Department of Physics, Ecole Normale Supérieure, 75005 Paris, France; email:
Annu Rev Neurosci. 2021 Jul 8;44:403-424. doi: 10.1146/annurev-neuro-120320-082744. Epub 2021 Apr 16.
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.
大脑中的神经元以其集体活动来表示信息。这种神经群体编码的保真度取决于一个神经元的响应变化是否以及如何与其他神经元共享。二十年来的研究已经调查了这些噪声相关性对神经编码特性的影响。我们提供了关于这个主题的理论发展的概述。使用简单、定性和一般的论点，我们讨论、分类和关联各种已发表的结果。我们强调噪声相关性的精细结构的相关性，并且提出了一种新的方法来解决这个问题。在整个综述中，我们强调了噪声相关性如何影响神经编码的几何图像。
