and mouse neurons. Besides identifying correlations and conditional independencies, the learned Bayesian networks might be useful for probabilistic reasoning regarding the morphology and electrophysiology of pyramidal neurons.
锥体神经元是大脑皮层中最常见的神经元。了解它们在不同物种之间的差异是神经科学中的一个关键挑战。我们根据电生理学和树突形态，比较了来自艾伦细胞类型数据库的人类颞叶皮层和小鼠视觉皮层的锥体神经元。我们发现，除了其他差异外，人类锥体神经元具有更高的动作电位阈值电压、更低的输入电阻和更大的树突分支。我们从数据中学习高斯贝叶斯网络，以识别变量之间的相关性和条件独立性，并在不同物种之间进行比较。我们发现两个物种的电生理和形态变量之间都存在很强的相关性。在人类细胞中，电生理变量甚至与一些与树突分支大小或直径没有直接关系的形态变量相关，如平均分叉角和平均分支曲折度。皮层深度与两个物种的电生理和形态变量都相关，其对电生理的影响无法用形态变量来解释。对于一些变量，皮层深度的影响在两个物种中是相反的。总体而言，人类和小鼠神经元之间变量的相关性存在显著差异。除了识别相关性和条件独立性外，学习到的贝叶斯网络可能有助于对锥体神经元的形态和电生理学进行概率推理。
