Department of Neurobiology, Hebrew University of Jerusalem, 9190401, Jerusalem, Israel.
Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202, Geneva, Switzerland.
Nat Commun. 2020 Jan 15;11(1):288. doi: 10.1038/s41467-019-13932-6.
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.
详细的基于电导的非线性神经元模型由数千个突触组成，对于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至关重要。这些模型的模拟计算量非常大，大大限制了它们的实用性。Neuron_Reduce 是一种新的分析方法，可以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保留它们向胞体传递阻抗的简化模型中；具有相同传递阻抗的突触被合并为一个 NEURON 过程，仍然保留它们各自的激活时间。对于各种细胞类型和真实数量（10,000-100,000）的突触，Neuron_Reduce 将模拟加速 40-250 倍，同时紧密复制电压动态和特定的树突计算。简化后的神经元模型将能够以前所未有的规模对神经网络进行逼真的模拟，包括来自微观连接组学研究和受生物启发的“深度网络”的网络。Neuron_Reduce 是公开可用的，并且易于实现。
