The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
Natural Image Statistics and Neural Representation
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“These include components for sensory coding29,30, normalization31, working memory32, evidence accumulation and decision mechanisms33–35, and motor control36.”
From Cognitive computational neuroscience (2018) · cites this paper as [30]
Citations
Cites
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-citationSparse 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-citationA 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
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
Portilla, J., & Simoncelli, E. P. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026553619983 · Cited by 1,806
Self-citationOptimal unsupervised learning in a single-layer linear feedforward neural network
Sanger, T. D. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90044-0 · Cited by 1,527
Perception as Bayesian Inference
(1996) · doi:10.1017/cbo9780511984037 · Cited by 1,425
Sparse Coding and Decorrelation in Primary Visual Cortex During Natural Vision
Vinje, W. E., & Gallant, J. L. (2000) · Science · doi:10.1126/science.287.5456.1273 · Cited by 1,286
Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995) · Neural Computation · doi:10.1162/neco.1995.7.5.889 · Cited by 1,207
What Is the Goal of Sensory Coding?
Field, D. J. (1994) · Neural Computation · doi:10.1162/neco.1994.6.4.559 · Cited by 1,183
Pyramid-based texture analysis/synthesis
Heeger, D. J., & Bergen, J. R. (1995) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/218380.218446 · Cited by 1,134
Independent component filters of natural images compared with simple cells in primary visual cortex
van Hateren, J. H., & van der Schaaf, A. (1998) · Proceedings of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rspb.1998.0303 · Cited by 1,106
Predictive coding: a fresh view of inhibition in the retina
Srinivasan, M. V., Laughlin, S. B., & Dubs, A. (1982) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1982.0085 · Cited by 1,042
A Simple Coding Procedure Enhances a Neuron's Information Capacity
Laughlin, S. B. (1981) · Zeitschrift für Naturforschung C · doi:10.1515/znc-1981-9-1040 · Cited by 1,015
Bialek, W., Rieke, F. M., van Steveninck, R. R. D. R., & Warland, D. K. (1991) · Science · doi:10.1126/science.2063199 · Cited by 1,001
Statistics of natural images: Scaling in the woods
Ruderman, D., & Bialek, W. (1994) · Physical Review Letters · doi:10.1103/physrevlett.73.814 · Cited by 936
Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex
Carandini, M., Heeger, D. J., & Movshon, J. A. (1997) · Journal of Neuroscience · doi:10.1523/jneurosci.17-21-08621.1997 · Cited by 926
Julesz, B. (1962) · IEEE Transactions on Information Theory · doi:10.1109/tit.1962.1057698 · Cited by 895
Evaluation of linear models of surface spectral reflectance with small numbers of parameters
Maloney, L. T. (1986) · Journal of the Optical Society of America A · doi:10.1364/josaa.3.001673 · Cited by 752
Statistics of cone responses to natural images: implications for visual coding
Ruderman, D., Cronin, T. W., & Chiao, C. (1998) · Journal of the Optical Society of America A · doi:10.1364/josaa.15.002036 · Cited by 726
Where do we Stand on Maximum Entropy
Jaynes, E. T. (1979) · Medical Entomology and Zoology · Cited by 706
What Does the Retina Know about Natural Scenes?
Atick, J. J., & Redlich, A. (1992) · Neural Computation · doi:10.1162/neco.1992.4.2.196 · Cited by 706
Could information theory provide an ecological theory of sensory processing?
Atick, J. J. (2011) · Network: Computation in Neural Systems · doi:10.3109/0954898x.2011.638888 · Cited by 679
Edge co-occurrence in natural images predicts contour grouping performance
Geisler, W. S., Perry, J. S., Super, B. J., & Gallogly, D. P. (2001) · Vision Research · doi:10.1016/s0042-6989(00)00277-7 · Cited by 678
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
Zhu, S. C., Wu, Y., & Mumford, D. B. (1998) · International Journal of Computer Vision · doi:10.1023/a:1007925832420 · Cited by 659
Adaptive Rescaling Maximizes Information Transmission
Brenner, N., Bialek, W., & van Steveninck, R. R. D. R. (2000) · Neuron · doi:10.1016/s0896-6273(00)81205-2 · Cited by 650
Source separation using higher order moments
Cardoso, J.-F. (2003) · International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1989.266878 · Cited by 565
Adaptation of retinal processing to image contrast and spatial scale
Smirnakis, S. M., Berry, M. J., Warland, D. K., Bialek, W., & Meister, M. (1997) · Nature · doi:10.1038/386069a0 · Cited by 548
Forming sparse representations by local anti-Hebbian learning
Földiák, P. (1990) · Biological Cybernetics · doi:10.1007/bf02331346 · Cited by 541
Hyvärinen, A., & Hoyer, P. O. (2000) · Neural Computation · doi:10.1162/089976600300015312 · Cited by 534
Image compression via joint statistical characterization in the wavelet domain
Buccigrossi, R. W., & Simoncelli, E. P. (1999) · IEEE Transactions on Image Processing · doi:10.1109/83.806616 · Cited by 524
Self-citationTrichromacy, opponent colours coding and optimum colour information transmission in the retina
Buchsbaum, G., & Gottschalk, A. (1983) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1983.0090 · Cited by 513
Attias, H. T. (1999) · Neural Computation · doi:10.1162/089976699300016458 · Cited by 498
Adaptation-Induced Plasticity of Orientation Tuning in Adult Visual Cortex
Dragoi, V., Sharma, J., & Sur, M. (2000) · Neuron · doi:10.1016/s0896-6273(00)00103-3 · Cited by 495
Bonds, A. B. (1989) · Visual Neuroscience · doi:10.1017/s0952523800004314 · Cited by 477
Dan, Y., Atick, J. J., & Reid, R. C. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-10-03351.1996 · Cited by 476
Rapid Adaptation in Visual Cortex to the Structure of Images
Müller, J. R., Metha, A. B., Krauskopf, J., & Lennie, P. (1999) · Science · doi:10.1126/science.285.5432.1405 · Cited by 458
Statistics of natural time-varying images
Dong, D. W., & Atick, J. J. (1995) · Network Computation in Neural Systems · doi:10.1088/0954-898x_6_3_003 · Cited by 457
Natural scene statistics at the centre of gaze
Reinagel, P., & Zador, A. M. (1999) · Network Computation in Neural Systems · doi:10.1088/0954-898x_10_4_304 · Cited by 439
Responses of neurons in primary and inferior temporal visual cortices to natural scenes
Baddeley, R. J., Abbott, L. F., Booth, M. C. A., Sengpiel, F., Freeman, T., Wakeman, E. A., & Rolls, E. T. (1997) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1997.0246 · Cited by 434
Origins of scaling in natural images
Ruderman, D. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00008-4 · Cited by 363
Rieke, F. M., Bodnar, D. A., & Bialek, W. (1995) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1995.0204 · Cited by 317
On a common circle: Natural scenes and Gestalt rules
Sigman, M., Cecchi, G., Gilbert, C. D., & Magnasco, M. O. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.4.1935 · Cited by 314
Probabilistic framework for the adaptation and comparison of image codes
Lewicki, M. S., & Olshausen, B. A. (1999) · Journal of the Optical Society of America A · doi:10.1364/josaa.16.001587 · Cited by 307
Self-citationLennie, P., & D’Zmura, M. (1988) · PubMed · Cited by 291
Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex
Rao, R. P. N., & Ballard, D. H. (1997) · Neural Computation · doi:10.1162/neco.1997.9.4.721 · Cited by 290
Statistical models for images: compression, restoration and synthesis
Simoncelli, E. P. (2002) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.1997.680530 · Cited by 273
Self-citationA theory of maximizing sensory information
van Hateren, J. H. (1992) · Biological Cybernetics · doi:10.1007/bf00203134 · Cited by 267
Adaptation and decorrelation in the cortex
Barlow, H. B., & Földiák, P. (1989) · Addison-Wesley Longman Publishing Co., Inc. eBooks · Cited by 263
Figure-ground discrimination by relative movement in the visual system of the fly
Reichardt, W., & Poggio, T. (1979) · Biological Cybernetics · doi:10.1007/bf00337434 · Cited by 258
Could information theory provide an ecological theory of sensory processing?
Atick, J. J. (1992) · Network Computation in Neural Systems · doi:10.1088/0954-898x/3/2/009 · Cited by 234
The principal components of natural images
Hancock, P., Baddeley, R., & Smith, L. S. (1992) · Network Computation in Neural Systems · doi:10.1088/0954-898x_3_1_008 · Cited by 231
Visual adaptation as optimal information transmission
Wainwright, M. J. (1999) · Vision Research · doi:10.1016/s0042-6989(99)00101-7 · Cited by 216
The `Ideal Homunculus': decoding neural population signals
Oram, M. W., Földiák, P., Perrett, D. I., Oram, M. W., & Sengpiel, F. (1998) · Trends in Neurosciences · doi:10.1016/s0166-2236(97)01216-2 · Cited by 215
Cortical neurons: Isolation of contrast gain control
Geisler, W. S., & Albrecht, D. G. (1992) · Vision Research · doi:10.1016/0042-6989(92)90196-p · Cited by 211
Human discrimination of fractal images
Knill, D. C., Field, D., & Kerstent, D. (1990) · Journal of the Optical Society of America A · doi:10.1364/josaa.7.001113 · Cited by 193
Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus
Dong, D. W., & Atick, J. J. (1995) · Network Computation in Neural Systems · doi:10.1088/0954-898x_6_2_003 · Cited by 183
Spatiotemporal contrast sensitivity of early vision
van Hateren, J. H. (1993) · Vision Research · doi:10.1016/0042-6989(93)90163-q · Cited by 160
The human visual system is optimised for processing the spatial information in natural visual images
Párraga, C. A., Troscianko, T., & Tolhurst, D. J. (2000) · Current Biology · doi:10.1016/s0960-9822(99)00262-6 · Cited by 156
Entropy reduction and decorrelation in visual coding by oriented neural receptive fields
Daugman, J. G. (1989) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.16456 · Cited by 152
Human colour perception and its adaptation
Webster, M. A. (1996) · Network Computation in Neural Systems · doi:10.1088/0954-898x_7_4_002 · Cited by 145
Predictability and redundancy of natural images
Kersten, D. (1987) · Journal of the Optical Society of America A · doi:10.1364/josaa.4.002395 · Cited by 143
Wainwright, M. J., Schwartz, O., & Simoncelli, E. P. (2002) · Cited by 141
Self-citationWhat is the Goal of Sensory Coding?
Field, D. J. (1999) · The MIT Press eBooks · doi:10.7551/mitpress/7011.003.0008 · Cited by 134
Pattern adaptation and cross-orientation interactions in the primary visual cortex
Carandini, M., Movshon, J. A., & Ferster, D. (1998) · Neuropharmacology · doi:10.1016/s0028-3908(98)00069-0 · Cited by 130
Temporal Low-Order Statistics of Natural Sounds
Attias, H. T., & Schreiner, C. E. (1996) · Cited by 103
What does post-adaptation color appearance reveal about cortical color representation?
Atick, J. J., Zhaoping, L., & Redlich, A. (1993) · Vision Research · doi:10.1016/0042-6989(93)90065-5 · Cited by 94
Olshausen, B. A. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/5583.003.0018 · Cited by 89
Self-citationCoding Time-Varying Signals Using Sparse, Shift-Invariant Representations
Lewicki, M. S., & B, T. J. S. A. (1998) · Cited by 84
Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus
Dong, D. W., & Atick, J. J. (1995) · Network Computation in Neural Systems · doi:10.1088/0954-898x/6/2/003 · Cited by 72
On a common circle: Natural scenes and Gestalt rules
Sigman, M. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.031571498 · Cited by 71
Natural Image Statistics and Divisive Normalization
Wainwright, M. J., Schwartz, O., & Simoncelli, E. P. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/5583.003.0015 · Cited by 71
Self-citationChromaticity coordinates of frequency-limited functions
Buchsbaum, G., & Gottschalk, A. (1984) · Journal of the Optical Society of America A · doi:10.1364/josaa.1.000885 · Cited by 54
The principal components of natural images
Hancock, P., Baddeley, R., & Smith, L. S. (1992) · Network Computation in Neural Systems · doi:10.1088/0954-898x/3/1/008 · Cited by 52
Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1999) · The MIT Press eBooks · doi:10.7551/mitpress/7011.003.0017 · Cited by 49
The role of early retinal lateral inhibition: More than maximizing luminance information
Balboa, R. M., & Grzywacz, N. M. (2000) · Visual Neuroscience · doi:10.1017/s0952523800171081 · Cited by 45
BCM network develops orientation selectivity and ocular dominance in natural scene environment
Shouval, H. Z., Intratorj, N., & Cooper, L. N. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00087-4 · Cited by 39
Statistical dependence between orientation filter outputs used in a human-vision-based image code
Wegmann, B., & Zetzsche, C. (1990) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.24279 · Cited by 38
Coding of Naturalistic Stimuli by Auditory Midbrain Neurons
Attias, H. T., & Schreiner, C. E. (1997) · Cited by 38
Human colour perception and its adaptation
Webster, M. (1996) · Network Computation in Neural Systems · doi:10.1088/0954-898x/7/4/002 · Cited by 37
Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA
Hyvärinen, A., & Hoyer, P. O. (1999) · Cited by 34
Natural Sound Statistics and Divisive Normalization in the Auditory System
Schwartz, O., & Simoncelli, E. P. (2000) · Cited by 31
Self-citationDong, D. W., & Dong, D. W. (1994) · Cited by 16
Adaptive filter model for striate cortical processing of spatial information
Dagnelie, G. (1986) · Annual Meeting Optical Society of America · doi:10.1364/oam.1986.thn8 · Cited by 9
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
Cited by
Sorted by citations
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578
Image information and visual quality
Sheikh, H. R., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859378 · Cited by 4,104
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661
Wu, Y., & He, K. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01198-w · Cited by 3,601
Metadata
- Authors
- Simoncelli, E. P., & Olshausen, B. A.
- Affiliations
- Howard Hughes Medical Institute (US)
- University of California, Davis (US)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
