Prominent models of spike trains assume only one source of variability – stochastic (Poisson) spiking – when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability. When applied to recordings from macaque premotor cortex, we find that time warping considerably improves predictions of neural activity. We suggest that such temporal variability is a widespread phenomenon in the brain which should be modeled.

中文翻译：

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神经活动的线性-非线性时间-时间-泊松模型。

在固定刺激和行为的情况下，尖峰火车的突出模型仅假设了可变性的一个来源-随机（泊松）尖峰。但是，由于诸如计划之类的内部过程，峰值列车也可能反映出可变性。例如，我们可以计划某个时间点的运动，并在以后任意任意时间执行它。因此，参与计划的神经元可能会共享未精确锁定到实际运动的基本时程。在这里，我们将标准的线性-非线性-泊松（LNP）模型与动态时间规整（DTW）结合起来以解决共享的时间可变性。当将其应用于猕猴前运动皮层的记录时，我们发现时间扭曲大大改善了神经活动的预测。我们建议这种时间变异性是大脑中普遍存在的现象，应该对其进行建模。