A general and efficient method for incorporating precise spike times in globally time-driven simulations.
A general and efficient method for incorporating precise spike times in globally time-driven simulations.
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DOI：
10.3389/fninf.2010.00113
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发表时间：
2010
影响因子：
3.5
通讯作者：
Diesmann M
中科院分区：
文献类型：
--
作者：
Hanuschkin A;Kunkel S;Helias M;Morrison A;Diesmann M
传统上，事件驱动的模拟已被限制到非常有限的一类神经元模型，未来尖峰的时间可以表示在封闭的形式。最近，这类模型是服从事件驱动的模拟已经扩展的技术的发展，以准确地计算发射时间的一些集成和发射神经元模型，不能够预测未来的尖峰在封闭的形式。这种发展的动机是普遍认为时间驱动的模拟是不精确的。在这里，我们证明了全局时间驱动方案可以计算出的触发时间与同一模型的事件驱动实现计算出的触发时间无法区分;此外，时间驱动方案的计算成本较低。关键的见解是，时间驱动的方法是基于识别最近的阈值交叉，这可以通过比预测事件驱动方法所需的未来阈值交叉的技术简单得多的算法来实现。由于运行时间主要取决于在每个传入尖峰执行的操作的成本，其中包括事件驱动模拟情况下的尖峰预测和时间驱动模拟情况下的回顾性检测，因此简单时间驱动算法优于事件驱动方法。此外，我们的方法通常适用于所有常用的集成和消防神经元模型，我们表明，采用标准的自适应求解器的非线性模型可以重现参考尖峰列车具有很高的精度。
Traditionally, event-driven simulations have been limited to the very restricted class of neuronal models for which the timing of future spikes can be expressed in closed form. Recently, the class of models that is amenable to event-driven simulation has been extended by the development of techniques to accurately calculate firing times for some integrate-and-fire neuron models that do not enable the prediction of future spikes in closed form. The motivation of this development is the general perception that time-driven simulations are imprecise. Here, we demonstrate that a globally time-driven scheme can calculate firing times that cannot be discriminated from those calculated by an event-driven implementation of the same model; moreover, the time-driven scheme incurs lower computational costs. The key insight is that time-driven methods are based on identifying a threshold crossing in the recent past, which can be implemented by a much simpler algorithm than the techniques for predicting future threshold crossings that are necessary for event-driven approaches. As run time is dominated by the cost of the operations performed at each incoming spike, which includes spike prediction in the case of event-driven simulation and retrospective detection in the case of time-driven simulation, the simple time-driven algorithm outperforms the event-driven approaches. Additionally, our method is generally applicable to all commonly used integrate-and-fire neuronal models; we show that a non-linear model employing a standard adaptive solver can reproduce a reference spike train with a high degree of precision.