Accurate neuron morphologies are paramount for computational model simulations of realistic neural responses. Over the last decade, the online repository NeuroMorpho.Org has collected over 140,000 available neuron morphologies to understand brain function and promote interaction between experimental and computational research. Neuron morphologies describe spatial aspects of neural structure; however, many of the available morphologies do not contain accurate diameters that are essential for computational simulations of electrical activity. To best utilize available neuron morphologies, we present a set of equations that predict dendritic diameter from other morphological features. To derive the equations, we used a set of NeuroMorpho.org archives with realistic neuron diameters, representing hippocampal pyramidal, cerebellar Purkinje, and striatal spiny projection neurons (SPNs). Each morphology is separated into initial, branching children, and continuing nodes. Our analysis reveals that the diameter of preceding nodes, Parent Diameter, is correlated to diameter of subsequent nodes for all cell types. Branching children and initial nodes each required additional morphological features to predict diameter, most commonly path length to soma and longest path to terminal end. Model simulations reveal that membrane potential response with predicted diameters was similar to the original response for several tested morphologies. Predictions that use the original diameter of initial nodes improved the membrane potential response, so that the difference from the simulations of the original morphology was reduced to an average of 20%. We provide our open source software to extend the utility of available NeuroMorpho.org morphologies, and suggest predictive equations may supplement morphologies that lack dendritic diameter and improve model simulations with realistic dendritic diameter.

中文翻译：

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从形态学预测神经直径以实现准确模拟

准确的神经元形态对于真实神经反应的计算模型模拟至关重要。在过去十年中，在线存储库 NeuroMorpho.Org 收集了超过 140,000 个可用的神经元形态，以了解大脑功能并促进实验和计算研究之间的相互作用。神经元形态描述了神经结构的空间方面；然而，许多可用的形态不包含对电活动的计算模拟必不可少的准确直径。为了最好地利用可用的神经元形态，我们提出了一组从其他形态特征预测树突直径的方程。为了推导出方程，我们使用了一组具有真实神经元直径的 NeuroMorpho.org 档案，代表海马锥体、小脑浦肯野、纹状体棘突投射神经元 (SPN)。每个形态分为初始节点、分支子节点和连续节点。我们的分析表明，对于所有细胞类型，先前节点的直径，即父直径，与后续节点的直径相关。分支子节点和初始节点都需要额外的形态特征来预测直径，最常见的是到体细胞的路径长度和到末端的最长路径。模型模拟表明，具有预测直径的膜电位响应与几种测试形态的原始响应相似。使用初始节点的原始直径的预测改进了膜电位响应，因此与原始形态模拟的差异减少到平均 20%。