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Is realistic neuronal modeling realistic?

机译：现实的神经元建模现实吗？

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Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models.
机译：科学模型是旨在解释自然现象的抽象。一个成功的模型显示了相对现象如何从相对简单的原理中产生出来，同时保留了主要的物理或生物学规则并预测了新颖的实验。模型不应该成为现实的复制品。它有助于理解它。与这个基本前提相反，在21世纪，对生物过程进行详细建模的计算工作激增。在这里，我们讨论了单个神经元的mormoronic现实建模。这一迅速发展的领域是由发现某些神经元不仅将其输入求和并在总和超过某个阈值时触发而发动的。因此，研究人员询问了单个神经元的计算能力是什么，并试图使用现实模型给出答案。我们简要回顾了区室建模的最新技术，重点介绍了最新进展和内在缺陷。然后，我们尝试解决两个基本问题。实际上，我们可以现实地对单个神经元建模吗？从哲学上讲，我们应该现实地对单个神经元建模吗？我们使用第5层新皮质锥体神经元作为测试案例来研究这些问题。我们对第5层锥体神经元的三个公开可用模型进行了三个简单的计算挑战。根据它们的性能和对已发布模型的部分调查，我们得出的结论是，当前的分区模型是临时的，不现实的模型，一旦它们超出了设计的特定问题，它们的功能就会很差。然后，我们尝试绘制可能的路径以生成现实的单个神经元模型。

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