Cited by 2,460Visual perception and processing mechanismsNeural dynamics and brain functionFace Recognition and Perception

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Natural Image Statistics and Neural Representation

Simoncelli, Eero P, Olshausen, Bruno A·2001·Annual Review of Neuroscience

DOI 10.1146/annurev.neuro.24.1.1193

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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.

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A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,324
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Self-citation
Sparse coding with an overcomplete basis set: A strategy employed by V1?
Olshausen, B. A., & Field, D. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00169-7 · Cited by 3,642
Self-citation
A Fast Fixed-Point Algorithm for Independent Component Analysis
Hyvärinen, A., & Erkki Oja, A. (1997) · Neural Computation · doi:10.1162/neco.1997.9.7.1483 · Cited by 3,373
Relations between the statistics of natural images and the response properties of cortical cells
Field, D. J. (1987) · Journal of the Optical Society of America A · doi:10.1364/josaa.4.002379 · Cited by 3,241
Some informational aspects of visual perception.
Attneave, F. (1954) · Psychological Review · doi:10.1037/h0054663 · Cited by 2,938
Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture
Jutten, C., & Hérault, J. (1991) · Signal Processing · doi:10.1016/0165-1684(91)90079-x · Cited by 2,550
The “independent components” of natural scenes are edge filters
Bell, A. J., & Sejnowski, T. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00121-1 · Cited by 2,201
Normalization of cell responses in cat striate cortex
Heeger, D. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800009640 · Cited by 1,835

Show all 87 references

Research works printed but not linked: 10

Barlow HB. 1961. Possible principles underlying the transformation of sensory messages. In Sensory Communication , ed. WA Rosenblith, pp. 217–34. Cambridge, MA: MIT Press
Bergen JR, 1986, J. Opt. Soc. Am. A, 3, 99
10.1088/0954-898X/6/3/003 · DOI
Jutten C, 1991, Signal Process, 24, 1
Lee AB, Mumford D. 1999. An occlusion model generating scale-invariant images. In IEEE Workshop on Statistical and Computational Theories of Vision , Fort Collins, CO. Also athttp://www.cis.ohiostate.edu/∼szhu/SCTV99.html
Lennie P, 1988, CRC Crit. Rev. Neurobiol., 3, 333
Penev P, Gegiu M, Kaplan E. 2000. Fast convergent factorial learning of the low-dimensional independent manifolds in optical imaging data. In Proc. 2nd Int. Workshop Indep. Comp. Anal. Signal Separation , pp. 133–38. Helsinki, Finland
10.1088/0954-898X/10/4/304 · DOI
Simoncelli EP, Schwartz O. 1999. Image statistics and cortical normalization models. In Advances in Neural Information Processing Systems , ed. MS Kearns, SA Solla, DA Cohn. 11:153–59
Zhu SC, 1998, Int. J. Comp. Vis., 27, 1

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Authors: Simoncelli, E. P., & Olshausen, B. A.
Affiliations: Howard Hughes Medical Institute (US)
University of California, Davis (US)
ORCID: Simoncelli, Eero P
Olshausen, Bruno A
Venue: Annual Review of Neuroscience
Publisher: Annual Reviews
Published: 2001-03
Volume: 24
Issue: 1
Pages: 1193-1216
ISSN: 0147-006X (ISSN-L)
1545-4126
DOI: 10.1146/annurev.neuro.24.1.1193
OpenAlex: W2151035455
Cited by: 2,460
References: 87
Citations by year: 2026: 99 · 2025: 135 · 2024: 114 · 2023: 130 · 2022: 117 · 2021: 133 · 2020: 108 · 2019: 136 · 2018: 107 · 2017: 98 · 2016: 118 · 2015: 110 · 2014: 122 · 2013: 124 · 2012: 93
FWCI: 4.19
Type: journal article
Language: en
Open access: closed
Locations: Annual Review of Neuroscience (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health (5r29mh057921-05)
National Institute of Mental Health (R29-MH57921)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
Keywords: natural image statistics, efficient coding hypothesis, coding efficiency, neural representations, sensory adaptation, information theory, population coding, visual neurons
MeSH terms: Animals, Brain Mapping, Environment, Humans, Image Processing, Computer-Assisted, Neurons, Pattern Recognition, Visual, Visual Cortex, Visual Perception

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