Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and utilizing form-function principles in realistic neuronal reconstructions.

中文翻译：

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轴突树的形态和信号传播动力学改善了神经元的分类。

神经元是多种多样的，可以通过它们的形态，电生理和分子特性来区分。当前基于形态学的分类方法在很大程度上依赖于树状树结构或整个轴突投影布局。在这里，我们使用来自神经元重建和膜特性的公共数据库中的数据来研究轴突和树突树的特征，以进行中间神经元分类。我们表明，结合通过沿分支的轴突树的活动的生物物理模拟观察到的信号传播模式与轴突和树突树的形态学参数，相比以前的方法大大改善了分类结果。此处介绍的分类方案可用于鲁棒的神经元分类。