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Modeling the Correlated Activity of Neural Populations: Areview

机译：建模的神经人口相关活动：审查。

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摘要

The principles of neural encoding and computations are inherently collective and usually involve large populations of interacting neurons with highly correlated activities. While theories of neural function have long recognized the importance of collective effects in populations of neurons, only in the past two decades has it become possible to record from many cells simultaneously using advanced experimental techniques with single-spike resolution and to relate these correlations to function and behavior. This review focuses on the modeling and inference approaches that have been recently developed to describe the correlated spiking activity of populations of neurons. We cover a variety of models describing correlations between pairs of neurons, as well as between larger groups, synchronous or delayed in time, with or without the explicit influence of the stimulus, and including or not latent variables. We discuss the advantages and drawbacks or each method, as well as the computational challenges related to their application to recordings of ever larger populations.
机译：神经编码和计算的原理本质上是集体的，通常涉及具有高度相关活动的大量交互神经元。尽管神经功能理论早已认识到集体效应对神经元群体的重要性，但仅在过去的二十年中，才有可能使用先进的实验技术以单峰分辨同时记录许多细胞并将这些相关性与功能联系起来。和行为。这篇综述着重于建模和推理方法，这些方法和方法最近已被开发用来描述神经元群体的相关峰值活动。我们涵盖了各种模型，这些模型描述了成对的神经元之间以及时间上同步或延迟的大组神经元之间的相关性，无论是否受到刺激的显式影响，包括或不包括潜在变量。我们讨论了每种方法的优缺点，以及与它们在越来越大的人口记录中的应用有关的计算挑战。

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  • 来源
    《Neural computation》 |2019年第2期|233–269|共37页
  • 作者

    Gardella Christophe; Marre Olivier; Mora Thierry;

  • 作者单位

    Univ Paris Diderot, Sorbonne Univ, CNRS, Lab Phys Stat, F-75005 Paris, France|Ecole Normale Super, F-75005 Paris, France|CNRS, INSERM, Inst Vis, F-75012 Paris, France|Sorbonne Univ, F-75012 Paris, France;

    CNRS, INSERM, Inst Vis, F-75012 Paris, France|Sorbonne Univ, F-75012 Paris, France;

    Univ Paris Diderot, Sorbonne Univ, CNRS, Lab Phys Stat, F-75005 Paris, France|Ecole Normale Super, F-75005 Paris, France;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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