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自然图像统计与神经表征。

Natural image statistics and neural representation.

作者信息

Simoncelli E P, Olshausen B A

机构信息

Howard Hughes Medical Institute, Center for Neural Science, and Courant Institute of Mathematical Sciences, New York University, New York, NY 10003. USA.

出版信息

Annu Rev Neurosci. 2001;24:1193-216. doi: 10.1146/annurev.neuro.24.1.1193.

Abstract

It has long been assumed that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical properties of the signals to which they are exposed. Attneave (1954)Barlow (1961) proposed that information theory could provide a link between environmental statistics and neural responses through the concept of coding efficiency. Recent developments in statistical modeling, along with powerful computational tools, have enabled researchers to study more sophisticated statistical models for visual images, to validate these models empirically against large sets of data, and to begin experimentally testing the efficient coding hypothesis for both individual neurons and populations of neurons.

摘要

长期以来,人们一直认为,感觉神经元通过进化和发育过程,适应了它们所接触信号的统计特性。阿特尼夫(1954年)和巴洛(1961年)提出,信息论可以通过编码效率的概念,在环境统计与神经反应之间建立联系。统计建模的最新进展,以及强大的计算工具,使研究人员能够研究更复杂的视觉图像统计模型,根据大量数据对这些模型进行实证验证,并开始通过实验测试单个神经元和神经元群体的有效编码假说。

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