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比较替代物以精确评估高阶尖峰相关性。

Comparing Surrogates to Evaluate Precisely Timed Higher-Order Spike Correlations.

机构信息

Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany

Theoretical Systems Neurobiology, RWTH Aachen University, 52062 Aachen, Germany.

出版信息

eNeuro. 2022 Jun 9;9(3). doi: 10.1523/ENEURO.0505-21.2022. Print 2022 May-Jun.

Abstract

The generation of surrogate data, i.e., the modification of data to destroy a certain feature, can be considered as the implementation of a null-hypothesis whenever an analytical approach is not feasible. Thus, surrogate data generation has been extensively used to assess the significance of spike correlations in parallel spike trains. In this context, one of the main challenges is to properly construct the desired null-hypothesis distribution and to avoid altering the single spike train statistics. A classical surrogate technique is uniform dithering (UD), which displaces spikes locally and uniformly distributed, to destroy temporal properties on a fine timescale while keeping them on a coarser one. Here, we compare UD against five similar surrogate techniques in the context of the detection of significant spatiotemporal spike patterns. We evaluate the surrogates for their performance, first on spike trains based on point process models with constant firing rate, and second on modeled nonstationary artificial data to assess the potential detection of false positive (FP) patterns in a more complex and realistic setting. We determine which statistical features of the spike trains are modified and to which extent. Moreover, we find that UD fails as an appropriate surrogate because it leads to a loss of spikes in the context of binning and clipping, and thus to a large number of FP patterns. The other surrogates achieve a better performance in detecting precisely timed higher-order correlations. Based on these insights, we analyze experimental data from the pre-/motor cortex of macaque monkeys during a reaching-and-grasping task.

摘要

替代数据的生成,即通过修改数据来破坏特定特征,在分析方法不可行时,可以被视为零假设的实现。因此,替代数据生成已被广泛用于评估平行尖峰序列中尖峰相关性的显著性。在这种情况下,主要挑战之一是正确构建所需的零假设分布,并避免改变单个尖峰序列的统计数据。一种经典的替代技术是均匀抖动(UD),它局部且均匀地移动尖峰,以在精细时间尺度上破坏时间特性,同时在较粗时间尺度上保持它们。在这里,我们在检测显著时空尖峰模式的背景下,将 UD 与五种类似的替代技术进行比较。我们首先评估替代数据在基于恒定发射率的点过程模型的尖峰序列上的性能,其次评估在建模的非平稳人工数据上的性能,以评估在更复杂和现实的设置中检测假阳性(FP)模式的潜力。我们确定了哪些尖峰序列的统计特征被修改以及修改的程度。此外,我们发现 UD 不能作为合适的替代,因为它会导致在 binning 和 clipping 时丢失尖峰,从而导致大量 FP 模式。其他替代技术在检测精确定时的高阶相关性方面表现更好。基于这些见解,我们分析了猕猴在进行抓握任务期间的前/运动皮层的实验数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64d3/9186111/2cc7caa155c2/ENEURO.0505-21.2022_f001.jpg

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