Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

中文翻译：

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相关神经群体中信息编码的几何。

大脑中的神经元在其集体活动中代表信息。这种神经种群代码的保真度取决于一个神经元的响应是否与其他神经元共享，以及如何共享。二十年的研究已经研究了这些噪声相关性对神经编码属性的影响。我们提供了有关该主题的理论发展的概述。使用简单，定性和一般性的论证，我们讨论，分类和关联了各种已发表的结果。我们强调噪声相关的精细结构的相关性，并提出了解决该问题的新方法。在整个审查过程中，我们强调了噪声相关性如何影响神经代码的几何图形。预期《神经科学年度评论》第44卷的最终最终在线发布日期是2021年7月。