Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and JARA Jülich, Germany.
Front Comput Neurosci. 2013 Oct 23;7:132. doi: 10.3389/fncom.2013.00132. eCollection 2013.
We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of their signatures, defined as the pairs of pattern size z and support c. Here, we derive in detail a statistical test for the significance of the signatures under the null hypothesis of full independence (pattern spectrum filtering, PSF) by means of surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected, yielding a low false negative rate. However, this approach is prone to additionally classify patterns resulting from chance overlap of real assembly activity and background spiking as significant. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains.
我们最近提出了频繁项集挖掘（FIM）作为一种方法，用于在大规模并行尖峰火车中执行模式的同步尖峰（项集）的优化搜索。此搜索输出不能由任何超集（封闭频繁项集）的计数简单解释的单个模式的出现计数（支持）。FIM 找到的模式数量使得直接进行统计测试变得不可行，因为多重测试非常严重。为了克服这个问题，我们提议不测试单个模式的显著性，而是测试它们的特征，这些特征定义为模式大小 z 和支持 c 的对。在这里，我们通过替代数据详细推导了在完全独立的零假设（模式谱过滤，PSF）下特征的显著性的统计检验。结果，注入的尖峰模式很好地模拟了集合活动，从而产生了低的假阴性率。然而，这种方法容易将真实集合活动和背景尖峰之间由于偶然重叠而产生的模式错误地分类为显著模式。这些模式对于给定特征嵌入在其他独立尖峰活动中的集合的零假设是假阳性。我们建议使用模式集减少（PSR）的附加方法通过条件过滤来消除这些假阳性。通过在神经元子集的注入尖峰同步形式下对并行尖峰火车的相关活动进行随机模拟，我们演示了在一系列参数设置下，由 FIM、PSF 和 PSR 组成的分析方案允许在大规模并行尖峰火车中可靠地检测活动集合。
