Natural Image Statistics and Neural Representation Annual Review of Neuroscience, Vol. 24, No. 1. (2001), pp. 1193-1216, doi:10.1146/annurev.neuro.24.1.1193 by Eero P. Simoncelli, Bruno A. Olshausen
    abstract = {▪ 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.},
    address = {Howard Hughes Medical Institute, Center for Neural Science, and Courant Institute of Mathematical Sciences, New York University, New York, NY 10003. USA.},
    author = {Simoncelli, Eero P. and Olshausen, Bruno A.},
    doi = {10.1146/annurev.neuro.24.1.1193},
    issn = {0147-006X},
    journal = {Annual Review of Neuroscience},
    keywords = {development, information\_theory, saliency, visual, visual-processing},
    number = {1},
    pages = {1193--1216},
    pmid = {11520932},
    posted-at = {2012-04-10 08:11:20},
    priority = {2},
    title = {Natural Image Statistics and Neural Representation},
    url = {},
    volume = {24},
    year = {2001}

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