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
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
Items 401–500 of 1,003
Hybrid No-Reference Natural Image Quality Assessment of Noisy, Blurry, JPEG2000, and JPEG Images
Shen, J., Qin, L., & Erlebacher, G. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2108661 · Cited by 74
Objective Quality Assessment and Perceptual Compression of Screen Content Images
Wang, S., Gu, K., Zeng, K., Wang, Z., & Lin, W. (2016) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2016.46 · Cited by 74
Inhibitory circuits for visual processing in thalamus
Wang, X., Sommer, F. T., & Hirsch, J. A. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.06.004 · Cited by 73
Mechanosensation is evolutionarily tuned to locomotor mechanics
Aiello, B. R., Westneat, M. W., & Hale, M. E. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1616839114 · Cited by 73
Jiang, Q., Shao, F., Lin, W., & Jiang, G. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2783938 · Cited by 74
A Free-Viewpoint Television System for Horizontal Virtual Navigation
Stankiewicz, O., Domański, M., Dziembowski, A., Grzelka, A., Mieloch, D., & Samelak, J. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2790162 · Cited by 73
Testing pseudo-linear models of responses to natural scenes in primate retinaPreprint
Heitman, A., Brackbill, N., Greschner, M., Sher, A., Litke, A. M., & Chichilnisky, E. J. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/045336 · Cited by 73
Pecka, M., Han, Y., Sader, E., & Mrsic‐Flogel, T. D. (2014) · Neuron · doi:10.1016/j.neuron.2014.09.010 · Cited by 74
The importance of information localization in scene gist recognition.
Loschky, L. C., Sethi, A., Simons, D. J., Pydimarri, T. N., Ochs, D., & Corbeille, J. L. (2007) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.33.6.1431 · Cited by 76
Error-driven learning in visual categorization and object recognition: A common-elements model.
Soto, F. A., & Wasserman, E. A. (2010) · Psychological Review · doi:10.1037/a0018695 · Cited by 72
Vinck, M. A., & Bosman, C. A. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00035 · Cited by 74
Compressible Distributions for High-dimensional StatisticsPreprint
Member, S., Cevher, V., Member, S., Davies, M. E., & Member, S. (2016) · Cited by 72
Spectro-temporal acoustical markers differentiate speech from song across cultures
Albouy, P., Mehr, S. A., Hoyer, R. S., Ginzburg, J., Du, Y., & Zatorre, R. J. (2024) · Nature Communications · doi:10.1038/s41467-024-49040-3 · Cited by 71
Artists portray human faces with the Fourier statistics of complex natural scenes
Redies, C., Hänisch, J., Blickhan, M., & Denzler, J. (2007) · Network Computation in Neural Systems · doi:10.1080/09548980701574496 · Cited by 71
Selectivity and sparseness in the responses of striate complex cells
Lehky, S. R., B, T. J. S. A., & Desimone, R. (2004) · Vision Research · doi:10.1016/j.visres.2004.07.021 · Cited by 71
How Infants View Natural Scenes Gathered From a Head‐Mounted Camera
Aslin, R. Ν. (2009) · Optometry and Vision Science · doi:10.1097/opx.0b013e3181a76e96 · Cited by 71
Graph-Represented Distribution Similarity Index for Full-Reference Image Quality Assessment
Shen, W., Zhou, M., Luo, J. T., Li, Z. G., & Kwong, S. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3390565 · Cited by 70
Timescale-dependent shaping of correlation by olfactory bulb lateral inhibition
Giridhar, S., Doiron, B., & Urban, N. N. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1015165108 · Cited by 71
Spatial frequency requirements for audiovisual speech perception
Munhall, K. G., Kroos, C. H., Jozan, G., & Vatikiotis‐Bateson, E. (2004) · Perception & Psychophysics · doi:10.3758/bf03194902 · Cited by 70
A Time-to-First-Spike CMOS Image Sensor
Guo, X., Qi, X., & Harris, J. G. (2007) · IEEE Sensors Journal · doi:10.1109/jsen.2007.900937 · Cited by 70
Perceptual image quality assessment by independent feature detector
Chang, H.-W., Zhang, Q., Wu, Q., & Gan, Y. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.04.081 · Cited by 69
Efficient data compression in perception and perceptual memory.
Bates, C., & Jacobs, R. A. (2020) · Psychological Review · doi:10.1037/rev0000197 · Cited by 75
Blind Source Separation: The Sparsity Revolution
Bobin, J., Starck, J., Moudden, Y., & Fadili, J. M. (2008) · Advances in imaging and electron physics · doi:10.1016/s1076-5670(08)00605-8 · Cited by 69
Deep Neural Networks in Computational NeurosciencePreprint
Kietzmann, T. C., McClure, P., & Kriegeskorte, N. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/133504 · Cited by 74
Mata, M. L., & Ringach, D. L. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00668.2004 · Cited by 69
Categorizing art: Comparing humans and computers
Wallraven, C., Fleming, R. W., Cunningham, D. W., Rigau, J., Feixas, M., & Sbert, M. (2009) · Computers & Graphics · doi:10.1016/j.cag.2009.04.003 · Cited by 68
Statistical Learning and Its Consequences
Turk‐Browne, N. B. (2012) · Nebraska Symposium on Motivation/The Nebraska symposium on motivation · doi:10.1007/978-1-4614-4794-8_6 · Cited by 72
The colors of paintings and viewers’ preferences
Nascimento, S., Linhares, J. M. M., Montagner, C., João, C. A. R., Amano, K., Alfaro, C., & Bailão, A. (2016) · Vision Research · doi:10.1016/j.visres.2016.11.006 · Cited by 68
Relevant sparse codes with variational information bottleneckPreprint
Chalk, M., Marre, O., & Tkačik, G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.07332 · Cited by 68
What Are Lightness Illusions and Why Do We See Them?
Corney, D. P. A., & Lotto, R. B. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030180 · Cited by 67
Iyer, L., Chua, Y., & Li, H. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.608567 · Cited by 76
Bayesian analogy with relational transformations.
Lu, H., Chen, D., & Holyoak, K. J. (2012) · Psychological Review · doi:10.1037/a0028719 · Cited by 72
Directional Asymmetries in Human Smooth Pursuit Eye Movements
Ke, S. R., Lam, J., Pai, D. K., & Spering, M. (2013) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.12-11369 · Cited by 67
Neural oscillations as a signature of efficient coding in the presence of synaptic delays
Chalk, M., Gutkin, B. S., & Denève, S. (2016) · eLife · doi:10.7554/elife.13824 · Cited by 70
Motor learning in real-world pool billiards
Haar, S., van Assel, C. M., & Faisal, A. A. (2020) · Scientific Reports · doi:10.1038/s41598-020-76805-9 · Cited by 67
Design of a Trichromatic Cone Array
Garrigan, P., Ratliff, C. P., Klein, J. M., Sterling, P., Brainard, D. H., & Balasubramanian, V. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000677 · Cited by 66
Designing ecosystems of intelligence from first principles
Friston, K. J., Ramstead, M. J. D., Kiefer, A. B., Tschantz, A., Buckley, C. L., Albarracin, M., Pitliya, R. J., Heins, C., Klein, B., Millidge, B., et al. (2024) · Collective Intelligence · doi:10.1177/26339137231222481 · Cited by 80
Visual Orientation and Directional Selectivity through Thalamic Synchrony
Stanley, G. B., Jin, J., Wang, Y., Desbordes, G., Wang, Q., Black, M. J., & Alonso, J. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.4968-11.2012 · Cited by 66
Chandler, D. M., & Hemami, S. S. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001164 · Cited by 66
General-purpose image forensics using patch likelihood under image statistical models
Fan, W., Wang, K., & Cayre, F. (2015) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2015.7368606 · Cited by 66
“What Not” Detectors Help the Brain See in Depth
Goncalves, N. R., & Welchman, A. E. (2017) · Current Biology · doi:10.1016/j.cub.2017.03.074 · Cited by 66
Effective and Efficient Photo-Based PM2.5 Concentration Estimation
Yue, G., Gu, K., & Qiao, J. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2886091 · Cited by 66
Ahmad, S., & Hawkins, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.00720 · Cited by 82
The role of higher order image statistics in masking scene gist recognition
Loschky, L. C., Hansen, B. C., Sethi, A., & Pydimarri, T. N. (2010) · Attention Perception & Psychophysics · doi:10.3758/app.72.2.427 · Cited by 66
Efficient Neural Coding in Auditory and Speech Perception
Gervain, J., & Geffen, M. N. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.09.004 · Cited by 65
Continuity fields enhance visual perception through positive serial dependence
Manassi, M., & Whitney, D. C. (2024) · Nature Reviews Psychology · doi:10.1038/s44159-024-00297-x · Cited by 69
Parallel Coding of First- and Second-Order Stimulus Attributes by Midbrain Electrosensory Neurons
McGillivray, P., Vonderschen, K., Fortune, E. S., & Chacron, M. J. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0478-12.2012 · Cited by 65
Preserving Information in Neural Transmission
Sincich, L. C., Horton, J. C., & Sharpee, T. O. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.3701-08.2009 · Cited by 65
Talebi, V., & Baker, C. L. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.4661-12.2012 · Cited by 68
Victor, J. D. (2005) · Nature Neuroscience · doi:10.1038/nn1607 · Cited by 65
Spratling, M. (2011) · Neural Computation · doi:10.1162/neco_a_00222 · Cited by 67
Boyce, P. R., & Wilkins, A. J. (2018) · Lighting Research & Technology · doi:10.1177/1477153517736467 · Cited by 65
Burton, B. G., & Laughlin, S. B. (2003) · Journal of Experimental Biology · doi:10.1242/jeb.00600 · Cited by 65
A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex
Meirhaeghe, N., Sohn, H., & Jazayeri, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.025 · Cited by 69
The generation of cortical novelty responses through inhibitory plasticity
Schulz, A., Miehl, C., Berry, M. J., & Gjorgjieva, J. (2021) · eLife · doi:10.7554/elife.65309 · Cited by 64
Functional maps within a single neuron
Narayanan, R., & Johnston, D. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00530.2012 · Cited by 64
Context-Adaptive Neural Network-Based Prediction for Image Compression
Dumas, T., Roumy, A., & Guillemot, C. M. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2934565 · Cited by 67
Synergistic Coding of Visual Information in Columnar Networks
Nigam, S., Pojoga, S., & Dragoi, V. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.006 · Cited by 65
Predictive coding of natural images by V1 firing rates and rhythmic synchronization
Uran, C., Peter, A., Lazăr, A., Barnes, W., Klon-Lipok, J., Shapcott, K. A., Roese, R., Fries, P., Singer, W., & Vinck, M. A. (2022) · Neuron · doi:10.1016/j.neuron.2022.01.002 · Cited by 71
Brief Stimuli Cast a Persistent Long-Term Trace in Visual Cortex
Fritsche, M., Solomon, S. G., & de Lange, F. P. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1350-21.2021 · Cited by 63
Tasking on Natural Statistics of Infrared Images
Goodall, T., Bovik, A. C., & Paulter, N. G. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2496289 · Cited by 65
Comparative Aspects of Hearing in Vertebrates and Insects with Antennal Ears
Albert, J. T., & Kozlov, A. S. (2016) · Current Biology · doi:10.1016/j.cub.2016.09.017 · Cited by 63
Optimal compensation for neuron loss
Barrett, D. G. T., Denève, S., & Machens, C. K. (2016) · eLife · doi:10.7554/elife.12454 · Cited by 68
Letter perception emerges from unsupervised deep learning and recycling of natural image features
Testolin, A., Stoianov, I. P., & Zorzi, M. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0186-2 · Cited by 67
Three brain states in the hippocampus and cortex
Kay, K., & Frank, L. M. (2018) · Hippocampus · doi:10.1002/hipo.22956 · Cited by 69
Adaptation and the Phenomenology of Perception
Webster, M. A., Werner, J. S., & Field, D. J. (2005) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198529699.003.0010 · Cited by 64
Longer fixation duration while viewing face images
Guo, K., Mahmoodi, S., Robertson, R. G., & Young, M. P. (2005) · Experimental Brain Research · doi:10.1007/s00221-005-0248-y · Cited by 63
Ramachandran, R., & Lisberger, S. G. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00281.2006 · Cited by 63
Natural Image Coding in V1: How Much Use Is Orientation Selectivity?
Eichhorn, J., Sinz, F. H., & Bethge, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000336 · Cited by 65
Allili, M. S., Ziou, D., Bouguila, N., & Boutemedjet, S. (2010) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2010.2077483 · Cited by 62
You Don’t See What I See: Individual Differences in the Perception of Meaning from Visual Stimuli
Partos, T., Cropper, S. J., & Rawlings, D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150615 · Cited by 75
Control of Long-Term Plasticity by Glutamate Transporters
Valtcheva, S., & Venance, L. (2019) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2019.00010 · Cited by 64
Tasks and their role in visual neuroscience
Kay, K., Bonnen, K., Denison, R. N., Arcaro, M. J., & Barack, D. L. (2023) · Neuron · doi:10.1016/j.neuron.2023.03.022 · Cited by 61
How do visual neurons respond in the real world?
Reinagel, P. (2001) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(00)00231-2 · Cited by 61
The weight of time: Gravitational force enhances discrimination of visual motion duration
Moscatelli, A., & Lacquaniti, F. (2011) · Journal of Vision · doi:10.1167/11.4.5 · Cited by 61
Topography of vision and behaviour
Smolka, J., & Hemmi, J. M. (2009) · Journal of Experimental Biology · doi:10.1242/jeb.032359 · Cited by 61
Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models
Fitzgerald, J. D., Rowekamp, R. J., Sincich, L. C., & Sharpee, T. O. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002249 · Cited by 61
High entropy of edge orientations characterizes visual artworks from diverse cultural backgrounds
Redies, C., Brachmann, A., & Wagemans, J. P. (2017) · Vision Research · doi:10.1016/j.visres.2017.02.004 · Cited by 61
The use of visual information in natural scenes
Mccotter, M. V., Gosselin, F., Sowden, P. T., & Schyns, P. G. (2005) · Visual Cognition · doi:10.1080/13506280444000599 · Cited by 66
First- and second-order information in natural images: a filter-based approach to image statistics
Johnson, A. P., & Baker, C. L. (2004) · Journal of the Optical Society of America A · doi:10.1364/josaa.21.000913 · Cited by 61
Temporal Limitations in Object Processing Across the Human Ventral Visual Pathway
McKeeff, T. J., Remus, D. A., & Tong, F. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00568.2006 · Cited by 61
Chandler, D. M., & Field, D. J. (2007) · Journal of the Optical Society of America A · doi:10.1364/josaa.24.000922 · Cited by 60
Probable nature of higher-dimensional symmetries underlying mammalian grid-cell activity patterns
Mathis, A., Stemmler, M., & Herz, A. V. M. (2015) · eLife · doi:10.7554/elife.05979 · Cited by 61
Region grouping in natural foliage scenes: Image statistics and human performance
Ing, A. D. (2010) · Journal of Vision · doi:10.1167/10.4.10 · Cited by 60
Cell types for our sense of location: where we are and where we are going
Hardcastle, K., Ganguli, S., & Giocomo, L. M. (2017) · Nature Neuroscience · doi:10.1038/nn.4654 · Cited by 59
Measuring and modeling the motor system with machine learning
Hausmann, S. B., Vargas, A. M., Mathis, A., & Mathis, M. W. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.04.004 · Cited by 64
1∕fα noise in reaction times: A proposed model based on Piéron’s law and information processing
Medina, J. M. (2009) · Physical Review E · doi:10.1103/physreve.79.011902 · Cited by 59
Uncomfortable images produce non-sparse responses in a model of primary visual cortex
Hibbard, P. B., & O’Hare, L. (2015) · Royal Society Open Science · doi:10.1098/rsos.140535 · Cited by 59
Toward Functional Classification of Neuronal Types
Sharpee, T. O. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.040 · Cited by 61
Reconstruction of natural visual scenes from neural spikes with deep neural networks
Zhang, Y., Jia, S., Zheng, Y., Yu, Z., Tian, Y., Ma, S., Huang, T., & Liu, J. K. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.01.033 · Cited by 61
Ancestral circuits for vertebrate color vision emerge at the first retinal synapse
Yoshimatsu, T., Bartel, P., Schröder, C., Janiak, F. K., St-Pierre, F., Berens, P., & Baden, T. (2021) · Science Advances · doi:10.1126/sciadv.abj6815 · Cited by 58
Timing Precision in Population Coding of Natural Scenes in the Early Visual System
Desbordes, G., Jin, J., Weng, C., Lesica, N. A., Stanley, G. B., & Alonso, J. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060324 · Cited by 60
Adaptation to Natural Binocular Disparities in Primate V1 Explained by a Generalized Energy Model
Haefner, R. M., & Cumming, B. G. (2008) · Neuron · doi:10.1016/j.neuron.2007.10.042 · Cited by 58
Multiple timescales of normalized value coding underlie adaptive choice behavior
Zimmermann, J., Glimcher, P. W., & Louie, K. (2018) · Nature Communications · doi:10.1038/s41467-018-05507-8 · Cited by 60
A machine-learning framework for automatic reference-free quality assessment in MRI
Küstner, T., Gatidis, S., Liebgott, A., Schwartz, M., Mauch, L., Martirosian, P., Schmidt, H. R., Schwenzer, N., Nikolaou, K., Bamberg, F., et al. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.07.003 · Cited by 58
Information Theoretic Approaches to Image Quality Assessment
Sheikh, H. R., & Bovik, A. C. (2005) · Elsevier eBooks · doi:10.1016/b978-012119792-6/50120-0 · Cited by 58
Learning rational temporal eye movement strategies
Hoppe, D., & Rothkopf, C. A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1601305113 · Cited by 57
An Introduction to Predictive Processing Models of Perception and Decision‐Making
Sprevak, M. D., & Smith, R. S. (2023) · Topics in Cognitive Science · doi:10.1111/tops.12704 · Cited by 66
Heterogeneity and Efficiency in the Brain
Balasubramanian, V. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2447016 · Cited by 58
Baudot, P., Levy, M. S., Marre, O., Monier, C., Pananceau, M., & Frégnac, Y. (2013) · Frontiers in Neural Circuits · doi:10.3389/fncir.2013.00206 · Cited by 59
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
