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 901–1,000 of 1,003
Evidence for Functional Groupings of Vibrissae across the Rodent Mystacial Pad
Hobbs, J. A., Towal, R. B., & Hartmann, M. J. Z. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004109 · Cited by 23
Linking V1 Activity to Behavior
Seidemann, E., & Geisler, W. S. (2018) · Annual Review of Vision Science · doi:10.1146/annurev-vision-102016-061324 · Cited by 23
Mechanisms of Sensory Discrimination: Insights fromDrosophilaOlfaction
Groschner, L. N., & Miesenböck, G. (2019) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-052118-115655 · Cited by 23
Generative adversarial networks with mixture of t-distributions noise for diverse image generation
Sun, J., Zhong, G., Chen, Y., Liu, Y., Li, T., & Huang, K. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.11.003 · Cited by 24
Martinez, B., Green, A., da Silva, M. F. M., Yang, Y., & Mascareñas, D. (2020) · Sensors · doi:10.3390/s20123526 · Cited by 24
Feedforward prediction error signals during episodic memory retrieval
Haque, R. U., Inati, S. K., Levey, A. I., & Zaghloul, K. A. (2020) · Nature Communications · doi:10.1038/s41467-020-19828-0 · Cited by 23
Information maximization in face processing
Bartlett, M. S. (2007) · Neurocomputing · doi:10.1016/j.neucom.2006.02.025 · Cited by 23
No‐Reference Image Quality Assessment: Past, Present, and Future
Mao, Q., Liu, S., Li, Q., Jeon, G., Kim, H., & Camacho, D. (2025) · Expert Systems · doi:10.1111/exsy.13842 · Cited by 22
Information optimal compressive sensing: static measurement design
Ashok, A., Huang, L.-C., & Neifeld, M. A. (2013) · Journal of the Optical Society of America A · doi:10.1364/josaa.30.000831 · Cited by 22
Ravello, C., Perrinet, L., Escobar, M.-J., & Palacios, A. G. (2019) · Scientific Reports · doi:10.1038/s41598-018-36861-8 · Cited by 22
Spectral Leakage and Rethinking the Kernel Size in CNNs
Tömen, N., & van Gemert, J. C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00509 · Cited by 23
Predictive coding I: Introduction
Sprevak, M. D. (2023) · Philosophy Compass · doi:10.1111/phc3.12950 · Cited by 22
Liu, J. K., Karamanlis, D., & Gollisch, T. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009925 · Cited by 22
Zero-Shot Cross-Modal Retrieval for Remote Sensing Images With Minimal Supervision
Chaudhuri, U., Bose, R., Banerjee, B., Bhattacharya, A., & Datcu, M. P. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3196307 · Cited by 24
Zylberberg, J., & DeWeese, M. R. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003182 · Cited by 22
Schweinhart, A. M., & Essock, E. A. (2013) · Perception · doi:10.1068/p7345 · Cited by 22
Lower bounds on the redundancy of natural images
Hosseini, R., Sinz, F. H., & Bethge, M. (2010) · Vision Research · doi:10.1016/j.visres.2010.07.025 · Cited by 22
Flexible Learning of Natural Statistics in the Human Brain
Schwarzkopf, D. S., Zhang, J., & Kourtzi, Z. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00028.2009 · Cited by 22
An Image Statistics–Based Model for Fixation Prediction
Yanulevskaya, V., Marsman, J.-B., Cornelissen, F. W., & Geusebroek, J. (2010) · Cognitive Computation · doi:10.1007/s12559-010-9087-7 · Cited by 22
Learning to Break Camouflage by Learning the Background
Chen, X., & Hegdé, J. (2012) · Psychological Science · doi:10.1177/0956797612445315 · Cited by 22
A Detail-Based Method for Linear Full Reference Image Quality Prediction
Claudio, E. D. D., & Jacovitti, G. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2757139 · Cited by 22
Leimkühler, T., Singh, G., Myszkowski, K., Seidel, H., & Ritschel, T. (2019) · ACM Transactions on Graphics · doi:10.1145/3355089.3356562 · Cited by 23
Fear conditioning prompts sparser representations of conditioned threat in primary visual cortex
Yin, S., Bo, K., Liu, Y., Thigpen, N., Keil, A., & Ding, M. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa122 · Cited by 22
The rapid extraction of numeric meaning
Corbett, J. E., Oriet, C., & Rensink, R. A. (2006) · Vision Research · doi:10.1016/j.visres.2005.11.015 · Cited by 26
Linking normative models of natural tasks to descriptive models of neural response
Jaini, P., & Burge, J. (2017) · Journal of Vision · doi:10.1167/17.12.16 · Cited by 22
Learning the Gestalt Rule of Collinearity from Object Motion
Prodöhl, C., Würtz, R. P., & von der Malsburg, C. (2003) · Neural Computation · doi:10.1162/08997660360675071 · Cited by 22
Optimal coding through divisive normalization models of V1 neurons
Valerio, R., & Navarro, R. (2003) · Network Computation in Neural Systems · doi:10.1088/0954-898x_14_3_310 · Cited by 22
Visual Perception and Quality Assessment
Moorthy, A. K., Wang, Z., & Bovik, A. C. (2011) · Wiley eBooks · doi:10.1002/9783527635245.ch19 · Cited by 22
On Distribution Preserving QuantizationPreprint
Li, M., Klejsa, J., & Kleijn, W. B. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1108.3728 · Cited by 22
Neural and perceptual signatures of efficient sensory codingPreprint
Ganguli, D., & Simoncelli, E. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.00058 · Cited by 22
Self-citationFrom water to land: Evolution of photoreceptor circuits for vision in air
Baden, T. (2024) · PLOS Biology · doi:10.1371/journal.pbio.3002422 · Cited by 21
Aesthetics of Neural Network Art
Hertzmann, A. (2019) · arXiv · doi:10.48550/arxiv.1903.05696 · Cited by 21
A three-layer model of natural image statistics
Gutmann, M. U., & Hyvärinen, A. (2013) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2013.01.001 · Cited by 21
Echeveste, R., & Gros, C. (2014) · Frontiers in Robotics and AI · doi:10.3389/frobt.2014.00001 · Cited by 21
An image reconstruction framework for characterizing initial visual encoding
Zhang, L.-Q., Cottaris, N. P., & Brainard, D. H. (2022) · eLife · doi:10.7554/elife.71132 · Cited by 21
Is our retina really upside down?
Baden, T., & Nilsson, D. (2022) · Current Biology · doi:10.1016/j.cub.2022.02.065 · Cited by 21
Processing visual ambiguity in fractal patterns: Pareidolia as a sign of creativity
Bellemare-Pepin, A., Harel, Y., O’Byrne, J., Mageau, G. A., Dietrich, A., & Jerbi, K. (2022) · iScience · doi:10.1016/j.isci.2022.105103 · Cited by 22
Two Sides of the Same Coin: Efficient and Predictive Neural Coding
Manookin, M. B., & Rieke, F. M. (2023) · Annual Review of Vision Science · doi:10.1146/annurev-vision-112122-020941 · Cited by 26
Santander, D. E., Pokorny, C., Ecker, A., Lazovskis, J., Santoro, M., Smith, J. P., Hess, K. P., Levi, R., & Reimann, M. (2024) · iScience · doi:10.1016/j.isci.2024.111585 · Cited by 22
Allen-Zhu, Z., Gelashvili, R., Micali, S. M., & Shavit, N. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419100111 · Cited by 22
Mechanisms underlying the basal forebrain enhancement of top‐down and bottom‐up attention
Avery, M. C., Dutt, N. D., & Krichmar, J. L. (2013) · European Journal of Neuroscience · doi:10.1111/ejn.12433 · Cited by 21
Interindividual differences in preferred directions of perceptual and motor decisions
Schütz, A. C. (2014) · Journal of Vision · doi:10.1167/14.12.16 · Cited by 21
Neural Encoding and Integration of Learned Probabilistic Sequences in Avian Sensory-Motor Circuitry
Bouchard, K. E., & Brainard, M. S. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2181-13.2013 · Cited by 23
Perceptual screen content image quality assessment and compression
Wang, S., Gu, K., Zeng, K., Wang, Z., & Lin, W. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351037 · Cited by 21
Generalized Gaussian scale mixtures: A model for wavelet coefficients of natural images
Gupta, P., Moorthy, A. K., Soundararajan, R., & Bovik, A. C. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.05.009 · Cited by 21
Sivyer, B., Tomlinson, A., & Taylor, W. R. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.3066-18.2019 · Cited by 21
Flow stimuli reveal ecologically appropriate responses in mouse visual cortex
Dyballa, L., Hoseini, M. S., Dadarlat, M. C., Zucker, S. W., & Stryker, M. P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1811265115 · Cited by 21
Efficient phase coding in hippocampal place cells
Seenivasan, P., & Narayanan, R. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033393 · Cited by 22
Surface segmentation based on the luminance and color statistics of natural scenes
Fine, I., MacLeod, D., & Boynton, G. M. (2002) · Journal of Vision · doi:10.1167/2.10.66 · Cited by 21
Neural adjustments to chromatic blur
Svec, L., Elliott, S. L., Mizokami, Y., & Webster, M. (2006) · Spatial Vision · doi:10.1163/156856806776923380 · Cited by 21
Adaptation in the visual cortex: a case for probing neuronal populations with natural stimuli
Snow, M., Coen-Cagli, R., & Schwartz, O. (2017) · F1000Research · doi:10.12688/f1000research.11154.1 · Cited by 21
The Mystery of Structure and Function of Sensory Processing Areas of the Neocortex: A Resolution
Lörincz, A., Szatmáry, B., & Szirtes, G. (2002) · Journal of Computational Neuroscience · doi:10.1023/a:1020262214821 · Cited by 21
Krüger, N., & Wörgötter, F. (2004) · Advances in imaging and electron physics · doi:10.1016/s1076-5670(04)31003-7 · Cited by 21
Learning sensory representations with intrinsic plasticity
Butko, N. J., & Triesch, J. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.11.006 · Cited by 21
Learning of sparse auditory receptive fields
Kording, K. P., Peter König, B., & Klein, D. J. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2002.1007648 · Cited by 21
Nonparametric Problem-Space Clustering: Learning Efficient Codes for Cognitive Control Tasks
Maisto, D., Donnarumma, F., & Pezzulo, G. (2016) · Entropy · doi:10.3390/e18020061 · Cited by 20
Long-Tail Zero and Few-Shot Learning via Contrastive Pretraining on and for Small Data
Rethmeier, N., & Augenstein, I. (2022) · doi:10.3390/cmsf2022003010 · Cited by 20
Serial dependence alters perceived object appearance
Collins, T. (2020) · Journal of Vision · doi:10.1167/jov.20.13.9 · Cited by 20
Zobeiri, O. A., Ostrander, B. T., Roat, J., Agrawal, Y., & Cullen, K. E. (2021) · The Journal of Physiology · doi:10.1113/jp281183 · Cited by 20
Structural-functional connectivity bandwidth of the human brain
Parsons, N., Ugon, J., Morgan, K. J., Shelyag, S., Hocking, A., Chan, S. Y., Poudel, G. R., D, J. F. D., & Caeyenberghs, K. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119659 · Cited by 20
Exact feature probabilities in images with occlusion
Pitkow, X. (2010) · Journal of Vision · doi:10.1167/10.14.42 · Cited by 21
Fiorillo, C. D. (2012) · Information · doi:10.3390/info3020175 · Cited by 20
Analyzing multicomponent receptive fields from neural responses to natural stimuli
Rowekamp, R. J., & Sharpee, T. O. (2011) · Network Computation in Neural Systems · doi:10.3109/0954898x.2011.566303 · Cited by 20
How long did it last? You would better ask a human
Lacquaniti, F., Carrozzo, M., d’Avella, A., Scaleia, B. L., Moscatelli, A., & Zago, M. (2014) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2014.00002 · Cited by 20
A weighted full-reference image quality assessment based on visual saliency
Wen, Y., Li, Y., Zhang, X., Shi, W., Wang, L., & Chen, J. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.12.005 · Cited by 20
Learning to represent signals spike by spike
Brendel, W., Bourdoukan, R., Vertechi, P., Machens, C. K., & Denève, S. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007692 · Cited by 25
Image statistics and their processing in insect vision
Dyakova, O., & Nordström, K. (2017) · Current Opinion in Insect Science · doi:10.1016/j.cois.2017.08.002 · Cited by 21
Joint Source-Channel Coding with Neural Networks for Analog Data Compression and Storage
Zarcone, R. V., Paiton, D. M., Anderson, A. S., Engel, J., Wong, H. ‐. P., & Olshausen, B. A. (2018) · DCC · doi:10.1109/dcc.2018.00023 · Cited by 21
Self-citationActive dendrites regulate the spatiotemporal spread of signaling microdomains
Basak, R., & Narayanan, R. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006485 · Cited by 21
Blind image quality assessment with semantic information
Ji, W., Wu, J., Shi, G., Wan, W., & Xie, X. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.11.038 · Cited by 20
Temporal tuning of repetition suppression across the visual cortex
Fritsche, M., Lawrence, S. J. D., & de Lange, F. P. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00582.2019 · Cited by 20
Modeling Biological Immunity to Adversarial Examples
Kim, E., Rego, J., Watkins, Y., & Kenyon, G. T. (2020) · 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/cvpr42600.2020.00472 · Cited by 20
Nested oscillations and brain connectivity during sequential stages of feature-based attention
Pagnotta, M. F., Pascucci, D., & Plomp, G. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117354 · Cited by 20
Burtan, D., Joyce, K., Burn, J. F., Handy, T. C., Ho, S., & Leonards, U. (2021) · Royal Society Open Science · doi:10.1098/rsos.201100 · Cited by 20
Roy, A., & Narayanan, R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.07.026 · Cited by 25
Gallistel, C. R., & Latham, P. E. (2022) · Timing & Time Perception · doi:10.1163/22134468-bja10069 · Cited by 20
Segmentation decreases the magnitude of the tilt illusion
Qiu, C., Kersten, D., & Olman, C. A. (2013) · Journal of Vision · doi:10.1167/13.13.19 · Cited by 20
The perceived contrast of texture patches embedded in natural images
McDonald, J. S., & Tadmor, Y. (2006) · Vision Research · doi:10.1016/j.visres.2006.04.014 · Cited by 20
Making the abstract concrete: The role of norms and values in experimental modeling
Peschard, I., & van Fraassen, B. C. (2013) · Studies in History and Philosophy of Science Part A · doi:10.1016/j.shpsa.2013.11.004 · Cited by 20
Hou, X., & Zhang, L. (2007) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1291233.1291288 · Cited by 20
Sawada, T., & Petrov, A. A. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00821.2016 · Cited by 20
Hue geometry and horizontal connections
Ben‐Shahar, O., & Zucker, S. W. (2004) · Neural Networks · doi:10.1016/j.neunet.2004.03.011 · Cited by 20
Sequential Bayesian Decoding with a Population of Neurons
Wu, S., Chen, D., Niranjan, M., & Амари, Ш.-И. (2003) · Neural Computation · doi:10.1162/089976603765202631 · Cited by 20
Bandet, M. V., & Winship, I. R. (2023) · eLife · doi:10.7554/elife.90080 · Cited by 20
Biological Basis and Computer Vision Applications of Image Phase Congruency: A Comprehensive Survey
Tian, Y., Wen, M., Lu, D., Zhong, X., & Wu, Z. (2024) · Biomimetics · doi:10.3390/biomimetics9070422 · Cited by 21
Coarse-to-fine processing drives the efficient coding of natural scenes in mouse visual cortex
Skyberg, R. J., Tanabe, S., Chen, H., & Cang, J. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110606 · Cited by 19
Input-specific synaptic depression shapes temporal integration in mouse visual cortex
Li, J. Y., & Glickfeld, L. L. (2023) · Neuron · doi:10.1016/j.neuron.2023.07.003 · Cited by 21
Effects of visual diet on colour discrimination and preference
Skelton, A. E., Maule, J., Floyd, S., Wozniak, B., Majid, A., Bosten, J. M., & Franklin, A. (2024) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2024.0909 · Cited by 20
Podgorski, K., Dunfield, D., & Haas, K. Z. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001236 · Cited by 19
Spatiotemporal Saliency: Towards a Hierarchical Representation of Visual Saliency
Bruce, N. D. B., & Tsotsos, J. K. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00582-4_8 · Cited by 20
Design of Kernels in Convolutional Neural Networks for Image Classification
Sun, Z., Özay, M., & Okatani, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_4 · Cited by 20
Leváková, M., Kostal, L., Monsempès, C., Jacob, V., & Lucas, P. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006586 · Cited by 19
The geometry of masking in neural populations
Ringach, D. L. (2019) · Nature Communications · doi:10.1038/s41467-019-12881-4 · Cited by 19
Visual indeterminacy in GAN art
Hertzmann, A. (2020) · ACM SIGGRAPH Art Gallery · doi:10.1145/3386567.3388574 · Cited by 20
Bertoni, T., Magosso, E., & Serino, A. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14981 · Cited by 20
Population Coding of Natural Electrosensory Stimuli by Midbrain Neurons
Metzen, M. G., & Chacron, M. J. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2232-20.2021 · Cited by 20
Contrast sensitivity functions in autoencoders
Li, Q., Gomez-Villa, A., Bertalmı́o, M., & Malo, J. (2022) · Journal of Vision · doi:10.1167/jov.22.6.8 · Cited by 21
Neural substrates of perception in the vestibular thalamus during natural self-motion: A review
Cullen, K. E., & Chacron, M. J. (2023) · Current Research in Neurobiology · doi:10.1016/j.crneur.2023.100073 · Cited by 19
ePPR: a new strategy for the characterization of sensory cells from input/output data
Rapela, J., Felsen, G., Touryan, J., Mendel, J. M., & Grzywacz, N. M. (2010) · Network Computation in Neural Systems · doi:10.3109/0954898x.2010.488714 · Cited by 19
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