The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.
A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information
Open accessAbstract
We present a biologically plausible model of an attentional mechanism for forming position- and scale-invariant representations of objects in the visual world. The model relies on a set of control neurons to dynamically modify the synaptic strengths of intracortical connections so that information from a windowed region of primary visual cortex (V1) is selectively routed to higher cortical areas. Local spatial relationships (i.e., topography) within the attentional window are preserved as information is routed through the cortex. This enables attended objects to be represented in higher cortical areas within an object-centered reference frame that is position and scale invariant. We hypothesize that the pulvinar may provide the control signals for routing information through the cortex. The dynamics of the control neurons are governed by simple differential equations that could be realized by neurobiologically plausible circuits. In preattentive mode, the control neurons receive their input from a low-level "saliency map" representing potentially interesting regions of a scene. During the pattern recognition phase, control neurons are driven by the interaction between top-down (memory) and bottom-up (retinal input) sources. The model respects key neurophysiological, neuroanatomical, and psychophysical data relating to attention, and it makes a variety of experimentally testable predictions.
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.
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
“It is known that thalamus plays an important role in cortico-cortical communication, acting as a dynamic control of information passed from one cortical area to another [57],[73].”
From Towards a mathematical theory of cortical micro-circuits (2009) · cites this paper as [73]
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
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
The attention system of the human brain
Posner, M. I., & Petersen, S. E. (1990) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.13.030190.000325 · Cited by 8,070 · cited 1×
“The pulvinar also receives a massive projection from the superior colliculus, which is known to encode the direction of saccade targets and may also be involved in setting up attentional targets (Posner and Petersen, 1990; Gattass and Desimone, 199 1, 1992).”
BackgroundDistributed 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
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
Ungerleider, L. G. (1982) · Medical Entomology and Zoology · Cited by 4,531
Components of visual orienting
Posner, M. I. (1984) · Attention and performance · Cited by 2,980
Llinás, R. R. (1988) · Science · doi:10.1126/science.3059497 · Cited by 2,285
Selective Attention Gates Visual Processing in the Extrastriate Cortex
Moran, J., & Desimone, R. (2003) · The MIT Press eBooks · doi:10.7551/mitpress/2834.003.0024 · Cited by 2,164
The updating of the representation of visual space in parietal cortex by intended eye movements
Duhamel, J.-R., Colby, C. L., & Goldberg, M. E. (1992) · Science · doi:10.1126/science.1553535 · Cited by 1,966
Research works printed but not linked: 3
- Connor CE, Gallant JL, Van Essen DC (1993) Effects of focal attention on receptive field profiles in area V4. Sot Neurosci Abstr, in press.
- Desimone R (1992) Neural circuits for visual attention in the primate brain. In: Neural networks for vision and image processing (Carpenter GA, Grossberg S, eds), pp 343-364. Cambridge, MA: MIT Press.
- Douglas RJ, Martin KAC (I 990a) Neocortex. In: Synaptic organiza- tion of the brain (Shepard GM, ed), pp 389-438. New York: Oxford UP.
Cited by
Sorted by citations
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Christof Koch, A., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,289
Neural Mechanisms of Selective Visual Attention
Desimone, R., & Duncan, J. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.001205 · Cited by 8,388
Frequency-tuned salient region detection
Achanta, R., Hemami, S. S., Estrada, F., & Susstrunk, S. E. (2009) · 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/cvpr.2009.5206596 · Cited by 4,364
Li, X., Wang, W., Hu, X., & Yang, J. J. (2019) · 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/cvpr.2019.00060 · Cited by 3,472
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309
A saliency-based search mechanism for overt and covert shifts of visual attention
Itti, L., & Koch, C. (2000) · Vision Research · doi:10.1016/s0042-6989(99)00163-7 · Cited by 3,261
Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention
Fries, P., Reynolds, J. H., Rorie, A. E., & Desimone, R. (2001) · Science · doi:10.1126/science.1055465 · Cited by 2,786
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
Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex
Luck, S. J., Chelazzi, L., Hillyard, S. A., & Desimone, R. (1997) · Journal of Neurophysiology · doi:10.1152/jn.1997.77.1.24 · Cited by 1,615
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557
Slow Feature Analysis: Unsupervised Learning of Invariances
Wiskott, L., & B, T. J. S. A. (2002) · Neural Computation · doi:10.1162/089976602317318938 · Cited by 1,358
A neuromodulatory role for the human amygdala in processing emotional facial expressions
Morris, J. (1998) · Brain · doi:10.1093/brain/121.1.47 · Cited by 1,291
Modeling attention to salient proto-objects
Walther, D. B., & Koch, C. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.10.001 · Cited by 1,256
Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4
Reynolds, J. H., Chelazzi, L., & Desimone, R. (1999) · The Journal of Neuroscience · doi:10.1523/jneurosci.19-05-01736.1999 · Cited by 1,236
Interneuron dysfunction in psychiatric disorders
Marín, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3155 · Cited by 1,190
Treisman, A. (1996) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(96)80070-5 · Cited by 1,157
Modeling visual attention via selective tuning
Tsotsos, J. K., Culhane, S. M., Kei Wai, W. Y., Lai, Y., Davis, N., & Nuflo, F. (1995) · Artificial Intelligence · doi:10.1016/0004-3702(95)00025-9 · Cited by 1,143
Zhang, K. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-06-02112.1996 · Cited by 1,001
Untangling invariant object recognition
DiCarlo, J. J., & Cox, D. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.06.010 · Cited by 1,000
Separating Style and Content with Bilinear Models
Tenenbaum, J. B., & Freeman, W. T. (2000) · Neural Computation · doi:10.1162/089976600300015349 · Cited by 840
Transient neural activity in human parietal cortex during spatial attention shifts
Yantis, S., Schwarzbach, J. V., Serences, J. T., Carlson, R. L., Steinmetz, M. A., Pekar, J. J., & Courtney, S. M. (2002) · Nature Neuroscience · doi:10.1038/nn921 · Cited by 733
Visual correlates of fixation selection: effects of scale and time
Tatler, B. W., Baddeley, R. J., & Gilchrist, I. D. (2004) · Vision Research · doi:10.1016/j.visres.2004.09.017 · Cited by 700
A saliency map in primary visual cortex
Li Zhaoping, A. (2002) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01817-9 · Cited by 661
Riesenhuber, M., & Poggio, T. (2000) · Nature Neuroscience · doi:10.1038/81479 · Cited by 670
Wang, Q., Teng, Z., Xing, J., Gao, J., Hu, W., & Maybank, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00510 · Cited by 604
Shadlen, M. N., & Movshon, J. A. (1999) · Neuron · doi:10.1016/s0896-6273(00)80822-3 · Cited by 606
A Neural Theory of Visual Attention: Bridging Cognition and Neurophysiology.
Bundesen, C., Habekost, T., & Kyllingsbæk, S. (2005) · Psychological Review · doi:10.1037/0033-295x.112.2.291 · Cited by 614
Large-scale cortical networks and cognition
Bressler, S. L. (1995) · Brain Research Reviews · doi:10.1016/0165-0173(94)00016-i · Cited by 557
A Robust Infrared Small Target Detection Algorithm Based on Human Visual System
Han, J., Ma, Y., Zhou, B., Fan, F., Liang, K., & Fang, Y. (2014) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2323236 · Cited by 543
INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM
Wallis, G. M., & Rolls, E. T. (1997) · Progress in Neurobiology · doi:10.1016/s0301-0082(96)00054-8 · Cited by 540
Linguistically Modulated Perception and Cognition: The Label-Feedback Hypothesis
Lupyan, G. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00054 · Cited by 542
von der Malsburg, C. (1999) · Neuron · doi:10.1016/s0896-6273(00)80825-9 · Cited by 501
Feature detection and letter identification
Pelli, D. G., Burns, C. W., Farell, B., & Moore-Page, D. C. (2006) · Vision Research · doi:10.1016/j.visres.2006.04.023 · Cited by 456
Spatial Attention Effects in Macaque Area V4
Connor, C. E., Preddie, D. C., Gallant, J. L., & Van Essen, D. C. (1997) · The Journal of Neuroscience · doi:10.1523/jneurosci.17-09-03201.1997 · Cited by 448
Feature combination strategies for saliency-based visual attention systems
Itti, L., & Christof Koch, A. (2001) · Journal of Electronic Imaging · doi:10.1117/1.1333677 · Cited by 441
Mechanisms of top-down attention
Baluch, F., & Itti, L. (2011) · Trends in Neurosciences · doi:10.1016/j.tins.2011.02.003 · Cited by 437
A visual salience map in the primate frontal eye field
Thompson, K. G., & Bichot, N. P. (2005) · Progress in Brain Research · doi:10.1016/s0079-6123(04)47019-8 · Cited by 431
The Temporal Correlation Hypothesis of Visual Feature Integration
Gray, C. M. (1999) · Neuron · doi:10.1016/s0896-6273(00)80820-x · Cited by 541
How Close Are We to Understanding V1?
Olshausen, B. A., & Field, D. J. (2005) · Neural Computation · doi:10.1162/0899766054026639 · Cited by 421
Self-citationThe functional logic of cortico-pulvinar connections
Shipp, S. (2003) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2002.1213 · Cited by 411
Perceiver: General Perception with Iterative Attention
Jaegle, A., Gimeno, F., Brock, A., Zisserman, A., Vinyals, O., & Carreira, J. (2021) · arXiv · doi:10.48550/arxiv.2103.03206 · Cited by 414
Salinas, E., & Thier, P. (2000) · Neuron · doi:10.1016/s0896-6273(00)00004-0 · Cited by 400
Towards a mathematical theory of cortical micro-circuits
George, D., & Hawkins, J. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000532 · Cited by 404
The brain circuitry of attention
Shipp, S. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.03.004 · Cited by 402
A specific role for the thalamus in mediating the interaction of attention and arousal in humans.
Portas, C. M., Rees, G., Howseman, A. M., Josephs, O., Turner, R., & Frith, C. D. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-21-08979.1998 · Cited by 395
A theory of eye movements during target acquisition.
Zelinsky, G. J. (2008) · Psychological Review · doi:10.1037/a0013118 · Cited by 407
Computational visual attention systems and their cognitive foundations
Frintrop, S., Rome, E., & Christensen, H. I. (2010) · ACM Transactions on Applied Perception · doi:10.1145/1658349.1658355 · Cited by 400
Pulvinar-Cortex Interactions in Vision and Attention
Zhou, H., Schafer, R. J., & Desimone, R. (2016) · Neuron · doi:10.1016/j.neuron.2015.11.034 · Cited by 384
Gating multiple signals through detailed balance of excitation and inhibition in spiking networks
Vogels, T. P., & Abbott, L. F. (2009) · Nature Neuroscience · doi:10.1038/nn.2276 · Cited by 375
The corticothalamocortical circuit drives higher-order cortex in the mouse
Theyel, B. B., Llano, D. A., & Sherman, S. M. (2010) · Nature Neuroscience · doi:10.1038/nn.2449 · Cited by 366
The Role of Neural Mechanisms of Attention in Solving the Binding Problem
Reynolds, J. H., & Desimone, R. (1999) · Neuron · doi:10.1016/s0896-6273(00)80819-3 · Cited by 370
Buzsáki, G. (2019) · doi:10.1093/oso/9780190905385.001.0001 · Cited by 383
Spatial attention improves performance in spatial resolution tasks
Yeshurun, Y., & Carrasco, M. (1999) · Vision Research · doi:10.1016/s0042-6989(98)00114-x · Cited by 351
Attention during natural vision warps semantic representation across the human brain
Çukur, T., Nishimoto, S., Huth, A. G., & Gallant, J. L. (2013) · Nature Neuroscience · doi:10.1038/nn.3381 · Cited by 351
In search of common foundations for cortical computation
Phillips, W. A., & Singer, W. (1997) · Behavioral and Brain Sciences · doi:10.1017/s0140525x9700160x · Cited by 342
COMPLETE FUNCTIONAL CHARACTERIZATION OF SENSORY NEURONS BY SYSTEM IDENTIFICATION
Wu, M. C., David, S. V., & Gallant, J. L. (2006) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113024 · Cited by 339
Roskies, A. L. (1999) · Neuron · doi:10.1016/s0896-6273(00)80817-x · Cited by 347
Beta activity: a carrier for visual attention
Wróbel, A. (2000) · Acta Neurobiologiae Experimentalis · doi:10.55782/ane-2000-1344 · Cited by 324
Visuospatial attention: Beyond a spotlight model
Cave, K. R., & Bichot, N. P. (1999) · Psychonomic Bulletin & Review · doi:10.3758/bf03212327 · Cited by 334
Human brain activity during spontaneously reversing perception of ambiguous figures
Kleinschmidt, A. K., Büchel, C., Zeki, S., & Frackowiak, R. S. J. (1998) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1998.0594 · Cited by 320
A Neurodynamical cortical model of visual attention and invariant object recognition
Deco, G., & Rolls, E. T. (2003) · Vision Research · doi:10.1016/j.visres.2003.09.037 · Cited by 317
Solutions to the Binding Problem
Treisman, A. (1999) · Neuron · doi:10.1016/s0896-6273(00)80826-0 · Cited by 327
A source for feature-based attention in the prefrontal cortex
Bichot, N. P., Heard, M. T., DeGennaro, E. M., & Desimone, R. (2015) · Neuron · doi:10.1016/j.neuron.2015.10.001 · Cited by 283
Predicting visual fixations on video based on low-level visual features
Meur, O. L., Callet, P. L., & Dominique Barba, B. (2007) · Vision Research · doi:10.1016/j.visres.2007.06.015 · Cited by 306
The functional anatomy of attention to visual motion. A functional MRI study
Buchel, C. (1998) · Brain · doi:10.1093/brain/121.7.1281 · Cited by 303
Object-based visual attention for computer vision
Sun, Y., & Fisher, R. B. (2003) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00399-5 · Cited by 297
Transfer of coded information from sensory to motor networks
Salinas, E., & Abbott, L. F. (1995) · Journal of Neuroscience · doi:10.1523/jneurosci.15-10-06461.1995 · Cited by 300
Attentional enhancement of spatial resolution: linking behavioural and neurophysiological evidence
Anton-Erxleben, K., & Carrasco, M. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3443 · Cited by 302
Flexible information routing by transient synchrony
Palmigiano, A., Geisel, T., Wolf, F., & Battaglia, D. A. (2017) · Nature Neuroscience · doi:10.1038/nn.4569 · Cited by 284
Principles of human brain organization derived from split-brain studies
Gazzaniga, M. S. (1995) · Neuron · doi:10.1016/0896-6273(95)90280-5 · Cited by 287
Control of Cognitive Processes
Monsell, S., & Driver, J. (2000) · The MIT Press eBooks · doi:10.7551/mitpress/1481.001.0001 · Cited by 1,650
A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs
George, D., Lehrach, W. P., Kansky, K., Lázaro-Gredilla, M., Laan, C., Marthi, B., Lou, X., Meng, Z., Liu, Y., Wang, H., et al. (2017) · Science · doi:10.1126/science.aag2612 · Cited by 289
Responses in area V4 depend on the spatial relationship between stimulus and attention
Connor, C. E., Gallant, J. L., Preddie, D. C., & Van Essen, D. C. (1996) · Journal of Neurophysiology · doi:10.1152/jn.1996.75.3.1306 · Cited by 278
Oscillations and Filtering Networks Support Flexible Routing of Information
Akam, T., & Kullmann, D. M. (2010) · Neuron · doi:10.1016/j.neuron.2010.06.019 · Cited by 245
Pulvinar neurons reveal neurobiological evidence of past selection for rapid detection of snakes
Le, Q. V., Isbell, L. A., Matsumoto, J., Nguyen, M. N., Hori, E., Maior, R. S., Tomaz, C. A. B., Tran, A. H., Ono, T., & Nishijo, H. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312648110 · Cited by 270
Raizada, R. D. S. (2002) · Cerebral Cortex · doi:10.1093/cercor/13.1.100 · Cited by 263
Salinas, E., & B, T. J. S. A. (2001) · The Neuroscientist · doi:10.1177/107385840100700512 · Cited by 264
Figure—Ground Segregation in a Recurrent Network Architecture
Roelfsema, P. R., Lamme, V. A. F., Spekreijse, H., & Bosch, H. (2002) · Journal of Cognitive Neuroscience · doi:10.1162/08989290260045756 · Cited by 254
Eye movements in iconic visual search
Rao, R. P. N., Zelinsky, G. J., Hayhoe, M., & Ballard, D. H. (2002) · Vision Research · doi:10.1016/s0042-6989(02)00040-8 · Cited by 255
Hopf, J., Boehler, C. N., Luck, S. J., Tsotsos, J. K., Heinze, H. –., & Schoenfeld, M. A. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0507746103 · Cited by 265
Gating and control of primary visual cortex by pulvinar
Purushothaman, G., Marion, R., Li, K., & Casagrande, V. A. (2012) · Nature Neuroscience · doi:10.1038/nn.3106 · Cited by 236
Shulman, G. L., Corbetta, M., Buckner, R. L., Fiez, J. A., Miezin, F. M., Raichle, M. E., & Petersen, S. E. (1997) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1997.9.5.624 · Cited by 260
Larkum, M. E., & Zhu, J. (2002) · Journal of Neuroscience · doi:10.1523/jneurosci.22-16-06991.2002 · Cited by 249
Serre, T., Kouh, M., Cadieu, C. F., Knoblich, U., Kreiman, G., & Poggio, T. (2005) · Defense Technical Information Center (DTIC) · Cited by 245
Computational Maps in the Visual Cortex
Miikkulainen, R., Sirosh, J., Choe, Y., & Bednar, J. A. (2005) · Springer eBooks · doi:10.1007/0-387-28806-6 · Cited by 247
Predictive coding as a model of biased competition in visual attention
Spratling, M. (2008) · Vision Research · doi:10.1016/j.visres.2008.03.009 · Cited by 251
Object-Centered Direction Selectivity in the Macaque Supplementary Eye Field
Olson, C. R., & Gettner, S. N. (1995) · Science · doi:10.1126/science.7638625 · Cited by 242
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
Surfing a spike wave down the ventral stream
VanRullen, R., & Thorpe, S. J. (2002) · Vision Research · doi:10.1016/s0042-6989(02)00298-5 · Cited by 240
Schneider, W. X. (1995) · Visual Cognition · doi:10.1080/13506289508401737 · Cited by 242
Inhibitory Plasticity: Balance, Control, and Codependence
Hennequin, G., Agnes, E. J., & Vogels, T. P. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031005 · Cited by 236
A hierarchical bayesian model of invariant pattern recognition in the visual cortex
George, D., & Hawkins, J. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556155 · Cited by 223
Hamker, F. H. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhh146 · Cited by 217
A quantitative theory of immediate visual recognition
Serre, T., Kreiman, G., Kouh, M., Cadieu, C. F., Knoblich, U., & Poggio, T. (2007) · Progress in brain research · doi:10.1016/s0079-6123(06)65004-8 · Cited by 221
Attentional Selection for Object Recognition — A Gentle Way
Walther, D. B., Itti, L., Riesenhuber, M., Poggio, T., & Koch, C. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36181-2_47 · Cited by 216
Attention Reduces Spatial Uncertainty in Human Ventral Temporal Cortex
Kay, K., Weiner, K. S., & Kalanit Grill-spector, A. (2015) · Current Biology · doi:10.1016/j.cub.2014.12.050 · Cited by 228
Coherent Oscillatory Activity in Monkey Area V4 Predicts Successful Allocation of Attention
Taylor, K., Mandon, S., Freiwald, W. A., & Kreiter, A. K. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhi023 · Cited by 209
Truccolo, W., Ding, M., Knuth, K. H., Nakamura, R., & Bressler, S. L. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(01)00739-8 · Cited by 213
Metadata
- Authors
- Olshausen, B., Anderson, C., & Van Essen, D.
- Affiliations
- California Institute of Technology (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 1993-11-01
- Volume
- 13
- Issue
- 11
- Pages
- 4700-4719
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6576339
- OpenAlex
- W2115441154
- Cited by
- 908
- References
- 86
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 16 · 2023: 18 · 2022: 28 · 2021: 23 · 2020: 29 · 2019: 54 · 2018: 18 · 2017: 23 · 2016: 20 · 2015: 30 · 2014: 26 · 2013: 28 · 2012: 33
- FWCI
- 10.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- California Institute of Technology
- Office of Naval Research (N00014)
- National Institute of Mental Health (MH19138-02 T32, T32MH019138)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- visual attention, invariant pattern recognition, position invariance, scale invariance, dynamic routing, pulvinar, primary visual cortex, top-down and bottom-up processing, saliency maps, cortical topography, biologically plausible models
- MeSH terms
- Animals, Association Learning, Attention, Computer Simulation, Mathematics, Memory, Models, Biological, Models, Psychological, Neurons, Pattern Recognition, Visual, Primates, Visual Cortex
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
