Neuroscientists increasingly analyze the joint activity of multineuron recordings to identify population-level structures believed to be significant and scientifically novel. Claims of significant population structure support hypotheses in many brain areas. However, these claims require first investigating the possibility that the population structure in question is an expected byproduct of simpler features known to exist in data. Classically, this critical examination can be either intuited or addressed with conventional controls. However, these approaches fail when considering population data, raising concerns about the scientific merit of population-level studies. Here we develop a framework to test the novelty of population-level findings against simpler features such as correlations across times, neurons and conditions. We apply this framework to test two recent population findings in prefrontal and motor cortices, providing essential context to those studies. More broadly, the methodologies we introduce provide a general neural population control for many population-level hypotheses.

中文翻译：

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神经人口记录中的结构：简单现象的预期副产物？

神经科学家越来越多地分析多神经元记录的联合活动，以鉴定据信是重要的和科学新颖的种群水平结构。关于重要人口结构的说法支持许多大脑区域的假说。但是，这些主张首先需要调查以下可能性：所讨论的人口结构是已知存在于数据中的较简单特征的预期副产品。传统上，这种批判性检查既可以通过常规的控制方法进行直观检查，也可以通过常规控制方法解决。但是，这些方法在考虑人口数据时失败了，引起了对人口水平研究的科学价值的担忧。在这里，我们建立了一个框架，以比较简单的特征（例如跨时间，神经元和状况的相关性）来测试人群水平发现的新颖性。我们应用此框架来测试前额叶皮层和运动皮层中的两个最近的人口发现，为这些研究提供必要的背景。更广泛地说，我们介绍的方法为许多人口水平的假设提供了一般的神经人口控制。