The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Receptive fields and functional architecture of monkey striate cortex
Abstract
1. The striate cortex was studied in lightly anaesthetized macaque and spider monkeys by recording extracellularly from single units and stimulating the retinas with spots or patterns of light. Most cells can be categorized as simple, complex, or hypercomplex, with response properties very similar to those previously described in the cat. On the average, however, receptive fields are smaller, and there is a greater sensitivity to changes in stimulus orientation. A small proportion of the cells are colour coded. 2. Evidence is presented for at least two independent systems of columns extending vertically from surface to white matter. Columns of the first type contain cells with common receptive‐field orientations. They are similar to the orientation columns described in the cat, but are probably smaller in cross‐sectional area. In the second system cells are aggregated into columns according to eye preference. The ocular dominance columns are larger than the orientation columns, and the two sets of boundaries seem to be independent. 3. There is a tendency for cells to be grouped according to symmetry of responses to movement; in some regions the cells respond equally well to the two opposite directions of movement of a line, but other regions contain a mixture of cells favouring one direction and cells favouring the other. 4. A horizontal organization corresponding to the cortical layering can also be discerned. The upper layers (II and the upper two‐thirds of III) contain complex and hypercomplex cells, but simple cells are virtually absent. The cells are mostly binocularly driven. Simple cells are found deep in layer III, and in IV A and IV B. In layer IV B they form a large proportion of the population, whereas complex cells are rare. In layers IV A and IV B one finds units lacking orientation specificity; it is not clear whether these are cell bodies or axons of geniculate cells. In layer IV most cells are driven by one eye only; this layer consists of a mosaic with cells of some regions responding to one eye only, those of other regions responding to the other eye. Layers V and VI contain mostly complex and hypercomplex cells, binocularly driven. 5. The cortex is seen as a system organized vertically and horizontally in entirely different ways. In the vertical system (in which cells lying along a vertical line in the cortex have common features) stimulus dimensions such as retinal position, line orientation, ocular dominance, and perhaps directionality of movement, are mapped in sets of superimposed but independent mosaics. The horizontal system segregates cells in layers by hierarchical orders, the lowest orders (simple cells monocularly driven) located in and near layer IV, the higher orders in the upper and lower layers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Self-citationShape and arrangement of columns in cat's striate cortex
Hubel, D. H., & Wiesel, T. N. (1963) · The Journal of Physiology · doi:10.1113/jphysiol.1963.sp007079 · Cited by 834
Self-citation
Cited by
Items 101–173 of 173
Real and Illusory Contour Processing in Area V1 of the Primate: a Cortical Balancing Act
Ramsden, B. M., Hung, C. P., & Roe, A. W. (2001) · Cerebral Cortex · doi:10.1093/cercor/11.7.648 · Cited by 200
Different glutamate receptors convey feedforward and recurrent processing in macaque V1
Self, M. W., Kooijmans, R. N., Supèr, H., Lamme, V. A., & Roelfsema, P. R. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1119527109 · Cited by 184
Edge-orientation processing in first-order tactile neurons
Pruszynski, J. A., & Johansson, R. S. (2014) · Nature Neuroscience · doi:10.1038/nn.3804 · Cited by 207
Gori, M., Sandini, G., Martinoli, C., & Burr, D. (2010) · Current Biology · doi:10.1016/j.cub.2009.11.069 · Cited by 205
Attention Modulates the Responses of Simple Cells in Monkey Primary Visual Cortex
McAdams, C. J., & Reid, R. C. (2005) · The Journal of Neuroscience · doi:10.1523/jneurosci.2904-05.2005 · Cited by 203
Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild
Liu, M., Wang, R., Li, S., Shan, S., Huang, Z., & Chen, X. (2014) · Proceedings of the 16th International Conference on Multimodal Interaction · doi:10.1145/2663204.2666274 · Cited by 202
A survey of swarm and evolutionary computing approaches for deep learning
Darwish, A., Hassanien, A. E., & Das, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09719-2 · Cited by 200
Simulation of a phosphene-based visual field: Visual acuity in a pixelized vision system
Cha, K., Horch, K., & Normann, R. A. (1992) · Annals of Biomedical Engineering · doi:10.1007/bf02368135 · Cited by 198
The cortical projections of foveal striate cortex in the rhesus monkey
Zeki, S. M. (1978) · The Journal of Physiology · doi:10.1113/jphysiol.1978.sp012270 · Cited by 195
Isolated and interrelated concepts
Goldstone, R. L. (1996) · Memory & Cognition · doi:10.3758/bf03201087 · Cited by 182
Chen, C., Su, T., Meng, G., Xing, Z., & Liu, Y. (2018) · Proceedings of the 40th International Conference on Software Engineering · doi:10.1145/3180155.3180240 · Cited by 188
Rousche, P. J., & Normann, R. A. (1999) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.750552 · Cited by 187
Meyer, H. S., Wimmer, V. C., Oberlaender, M., de Kock, C. P. J., Sakmann, B., & Helmstaedter, M. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhq067 · Cited by 170
Top-down attention switches coupling between low-level and high-level areas of human visual cortex
Al-Aidroos, N., Said, C. P., & Turk-Browne, N. B. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1202095109 · Cited by 180
Development of direction selectivity in mouse cortical neurons
Rochefort, N. L., Narushima, M., Grienberger, C., Marandi, N., Hill, D. N., & Konnerth, A. (2011) · Neuron · doi:10.1016/j.neuron.2011.06.013 · Cited by 178
Feedforward, feedback and inhibitory connections in primate visual cortex
Callaway, E. M. (2004) · Neural Networks · doi:10.1016/j.neunet.2004.04.004 · Cited by 165
A model for intracortical visual prosthesis research
Troyk, P., Bak, M., Berg, J., Bradley, D., Cogan, S., Erickson, R., Kufta, C., McCreery, D., Schmidt, E., & Towle, V. (2003) · Artificial Organs · doi:10.1046/j.1525-1594.2003.07308.x · Cited by 166
Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A., & Konkle, T. (2022) · doi:10.1101/2022.03.28.485868 · Cited by 160
Williams, B. M., Borroni, D., Liu, R., Zhao, Y., Zhang, J., Lim, J., Ma, B., Romano, V., Qi, H., Ferdousi, M., et al. (2020) · Diabetologia · doi:10.1007/s00125-019-05023-4 · Cited by 155
Neural Basis for Looming Size and Velocity Encoding in the Drosophila Giant Fiber Escape Pathway
Ache, J. M., Polsky, J., Alghailani, S., Parekh, R., Breads, P., Peek, M. Y., Bock, D. D., von Reyn, C. R., & Card, G. M. (2019) · Current Biology · doi:10.1016/j.cub.2019.01.079 · Cited by 153
Posner, M. I. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.040 · Cited by 151
A Laminar Organization for Selective Cortico-Cortical Communication
D’Souza, R. D., & Burkhalter, A. (2017) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2017.00071 · Cited by 138
A social Bayesian brain: how social knowledge can shape visual perception
Otten, M., Seth, A. K., & Pinto, Y. (2017) · Brain and Cognition · doi:10.1016/j.bandc.2016.05.002 · Cited by 124
Pixel-wise regression using U-Net and its application on pansharpening
Yao, W., Zeng, Z., Lian, C., & Tang, H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.103 · Cited by 133
A neural network trained for prediction mimics diverse features of biological neurons and perception
Lotter, W., Kreiman, G., & Cox, D. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0170-9 · Cited by 128
Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective
Zhang, S., Bamakan, S. M. H., Qu, Q., & Li, S. (2019) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2018.2864254 · Cited by 125
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
Laminar processing in the visual cortical column.
Hirsch, J. A., & Martinez, L. M. (2006) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2006.06.014 · Cited by 120
Grossberg, S. (2017) · Neural Networks · doi:10.1016/j.neunet.2016.11.003 · Cited by 120
Zhang, N. R., & von der Heydt, R. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5168-09.2010 · Cited by 108
Erickson, C. A., Jagadeesh, B., & Desimone, R. (2000) · Nature Neuroscience · doi:10.1038/80664 · Cited by 115
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-citationPastur-Romay, L., Cedrón, F., Pazos, A., & Porto-Pazos, A. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17081313 · Cited by 109
The optic tectum of birds: Mapping our way to understanding visual processing.
Wylie, D. R. W., Gutierrez-Ibanez, C., Pakan, J. M. P., & Iwaniuk, A. N. (2009) · Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale · doi:10.1037/a0016826 · Cited by 107
Bosking, W. H., Beauchamp, M. S., & Yoshor, D. (2017) · Annual Review of Vision Science · doi:10.1146/annurev-vision-111815-114525 · Cited by 93
Ordinal position and afferent input of neurons in monkey striate cortex
Bullier, J., & Henry, G. H. (1980) · Journal of Comparative Neurology · doi:10.1002/cne.901930407 · Cited by 99
Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A., & Konkle, T. (2024) · Nature Communications · doi:10.1038/s41467-024-53147-y · Cited by 100
Functional connectivity and neurological recovery
Corbetta, M. (2012) · Developmental Psychobiology · doi:10.1002/dev.20507 · Cited by 96
Coevolution of active vision and feature selection
Floreano, D., Kato, T., Marocco, D., & Sauser, E. (2004) · Biological Cybernetics · doi:10.1007/s00422-004-0467-5 · Cited by 97
Predictive Coding in Sensory Cortex
Kok, P., & de Lange, F. P. (2015) · An Introduction to Model-Based Cognitive Neuroscience · doi:10.1007/978-1-4939-2236-9_11 · Cited by 98
Deep Neural Networks for Modeling Visual Perceptual Learning
Wenliang, L. K., & Seitz, A. R. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.1620-17.2018 · Cited by 83
A Deep Learning Perspective on the Origin of Facial Expressions
Breuer, R., & Kimmel, R. (2017) · arXiv · doi:10.48550/arxiv.1705.01842 · Cited by 92
Ito, S., Feldheim, D. A., & Litke, A. M. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3689-16.2017 · Cited by 92
Neural dynamics of perceptual inference and its reversal during imagery
Dijkstra, N., Ambrogioni, L., Vidaurre, D., & van Gerven, M. (2020) · eLife · doi:10.7554/elife.53588 · Cited by 89
Phosphene induction and the generation of saccadic eye movements by striate cortex
Tehovnik, E. J., Slocum, W. M., Carvey, C. E., & Schiller, P. H. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00736.2004 · Cited by 87
Adaptation-induced synchronization in laminar cortical circuits
Hansen, B. J., & Dragoi, V. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1102017108 · Cited by 83
Multisensory Integration Develops Late in Humans
Burr, D., & Gori, M. (2011) · Frontiers in Neuroscience · doi:10.1201/9781439812174-23 · Cited by 82
Neural basis of multisensory looming signals
Tyll, S., Bonath, B., Schoenfeld, M. A., Heinze, H.-J., Ohl, F. W., & Noesselt, T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.056 · Cited by 75
Analogous intermediate shape coding in vision and touch
Yau, J. M., Pasupathy, A., Fitzgerald, P. J., Hsiao, S. S., & Connor, C. E. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0904186106 · Cited by 77
Predictive feedback can account for biphasic responses in the lateral geniculate nucleus
Jehee, J. F. M., & Ballard, D. H. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000373 · Cited by 75
The neural basis of tactile motion perception
Pei, Y.-C., & Bensmaia, S. J. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00391.2014 · Cited by 77
Predictive coding in auditory perception: challenges and unresolved questions
Denham, S. L., & Winkler, I. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.13802 · Cited by 76
Conway, B. R. (2002) · Cerebral Cortex · doi:10.1093/cercor/12.9.915 · Cited by 74
Microstimulation of visual cortex to restore vision
Tehovnik, E. J., Slocum, W. M., Smirnakis, S. M., & Tolias, A. S. (2009) · Progress in Brain Research · doi:10.1016/s0079-6123(09)17524-6 · Cited by 71
Machine Learning and Imaging Informatics in Oncology
Tseng, H.-H., Wei, L., Cui, S., Luo, Y., Ten Haken, R. K., & El Naqa, I. (2020) · Oncology · doi:10.1159/000493575 · Cited by 64
Cortical visual prostheses: From microstimulation to functional percept
Najarpour Foroushani, A., Pack, C. C., & Sawan, M. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaa904 · Cited by 62
Klink, P. C., Chen, X., Vanduffel, W., & Roelfsema, P. R. (2021) · eLife · doi:10.7554/elife.67304 · Cited by 63
Caroppo, A., Leone, A., & Siciliano, P. (2020) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-9665-4 · Cited by 60
Prior knowledge of illumination for 3D perception in the human brain
Gerardin, P., Kourtzi, Z., & Mamassian, P. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1006285107 · Cited by 57
Local neuronal relational structures underlying the contents of human conscious experience
Malach, R. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab028 · Cited by 53
The knowledge used in vision and where it comes from
Barlow, H. B. (1997) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.1997.0097 · Cited by 54
The Influence of Wiring Economy on Nervous System Evolution
Wang, I. E., & Clandinin, T. R. (2016) · Current Biology · doi:10.1016/j.cub.2016.08.053 · Cited by 45
Lomazzi, L., Giglio, M., & Cadini, F. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106003 · Cited by 43
Quality assessment of motion rendition in video coding
van den Branden Lambrecht, C. J., Costantini, D. M., Sicuranza, G. L., & Kunt, M. (1999) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/76.780365 · Cited by 38
Ewert, J. P. (1984) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0001846x · Cited by 31
Contrastive Predictive Coding Based Feature for Automatic Speaker Verification
Lai, C.-I. (2019) · arXiv · doi:10.48550/arxiv.1904.01575 · Cited by 20
LeDoux, J. E. (1984) · Handbook of Cognitive Neuroscience · doi:10.1007/978-1-4899-2177-2_17 · Cited by 18
Interlaminar and varicose-projection astrocytes: toward a new understanding of the primate brain
Ciani, C., & Falcone, C. (2024) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2024.1477753 · Cited by 11
Autoencoding sensory substitution
Tóth, V., & Parkkonen, L. (2019) · Unpublished · doi:10.13140/rg.2.2.10576.87048 · Cited by 7
A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision
Gifford, A. T., Cichy, R. M., Naselaris, T., & Kay, K. (2026) · Nature Communications · doi:10.1038/s41467-026-69345-9 · Cited by 6
Simulating cortical networks on heterogeneous multi-GPU systems
Nere, A., Franey, S., Hashmi, A., & Lipasti, M. (2013) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2012.02.006 · Cited by 6
Transient localist representations in critical branching networks
Rodny, J. J., Shea, T. M., & Kello, C. T. (2017) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2016.1242760 · Cited by 4
The brain as image processor and generator: towards function-restoring brain-computer-interfaces
van Hoof, R. (2022) · doi:10.26481/dis.20220603rh · Cited by 1
Metadata
- Authors
- Hubel, D. H., & Wiesel, T. N.
- Venue
- The Journal of Physiology
- Publisher
- Wiley
- Published
- 1968-03
- Volume
- 195
- Issue
- 1
- Pages
- 215-243
- ISSN
- 0022-3751 (ISSN-L)
- 1469-7793
- OpenAlex
- W2117731089
- Cited by
- 6,568
- References
- 2
- FWCI
- 47.05
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.