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High-dimensional geometry of population responses in visual cortex

机译:视觉皮层中种群反应的高维几何

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摘要

A neuronal population encodes information most efficiently when its stimulus responses are high-dimensional and uncorrelated, and most robustly when they are lower-dimensional and correlated. Here we analysed the dimensionality of the encoding of natural images by large populations of neurons in the visual cortex of awake mice. The evoked population activity was high-dimensional, and correlations obeyed an unexpected power law: the nth principal component variance scaled as 1. This scaling was not inherited from the power law spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that if the variance spectrum was to decay more slowly then the population code could not be smooth, allowing small changes in input to dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness may represent a fundamental constraint that determines correlations in neural population codes.
机译:当神经元的刺激响应是高维且不相关时,其编码效率最高,而在低维和相关时则最有效。在这里,我们分析了清醒小鼠视觉皮层中大量神经元对自然图像编码的维数。诱发的种群活动是高维的,并且相关性服从意外的幂定律:第n个主成分方差定标为1 / n。这种缩放比例不是从自然图像的幂定律谱继承的,因为它在刺激白化后仍然存在。我们用数学方法证明了,如果方差谱衰减得更慢,则总体代码将变得不平滑,从而允许输入中的微小变化主导总体活动。该理论还预测了较低维刺激合奏的较大幂律指数,我们通过实验进行了验证。这些结果表明,编码平滑度可能代表确定神经种群编码中相关性的基本约束。

著录项

  • 来源
    《Nature》 |2019年第7765期|361-365|共5页
  • 作者单位

    HHMI Janelia Res Campus, Ashburn, VA 20147 USA|UCL, UCL Gatsby Computat Neurosci Unit, London, England;

    HHMI Janelia Res Campus, Ashburn, VA 20147 USA|UCL, UCL Inst Neurol, London, England;

    UCL, UCL Inst Neurol, London, England|Univ Washington, Dept Biol Struct, Seattle, WA 98195 USA;

    UCL, UCL Inst Ophthalmol, London, England;

    UCL, UCL Inst Neurol, London, England;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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