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RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT
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Cited by
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
SlowFast Networks for Video Recognition
Feichtenhofer, C., Fan, H., Malik, J., & He, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00630 · Cited by 4,007
Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx179 · Cited by 3,657
Dynamic predictions: Oscillations and synchrony in top–down processing
Engel, A. K., Fries, P., & Singer, W. (2001) · Nature Reviews Neuroscience · doi:10.1038/35094565 · Cited by 3,454
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309
Mesulam, M. (1998) · Brain · doi:10.1093/brain/121.6.1013 · Cited by 3,072
Ferrier lecture. Functional architecture of macaque monkey visual cortex
Hubel, D. H., & Wiesel, T. N. (1977) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1977.0085 · Cited by 2,608
Carver, C. S., & Scheier, M. F. (1982) · Psychological Bulletin · doi:10.1037//0033-2909.92.1.111 · Cited by 2,650
Neuronal Synchrony: A Versatile Code for the Definition of Relations?
Singer, W. (1999) · Neuron · doi:10.1016/s0896-6273(00)80821-1 · Cited by 2,581
The structural organization of layer IV in the somatosensory region (S I) of mouse cerebral cortex
Woolsey, T. A., & Van der Loos, H. (1970) · Brain Research · doi:10.1016/0006-8993(70)90079-x · Cited by 2,254
Normalization of cell responses in cat striate cortex
Heeger, D. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800009640 · Cited by 1,835
Visual field maps in human cortex.
Wandell, B. A., Dumoulin, S. O., & Brewer, A. A. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.012 · Cited by 1,231
The steady-state visual evoked potential in vision research: A review
Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. R., & Rossion, B. (2015) · Journal of Vision · doi:10.1167/15.6.4 · Cited by 1,234
The functional logic of cortical connections
Zeki, S., & Shipp, S. (1988) · Nature · doi:10.1038/335311a0 · Cited by 1,067
Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition
Fukushima, K., & Miyake, S. (1982) · Lecture Notes in Biomathematics · doi:10.1007/978-3-642-46466-9_18 · Cited by 991
Postnatal development of the visual cortex and the influence of environment
Wiesel, T. N. (1982) · Nature · doi:10.1038/299583a0 · Cited by 978
Self-citationHierarchical organization and functional streams in the visual cortex
Van Essen, D. C., & Maunsell, J. H. R. (1983) · Trends in Neurosciences · doi:10.1016/0166-2236(83)90167-4 · Cited by 962
Expectation in perceptual decision making: neural and computational mechanisms
Summerfield, C., & de Lange, F. P. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3838 · Cited by 832
Predictive Processing: A Canonical Cortical Computation
Keller, G. B., & Mrsic-Flogel, T. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.003 · Cited by 836
Sillito, A. M. (1975) · The Journal of Physiology · doi:10.1113/jphysiol.1975.sp011056 · Cited by 742
A neural circuit for spatial summation in visual cortex
Adesnik, H., Bruns, W., Taniguchi, H., Huang, Z. J., & Scanziani, M. (2012) · Nature · doi:10.1038/nature11526 · Cited by 702
Ocular dominance in layer IV of the cat's visual cortex and the effects of monocular deprivation.
Shatz, C. J., & Stryker, M. P. (1978) · The Journal of Physiology · doi:10.1113/jphysiol.1978.sp012421 · Cited by 691
Shadlen, M. N., & Movshon, J. A. (1999) · Neuron · doi:10.1016/s0896-6273(00)80822-3 · Cited by 606
Effects of sleep and arousal on the processing of visual information in the cat
Livingstone, M. S., & Hubel, D. H. (1981) · Nature · doi:10.1038/291554a0 · Cited by 583
Self-citationThe Temporal Correlation Hypothesis of Visual Feature Integration
Gray, C. M. (1999) · Neuron · doi:10.1016/s0896-6273(00)80820-x · Cited by 541
Parallel visual pathways: A review
Lennie, P. (1980) · Vision Research · doi:10.1016/0042-6989(80)90115-7 · Cited by 509
von der Malsburg, C. (1999) · Neuron · doi:10.1016/s0896-6273(00)80825-9 · Cited by 501
Shin, H.-C., Orton, M. R., Collins, D. J., Doran, S. J., & Leach, M. O. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.277 · Cited by 488
Multiple Realizability Revisited: Linking Cognitive and Neural States
Bechtel, W., & Mundale, J. (1999) · Philosophy of Science · doi:10.1086/392683 · Cited by 483
Schiller, P. H., Finlay, B. L., & Volman, S. F. (1976) · Journal of Neurophysiology · doi:10.1152/jn.1976.39.6.1288 · Cited by 458
Tononi, G., Sporns, O., & Edelman, G. M. (1992) · Cerebral Cortex · doi:10.1093/cercor/2.4.310 · Cited by 435
Huang, Y., & Rao, R. P. N. (2011) · WIREs Cognitive Science · doi:10.1002/wcs.142 · Cited by 470
Expectation and Surprise Determine Neural Population Responses in the Ventral Visual Stream
Egner, T., Monti, J. M., & Summerfield, C. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.2770-10.2010 · Cited by 350
Abstraction and The Process of Recognition
Posner, M. I. (1970) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60397-7 · Cited by 379
Perceptual Image Hashing Via Feature Points: Performance Evaluation and Tradeoffs
Monga, V., & Evans, B. L. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881948 · Cited by 363
Grid cells and cortical representation
Moser, E. I., Roudi, Y., Witter, M. P., Kentros, C., Bonhoeffer, T., & Moser, M.-B. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3766 · Cited by 335
Tsumoto, T., Eckart, W., & Creutzfeldt, O. D. (1979) · Experimental Brain Research · doi:10.1007/bf00235678 · Cited by 302
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
Generation of end-inhibition in the visual cortex via interlaminar connections
Bolz, J., & Gilbert, C. D. (1986) · Nature · doi:10.1038/320362a0 · Cited by 287
Corticofugal feedback influences the generation of length tuning in the visual pathway
Murphy, P. C., & Sillito, A. M. (1987) · Nature · doi:10.1038/329727a0 · Cited by 257
Neurons in monkey visual area V2 encode combinations of orientations
Anzai, A., Peng, X., & Van Essen, D. C. (2007) · Nature Neuroscience · doi:10.1038/nn1975 · Cited by 244
Role of the temporal domain for response selection and perceptual binding
Engel, A. (1997) · Cerebral Cortex · doi:10.1093/cercor/7.6.571 · Cited by 237
An intracellular study of neuronal organization in the visual cortex
Toyama, K., Matsunami, K., Ohno, T., & Tokashiki, S. (1974) · Experimental Brain Research · doi:10.1007/bf00234257 · Cited by 231
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Shridhar, M., Manuelli, L., & Fox, D. (2021) · arXiv · doi:10.48550/arxiv.2109.12098 · Cited by 223
Kisvárday, Z. F., Beaulieu, C., & Eysel, U. T. (1993) · Journal of Comparative Neurology · doi:10.1002/cne.903270307 · Cited by 195
Mapping multiple features in the population response of visual cortex
Basole, A., White, L. E., & Fitzpatrick, D. (2003) · Nature · doi:10.1038/nature01721 · Cited by 191
Leventhal, A. G., Keens, J., & Törk, I. (1980) · Journal of Comparative Neurology · doi:10.1002/cne.901940305 · Cited by 149
Interlaminar astroglia of the cerebral cortex: a marker of the primate brain
Colombo, J. A., & Reisin, H. D. (2004) · Brain Research · doi:10.1016/j.brainres.2004.02.003 · Cited by 132
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Keleş, M. F., & Frye, M. A. (2017) · Current Biology · doi:10.1016/j.cub.2017.01.012 · Cited by 130
Objective quality assessment of color images based on a generic perceptual reduced reference
Carnec, M., Le Callet, P., & Barba, D. (2008) · Signal Processing: Image Communication · doi:10.1016/j.image.2008.02.003 · Cited by 124
Topology and dynamics of the canonical circuit of cat V1
Binzegger, T., Douglas, R. J., & Martin, K. A. C. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.07.011 · Cited by 108
The postnatal development of the visual cortex and the influence of environment
Wiesel, T. N. (1982) · Bioscience Reports · doi:10.1007/bf01119299 · Cited by 110
Self-citationCholinergic Enhancement Reduces Spatial Spread of Visual Responses in Human Early Visual Cortex
Silver, M. A., Shenhav, A., & D'Esposito, M. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.038 · Cited by 100
Segregation of feedforward and feedback projections in mouse visual cortex
Berezovskii, V. K., Nassi, J. J., & Born, R. T. (2011) · Journal of Comparative Neurology · doi:10.1002/cne.22675 · Cited by 84
Information and selforganization: A unifying approach and applications
Haken, H., & Portugali, J. (2016) · Entropy · doi:10.3390/e18060197 · Cited by 75
Model metamers reveal divergent invariances between biological and artificial neural networks
Feather, J., Leclerc, G., Mądry, A., & McDermott, J. H. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01442-0 · Cited by 59
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Städele, C., Keleş, M. F., Mongeau, J.-M., & Frye, M. A. (2020) · Current Biology · doi:10.1016/j.cub.2020.04.069 · Cited by 52
Automated visual classification of frequent defects in flat steel coils
Medina, R., Gayubo, F., González-Rodrigo, L. M., Olmedo, D., Gómez-García-Bermejo, J., Zalama, E., & Perán, J. R. (2011) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-011-3352-0 · Cited by 49
The neurophysiological bases of high IQ
Robinson, D. L. (1989) · International Journal of Neuroscience · doi:10.3109/00207458908986260 · Cited by 45
Grounding predictive coding models in empirical neuroscience research
Egner, T., & Summerfield, C. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1200218x · Cited by 36
Considerations on the astroglial architecture and the columnar organization of the cerebral cortex.
Reisin, H. D., & Colombo, J. A. (2002) · Cellular and Molecular Neurobiology · doi:10.1023/a:1021892521180 · Cited by 31
Feature-specific prediction errors for visual mismatch
Stefanics, G., Stephan, K. E., & Heinzle, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.020 · Cited by 26
Lorazepam impairs perceptual integration of visual forms: A central effect
Giersch, A., Boucart, M., Speeg-Schatz, C., Muller-Kauffmann, F., & Danion, J. M. (1996) · Psychopharmacology · doi:10.1007/bf02246456 · Cited by 28
Bonhoeffer, T., & Grinvald, A. (1993) · Advances in Experimental Medicine and Biology · doi:10.1007/978-1-4899-2468-1_7 · Cited by 13
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Furutachi, S., & Hofer, S. B. (2026) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-102124-031410 · Cited by 3
Metadata
- Authors
- Hubel, D. H., & Wiesel, T. N.
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 1965-03
- Volume
- 28
- Issue
- 2
- Pages
- 229-289
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W1594551768
- Cited by
- 2,603
- References
- 0
- FWCI
- 35.24
- Type
- journal article
- Language
- en
- Open access
- closed
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