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Multiple Tests Based on a Gaussian Approximation of the Unitary Events Method with Delayed Coincidence Count

机译:基于一元事件方法的高斯近似的重合计数延迟的多重检验

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摘要

The unitary events (UE) method is one of the most popular and efficient methods used over the past decade to detect patterns of coincident joint spike activity among simultaneously recorded neurons. The detection of coincidences is usually based on binned coincidence count (Grün, 1996), which is known to be subject to loss in synchrony detection (Grün, Diesmann, Grammont, Riehle, & Aertsen, 1999). This defect has been corrected by the multiple shift coincidence count (Grün et al., 1999). The statistical properties of this count have not been further investigated until this work, the formula being more difficult to deal with than the original binned count. First, we propose a new notion of coincidence count, the delayed coincidence count, which is equal to the multiple shift coincidence count when discretized point processes are involved as models for the spike trains. Moreover, it generalizes this notion to nondiscretized point processes, allowing us to propose a new gaussian approximation of the count. Since unknown parameters are involved in the approximation, we perform a plug-in step, where unknown parameters are replaced by estimated ones, leading to a modification of the approximating distribution. Finally the method takes the multiplicity of the tests into account via a Benjamini and Hochberg approach (Benjamini & Hochberg, 1995), to guarantee a prescribed control of the false discovery rate. We compare our new method, MTGAUE (multiple tests based on a gaussian approximation of the unitary events) and the UE method proposed in Grün et al. (1999) over various simulations, showing that MTGAUE extends the validity of the previous method. In particular, MTGAUE is able to detect both profusion and lack of coincidences with respect to the i- dependence case and is robust to changes in the underlying model. Furthermore MTGAUE is applied on real data.
机译:单一事件(UE)方法是过去十年中用于检测同时记录的神经元中同时发生的突波活动模式的最流行和最有效的方法之一。巧合的检测通常基于合并的巧合计数(Grün, 1996 ),众所周知,同步计数可能会丢失(Grün, Diesmann,Grammont,Riehle和Aertsen, 1999 )。此缺陷已通过多次移位同时计数得到纠正(Grün等人, 1999 )。在进行这项工作之前,尚未对该计数的统计属性进行进一步研究,该公式比原始的合并计数更难处理。首先,我们提出了一种新的重合计数概念,即延迟重合计数,当包含离散点过程作为尖峰序列模型时,它等于多次移位重合计数。而且,它将这个概念推广到非离散点过程,从而使我们能够提出一个新的计数高斯近似。由于未知参数包含在近似中,因此我们执行了一个插入步骤,其中未知参数被估计的参数替换,从而导致近似分布的修改。最终,该方法通过Benjamini和Hochberg方法(Benjamini&Hochberg, 1995 )考虑了测试的多样性,以确保对错误发现率。我们比较了我们的新方法MTGAUE(基于单一事件的高斯近似的多次测试)和Grün等人提出的UE方法。 ( 1999 )在各种模拟中的表现,表明MTGAUE扩展了先前方法的有效性。特别是,MTGAUE能够检测到i依赖情况下的大量信息和缺乏巧合,并且对基础模型的更改具有鲁棒性。此外,MTGAUE应用于实际数据。

著录项

  • 来源
    《Neural computation》 |2014年第7期|1408-1454|共47页
  • 作者单位

    Universit?? Nice Sophia Antipolis, CNRS, LJAD, UMR 7351, 06100 Nice, France malot@unice.fr;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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