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

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

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

DOI 10.1146/annurev.neuro.24.1.1193

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1146/annurev.neuro.24.1.1193

Cites

90 of 90 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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

Sorted by citations

Items 601–700 of 1,003

Uniform color spaces and natural image statistics
McDermott, K. C., & Webster, M. A. (2012) · Journal of the Optical Society of America A · doi:10.1364/josaa.29.00a182 · Cited by 44
Natural Image Statistics and Low-Complexity Feature Selection
Vasconcelos, M., & Vasconcelos, N. M. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.77 · Cited by 44
The effects of spontaneous activity, background noise, and the stimulus ensemble on information transfer in neurons
Chacron, M. J., Longtin, A., & Maler, L. (2003) · Network Computation in Neural Systems · doi:10.1088/0954-898x/14/4/010 · Cited by 44
Natural scenes upset the visual applecart
Braun, J. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)00008-6 · Cited by 44
Neuroeconomics: a view from neuroscience.
Montague, P. R. (2007) · PubMed · Cited by 44
Motion as a source of environmental information: a fresh view on biological motion computation by insect brains
Egelhaaf, M., Kern, R., & Lindemann, J. P. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00127 · Cited by 43
Regulation of pupil size in natural vision across the human lifespan
Lazar, R., Degen, J., Fiechter, A.-S., Monticelli, A., & Spitschan, M. (2024) · Royal Society Open Science · doi:10.1098/rsos.191613 · Cited by 44
Information theoretic approaches to understanding circuit function
Fairhall, A. L., Shea‐Brown, E., & Barreiro, A. K. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.06.005 · Cited by 43
Statistics of Optical Coherence Tomography Data From Human Retina
Grzywacz, N. M., Juan, J. D., Ferrone, C., Giannini, D., Huang, D., Koch, G., Russo, V., Tan, O., & Bruni, C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2038375 · Cited by 43
Free-Energy Principle Inspired Video Quality Metric and Its Use in Video Coding
Xu, L., Lin, W., Ma, L., Zhang, Y., Fang, Y., Ngan, K. N., Li, S., & Yan, Y. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2525004 · Cited by 44
Noise Level Estimation for Natural Images Based on Scale-Invariant Kurtosis and Piecewise Stationarity
Dong, L., Zhou, J., & Tang, Y. Y. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2639447 · Cited by 43
The Neuronal Basis of an Illusory Motion Percept Is Explained by Decorrelation of Parallel Motion Pathways
Salazar-Gatzimas, E., Agrochão, M., Fitzgerald, J. E., & Clark, D. A. (2018) · Current Biology · doi:10.1016/j.cub.2018.10.007 · Cited by 45
Balancing Flexibility and Interference in Working Memory
Buschman, T. J. (2021) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100419-104831 · Cited by 51
The constrained disorder principle defines living organisms and provides a method for correcting disturbed biological systems
Ilan, Y. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.015 · Cited by 42
A Calcium-Dependent Plasticity Rule for HCN Channels Maintains Activity Homeostasis and Stable Synaptic Learning
Honnuraiah, S., & Narayanan, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055590 · Cited by 43
A Simple Model of Optimal Population Coding for Sensory Systems
Doi, E., & Lewicki, M. S. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003761 · Cited by 43
Localized information is necessary for scene categorization, including the Natural/Man-made distinction
Loschky, L. C., & Larson, A. M. (2008) · Journal of Vision · doi:10.1167/8.1.4 · Cited by 43
Efficient statistics, common currencies and the problem of reward-harvesting
Montague, P. R., & King‐Casas, B. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.10.002 · Cited by 43
Task-driven neural network models predict neural dynamics of proprioception
Vargas, A. M., Bisi, A., Chiappa, A. S., Versteeg, C., Miller, L. E., & Mathis, A. (2024) · Cell · doi:10.1016/j.cell.2024.02.036 · Cited by 44
Learning-dependent plasticity with and without training in the human brain
Zhang, J., & Kourtzi, Z. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1002506107 · Cited by 42
Specificity and timescales of cortical adaptation as inferences about natural movie statistics
Snow, M., Coen-Cagli, R., & Schwartz, O. (2016) · Journal of Vision · doi:10.1167/16.13.1 · Cited by 42
Frogs Exploit Statistical Regularities in Noisy Acoustic Scenes to Solve Cocktail-Party-like Problems
Lee, N., Ward, J. L., Vélez, A., Micheyl, C., & Bee, M. A. (2017) · Current Biology · doi:10.1016/j.cub.2017.01.031 · Cited by 42
Processing bias: extending sensory drive to include efficacy and efficiency in information processing
Renoult, J. P., & Mendelson, T. C. (2019) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2019.0165 · Cited by 47
Humans Use Predictive Gaze Strategies to Target Waypoints for Steering
Tuhkanen, S., Pekkanen, J., Rinkkala, P., Mole, C. D., Wilkie, R. M., & Lappi, O. (2019) · Scientific Reports · doi:10.1038/s41598-019-44723-0 · Cited by 44
Multiple Timescales Account for Adaptive Responses across Sensory Cortices
Latimer, K. W., Barbera, D., Sokoletsky, M., Awwad, B., Katz, Y., Nelken, I., Lampl, I., Fairhall, A. L., & Priebe, N. J. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1642-19.2019 · Cited by 42
Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes
Yu, Z., Liu, J. K., Jia, S., Zhang, Y., Zheng, Y., Tian, Y., & Huang, T. (2020) · Engineering · doi:10.1016/j.eng.2020.02.004 · Cited by 43
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
Klindt, D. A., Schott, L., Sharma, Y., Ustyuzhaninov, I., Brendel, W., Bethge, M., & Paiton, D. M. (2020) · Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) · doi:10.48550/arxiv.2007.10930 · Cited by 88
Adaptive precision pooling of model neuron activities predicts the efficiency of human visual learning
Jacobs, R. A. (2009) · Journal of Vision · doi:10.1167/9.4.22 · Cited by 43
Does depth perception require vertical-disparity detectors?
Read, J. C. A., & Cumming, B. G. (2006) · Journal of Vision · doi:10.1167/6.12.1 · Cited by 42
Speech Perception within a Biologically Realistic Information-Theoretic Framework
Kluender, K. R., & Kiefte, M. (2006) · Elsevier eBooks · doi:10.1016/b978-012369374-7/50007-9 · Cited by 43
Minimal Models of Multidimensional Computations
Fitzgerald, J. D., Sincich, L. C., & Sharpee, T. O. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001111 · Cited by 42
Odor-Specific, Olfactory Marker Protein-Mediated Sparsening of Primary Olfactory Input to the Brain after Odor Exposure
Kass, M. D., Moberly, A. H., Rosenthal, M. C., Guang, S. A., & McGann, J. P. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1442-12.2013 · Cited by 41
Insights from the retina into the diverse and general computations of adaptation, detection, and prediction
Kastner, D. B., & Baccus, S. A. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.11.012 · Cited by 41
Local statistics of retinal optic flow for self-motion through natural sceneries
Calow, D., & Lappe, M. (2007) · Network Computation in Neural Systems · doi:10.1080/09548980701642277 · Cited by 41
Transformation of Visual Signals by Inhibitory Interneurons in Retinal Circuits
Jadzinsky, P. D., & Baccus, S. A. (2013) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062012-170315 · Cited by 41
Variability in State-Dependent Plasticity of Intrinsic Properties during Cell-Autonomous Self-Regulation of Calcium Homeostasis in Hippocampal Model Neurons
Srikanth, S., & Narayanan, R. (2015) · eNeuro · doi:10.1523/eneuro.0053-15.2015 · Cited by 41
Comparison-Based Image Quality Assessment for Selecting Image Restoration Parameters
Liang, H.-Y., & Weller, D. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2601783 · Cited by 35
Beauty is in the efficient coding of the beholder
Renoult, J. P., Bovet, J., & Michel Raymond, A. (2016) · Royal Society Open Science · doi:10.1098/rsos.160027 · Cited by 41
Populations of local direction–selective cells encode global motion patterns generated by self-motion
Henning, M., Ramos-Traslosheros, G., Gür, B., & Silies, M. (2022) · Science Advances · doi:10.1126/sciadv.abi7112 · Cited by 41
Temporal whitening: Transient noise perceptually equalizes the 1/f temporal amplitude spectrum
Cass, J., Alais, D., Špehar, B., & Bex, P. J. (2009) · Journal of Vision · doi:10.1167/9.10.12 · Cited by 41
Impact of Photoreceptor Loss on Retinal Circuitry
Lee, J. Y., Care, R. A., Santina, L. D., & Dunn, F. A. (2021) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100119-124713 · Cited by 43
Characterizing ensemble statistics: mean size is represented across multiple frames of reference
Corbett, J. E., & Melcher, D. (2013) · Attention Perception & Psychophysics · doi:10.3758/s13414-013-0595-x · Cited by 40
Population coding under normalization
Ringach, D. L. (2009) · Vision Research · doi:10.1016/j.visres.2009.12.007 · Cited by 40
Neural Heterogeneities Determine Response Characteristics to Second-, but Not First-Order Stimulus Features
Metzen, M. G., & Chacron, M. J. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3946-14.2015 · Cited by 40
Predicting the biological variability of environmental rhythms: Weak or strong anticipation for sensorimotor synchronization?
Torre, K., Varlet, M., & Marmelat, V. (2013) · Brain and Cognition · doi:10.1016/j.bandc.2013.10.002 · Cited by 42
Envelope statistics of self-motion signals experienced by human subjects during everyday activities: Implications for vestibular processing
Carriot, J., Jamali, M., Cullen, K. E., & Chacron, M. J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178664 · Cited by 40
Task-specific sensory coding strategies are matched to detection and discrimination performance
Allen, K. M., & Marsat, G. (2018) · Journal of Experimental Biology · doi:10.1242/jeb.170563 · Cited by 40
Subpopulations of neurons in lOFC encode previous and current rewards at time of choice
Hocker, D. L., Brody, C. D., Savin, C., & Constantinople, C. M. (2021) · eLife · doi:10.7554/elife.70129 · Cited by 40
From information scaling of natural images to regimes of statistical models
Wu, Y. N., Guo, C.-E., & Zhu, S. C. (2007) · Quarterly of Applied Mathematics · doi:10.1090/s0033-569x-07-01063-2 · Cited by 40
On the Information Theoretic Implications of Embodiment – Principles and Methods
Pfeifer, R., Lungarella, M., Sporns, O., & Kuniyoshi, Y. (2007) · Springer eBooks · doi:10.1007/978-3-540-77296-5_8 · Cited by 40
Saccades during Object Viewing Modulate Oscillatory Phase in the Superior Temporal Sulcus
Bartlett, A. M., Ovaysikia, S., Logothetis, N. K., & Hoffman, K. L. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.4102-11.2011 · Cited by 43
Modelling the dynamics of motion integration with a new luminance-gated diffusion mechanism
Tlapale, É., Masson, G. S., & Kornprobst, P. (2010) · Vision Research · doi:10.1016/j.visres.2010.05.022 · Cited by 40
Theoretical Perspectives on Deep Learning Methods in Inverse Problems
Scarlett, J., Heckel, R., Rodrigues, M. R. D., Hand, P., & Eldar, Y. C. (2022) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2023.3241123 · Cited by 48
Turning the other cheek: the viewpoint dependence of facial expression after-effects
Benton, C. P., Etchells, P. J., Porter, G., Clark, A. P., Penton‐Voak, I. S., & Nikolov, S. G. (2007) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2007.0473 · Cited by 39
Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits
Ujfalussy, B. B., Makara, J. K., Branco, T., & Lengyel, M. (2015) · eLife · doi:10.7554/elife.10056 · Cited by 42
Blind image quality assessment with improved natural scene statistics model
Zhang, Y., Wu, J., Xie, X., Li, L., & Shi, G. (2016) · Digital Signal Processing · doi:10.1016/j.dsp.2016.05.012 · Cited by 39
Multiple and Dissociable Effects of Sensory History on Working-Memory Performance
Hajonides, J. E., van Ede, F., Stokes, M. G., Nobre, A. C., & Myers, N. E. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1200-22.2023 · Cited by 41
Towards Comprehensive Foundations of Computational Intelligence
Duch, W. (2007) · Studies in computational intelligence · doi:10.1007/978-3-540-71984-7_11 · Cited by 39
A comparative psychophysical approach to visual perception in primates
Matsuno, T., & Fujita, K. (2009) · Primates · doi:10.1007/s10329-008-0128-8 · Cited by 40
Contextual modulation of orientation tuning contributes to efficient processing of natural stimuli
Felsen, G., Touryan, J., & Dan, Y. (2005) · Network Computation in Neural Systems · doi:10.1080/09548980500463347 · Cited by 39
Mapping the primate lateral geniculate nucleus: A review of experiments and methods
Jeffries, A. M., Killian, N. J., & Pezaris, J. S. (2013) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2013.10.001 · Cited by 39
Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics
Schwartz, O., B, T. J. S. A., & Dayan, P. (2006) · Neural Computation · doi:10.1162/neco.2006.18.11.2680 · Cited by 41
Influence of scale and orientation on the visual perception of natural scenes
Hansen, B. C., & Essock, E. A. (2005) · Visual Cognition · doi:10.1080/13506280444000715 · Cited by 39
Centre‐surround interactions in response to natural scene stimulation in the primary visual cortex
Guo, K., Robertson, R. G., Mahmoodi, S., & Young, M. P. (2005) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2005.03858.x · Cited by 39
Neural dynamics under active inference: plausibility and efficiency of information processing
Da Costa, L., Parr, T., Sengupta, B., & Friston, K. (2021) · Entropy · doi:10.3390/e23040454 · Cited by 38
The response of retinal neurons to high-frequency stimulation
Cai, C., Twyford, P., & Fried, S. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/3/036009 · Cited by 38
Hierarchical model of natural images and the origin of scale invariance
Saremi, S., & B, T. J. S. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222618110 · Cited by 38
3D Visual Response Properties of MSTd Emerge from an Efficient, Sparse Population Code
Beyeler, M., Dutt, N. D., & Krichmar, J. L. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0396-16.2016 · Cited by 38
Degeneracy in the robust expression of spectral selectivity, subthreshold oscillations, and intrinsic excitability of entorhinal stellate cells
Mittal, D., & Narayanan, R. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00136.2018 · Cited by 39
Deep Perceptual Preprocessing for Video Coding
Chadha, A., & Andreopoulos, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01461 · Cited by 38
Cross-modal effects of value on perceptual acuity and stimulus encoding
Pooresmaeili, A., FitzGerald, T. H. B., Bach, D. R., Toelch, U., Ostendorf, F., & Dolan, R. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408873111 · Cited by 39
Invariance of visual operations at the level of receptive fields
Lindeberg, T. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066990 · Cited by 39
Topology of Functional Connectivity and Hub Dynamics in the Beta Band As Temporal Prior for Natural Vision in the Human Brain
Betti, V., Corbetta, M., de Pasquale, F., Wens, V., & Penna, S. D. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1089-17.2018 · Cited by 40
Task-induced modulations of neuronal activity along the auditory pathway
Franceschi, G. D., & Barkat, T. R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110115 · Cited by 37
Infant color perception: Insight into perceptual development
Skelton, A. E., Maule, J., & Franklin, A. (2022) · Child Development Perspectives · doi:10.1111/cdep.12447 · Cited by 37
Improved Time Series Reconstruction for Dynamic Magnetic Resonance Imaging
Sümbül, U., Santos, J. M., & Pauly, J. M. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2012030 · Cited by 37
The effects of preferred natural stimuli on humans’ affective states, physiological stress and mental health, and the potential implications for well-being in captive animals
Ross, M., & Mason, G. J. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.09.012 · Cited by 38
Functional diversity among sensory neurons from efficient coding principles
Gjorgjieva, J., Meister, M., & Sompolinsky, H. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007476 · Cited by 37
Finding the Brain in the Nose
Brann, D. H., & Datta, S. R. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-102119-103452 · Cited by 37
The Neurobiology of Context-Dependent Valuation and Choice
Louie, K., & Martino, B. D. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-416008-8.00024-3 · Cited by 41
Why is Binocular Rivalry Uncommon? Discrepant Monocular Images in the Real World
Arnold, D. H. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00116 · Cited by 37
Estimating Information Rates with Confidence Intervals in Neural Spike Trains
Shlens, J., Kennel, M. B., Abarbanel, H. D. I., & Chichilnisky, E. J. (2007) · Neural Computation · doi:10.1162/neco.2007.19.7.1683 · Cited by 37
Using CNN Features to Better Understand What Makes Visual Artworks Special
Brachmann, A., Barth, E., & Redies, C. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00830 · Cited by 41
From Perception to Pleasure
Zatorre, R. J. (2023) · doi:10.1093/oso/9780197558287.001.0001 · Cited by 43
Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues
Pinoli, J., & Debayle, J. (2006) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2007/36105 · Cited by 45
Statistical Models of Natural Images and Cortical Visual Representation
Hyvärinen, A. (2009) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2009.01057.x · Cited by 36
Shedding light on emotional perception: Interaction of brightness and semantic content in extrastriate visual cortex
Schettino, A., Keil, A., Porcu, E., & Müller, M. M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.020 · Cited by 36
Derivatives and inverse of cascaded linear+nonlinear neural models
Martínez-Garcia, M., Cyriac, P., Batard, T., Bertalmı́o, M., & Malo, J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201326 · Cited by 36
Adaptation of olfactory receptor abundances for efficient coding
Teşileanu, T., Cocco, S., Monasson, R., & Balasubramanian, V. (2019) · eLife · doi:10.7554/elife.39279 · Cited by 36
Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment
Cao, Y., Wan, Z., Ren, D., Yan, Z., & Zuo, W. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00576 · Cited by 36
Representation of central and peripheral vision in the primate cerebral cortex: Insights from studies of the marmoset brain
Yu, H., Chaplin, T. A., & Rosa, M. G. P. (2014) · Neuroscience Research · doi:10.1016/j.neures.2014.09.004 · Cited by 40
Saccadic head and thorax movements in freely walking blowflies
Blaj, G., & van Hateren, J. H. (2004) · Journal of Comparative Physiology A · doi:10.1007/s00359-004-0541-4 · Cited by 36
Understanding natural scenes: Contributions of image statistics
Cesarei, A. D., Loftus, G. R., Mastria, S., & Codispoti, M. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.01.012 · Cited by 38
Camouflage and Perceptual Organization in the Animal Kingdom
Osorio, D., & Cuthill, I. C. (2014) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199686858.013.044 · Cited by 36
An Improvement of Kernel-Based Object Tracking Based on Human Perception
Bruni, V., & Vitulano, D. (2014) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2014.2331217 · Cited by 35
The direction of measured face aftereffects
Benton, C. P., & Burgess, E. C. (2008) · Journal of Vision · doi:10.1167/8.15.1 · Cited by 35
Sparse Coding Can Predict Primary Visual Cortex Receptive Field Changes Induced by Abnormal Visual Input
Hunt, J. J., Dayan, P., & Goodhill, G. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003005 · Cited by 37
A Review of Image Denoising Methods
Irum, I., Shahid, M. A., Sharif, M., & Raza, M. (2015) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.085.07 · Cited by 35
Sexual signaling pattern correlates with habitat pattern in visually ornamented fishes
Hulse, S. V., Renoult, J. P., & Mendelson, T. C. (2020) · Nature Communications · doi:10.1038/s41467-020-16389-0 · Cited by 35
Image-Computable Ideal Observers for Tasks with Natural Stimuli
Burge, J. (2020) · Annual Review of Vision Science · doi:10.1146/annurev-vision-030320-041134 · Cited by 39

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

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

Natural Image Statistics and Neural Representation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks