神经编码
编码(社会科学)
预测编码
计算机科学
统计假设检验
感觉系统
人工智能
统计模型
代表(政治)
信息论
统计分析
机器学习
神经科学
心理学
统计
数学
法学
政治
政治学
作者
Eero P. Simoncelli,Bruno A. Olshausen
出处
期刊:Annual Review of Neuroscience
[Annual Reviews]
日期:2001-03-01
卷期号:24 (1): 1193-1216
被引量:2328
标识
DOI:10.1146/annurev.neuro.24.1.1193
摘要
▪ 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.
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