Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these models are computationally expensive, considerably curtailing their utility. Neuron_Reduce is a new analytical approach to reduce the morphological complexity and computational time of nonlinear neuron models. Synapses and active membrane channels are mapped to the reduced model preserving their transfer impedance to the soma; synapses with identical transfer impedance are merged into one NEURON process still retaining their individual activation times. Neuron_Reduce accelerates the simulations by 40-250 folds for a variety of cell types and realistic number (10,000-100,000) of synapses while closely replicating voltage dynamics and specific dendritic computations. The reduced neuron-models will enable realistic simulations of neural networks at unprecedented scale, including networks emerging from micro-connectomics efforts and biologically-inspired "deep networks". Neuron_Reduce is publicly available and is straightforward to implement.

中文翻译：

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详细非线性神经元模型的有效分析简化。

由数千个突触组成的详细的基于电导的非线性神经元模型是理解单个神经元和大型神经元网络的计算特性以及解释实验结果的关键。这些模型的模拟计算成本很高，大大削弱了它们的实用性。Neuron_Reduce 是一种新的分析方法，可降低非线性神经元模型的形态复杂性和计算时间。突触和活性膜通道被映射到简化模型，保留它们到体体的传输阻抗；具有相同转移阻抗的突触被合并到一个神经元过程中，仍然保留它们各自的激活时间。Neuron_Reduce 将各种细胞类型和实际数量 (10,000-100,000) 突触的模拟速度加快 40-250 倍，同时紧密复制电压动态和特定树突计算。简化的神经元模型将能够以前所未有的规模对神经网络进行真实模拟，包括从微连接组学工作中出现的网络和受生物学启发的“深层网络”。Neuron_Reduce 是公开可用的并且易于实施。