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Goodness-of-Fit Tests and Nonparametric Adaptive Estimation for Spike Train Analysis.
The Journal of Mathematical Neuroscience ( IF 2.3 ) Pub Date : 2014-04-17 , DOI: 10.1186/2190-8567-4-3
Patricia Reynaud-Bouret 1 , Vincent Rivoirard , Franck Grammont , Christine Tuleau-Malot
Affiliation  

When dealing with classical spike train analysis, the practitioner often performs goodness-of-fit tests to test whether the observed process is a Poisson process, for instance, or if it obeys another type of probabilistic model (Yana et al. in Biophys. J. 46(3):323-330, 1984; Brown et al. in Neural Comput. 14(2):325-346, 2002; Pouzat and Chaffiol in Technical report, http://arxiv.org/abs/arXiv:0909.2785, 2009). In doing so, there is a fundamental plug-in step, where the parameters of the supposed underlying model are estimated. The aim of this article is to show that plug-in has sometimes very undesirable effects. We propose a new method based on subsampling to deal with those plug-in issues in the case of the Kolmogorov-Smirnov test of uniformity. The method relies on the plug-in of good estimates of the underlying model that have to be consistent with a controlled rate of convergence. Some nonparametric estimates satisfying those constraints in the Poisson or in the Hawkes framework are highlighted. Moreover, they share adaptive properties that are useful from a practical point of view. We show the performance of those methods on simulated data. We also provide a complete analysis with these tools on single unit activity recorded on a monkey during a sensory-motor task.Electronic Supplementary MaterialThe online version of this article (doi:10.1186/2190-8567-4-3) contains supplementary material.

中文翻译:

用于尖峰训练分析的拟合优度测试和非参数自适应估计。

在处理经典尖峰序列分析时,从业者通常会执行拟合优度测试以测试观察到的过程是否是泊松过程,或者它是否遵循另一种类型的概率模型(Yana 等人在 Biophys. J 中) . 46(3):323-330, 1984; Brown et al. in Neural Comput. 14(2):325-346, 2002; Pouzat and Chaffiol in Technical report, http://arxiv.org/abs/arXiv: 0909.2785, 2009)。在这样做时,有一个基本的插件步骤,其中估计假设的基础模型的参数。本文的目的是表明插件有时会产生非常不受欢迎的效果。在 Kolmogorov-Smirnov 一致性检验的情况下,我们提出了一种基于子采样的新方法来处理这些插件问题。该方法依赖于基础模型的良好估计插件,该插件必须与受控收敛速度一致。突出显示了满足泊松或霍克斯框架中这些约束的一些非参数估计。此外,它们共享从实用的角度来看有用的自适应特性。我们展示了这些方法在模拟数据上的性能。我们还使用这些工具对在感觉运动任务期间记录在猴子身上的单个单元活动进行了完整分析。电子补充材料本文的在线版本 (doi:10.1186/2190-8567-4-3) 包含补充材料。它们共享从实际角度来看有用的自适应特性。我们展示了这些方法在模拟数据上的性能。我们还使用这些工具对在感觉运动任务期间记录在猴子身上的单个单元活动进行了完整分析。电子补充材料本文的在线版本 (doi:10.1186/2190-8567-4-3) 包含补充材料。它们共享从实际角度来看有用的自适应特性。我们展示了这些方法在模拟数据上的性能。我们还使用这些工具对在感觉运动任务期间记录在猴子身上的单个单元活动进行了完整分析。电子补充材料本文的在线版本 (doi:10.1186/2190-8567-4-3) 包含补充材料。
更新日期:2019-11-01
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