Ghent University, Electronics and Information Systems Department, 9000 Ghent, Belgium.
Neural Comput. 2010 Jun;22(6):1468-72. doi: 10.1162/neco.2010.07-09-1070.
Recently van Elburg and van Ooyen (2009) published a generalization of the event-based integration scheme for an integrate-and-fire neuron model with exponentially decaying excitatory currents and double exponential inhibitory synaptic currents, introduced by Carnevale and Hines. In the paper, it was shown that the constraints on the synaptic time constants imposed by the Newton-Raphson iteration scheme, can be relaxed. In this note, we show that according to the results published in D'Haene, Schrauwen, Van Campenhout, and Stroobandt (2009), a further generalization is possible, eliminating any constraint on the time constants. We also demonstrate that in fact, a wide range of linear neuron models can be efficiently simulated with this computation scheme, including neuron models mimicking complex neuronal behavior. These results can change the way complex neuronal spiking behavior is modeled: instead of highly nonlinear neuron models with few state variables, it is possible to efficiently simulate linear models with a large number of state variables.
最近，van Elburg 和 van Ooyen（2009）发表了一篇综述，对 Carnevale 和 Hines 提出的具有指数衰减兴奋性电流和双指数抑制性突触电流的积分-点火神经元模型的基于事件的积分方案进行了推广。在该文中，作者表明可以放宽由牛顿-拉普森迭代方案施加在突触时间常数上的约束。在本注记中，我们表明根据 D'Haene、Schrauwen、Van Campenhout 和 Stroobandt（2009）发表的结果，还可以进一步推广，消除对时间常数的任何约束。我们还证明，实际上，这种计算方案可以有效地模拟广泛的线性神经元模型，包括模拟复杂神经元行为的神经元模型。这些结果可能会改变复杂神经元放电行为建模的方式：不再使用具有少数状态变量的高度非线性神经元模型，而是可以有效地模拟具有大量状态变量的线性模型。
