Cited by 908Visual perception and processing mechanismsVisual Attention and Saliency DetectionNeural dynamics and brain function

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 access

Olshausen, BA, Anderson, CH, Van Essen, DC·1993·The Journal of Neuroscience

DOI 10.1523/jneurosci.13-11-04700.1993PMC PMC6576339

Open this paper in CoChat — free account Open PDF

Abstract

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.

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

Crossref · 10.1523/jneurosci.13-11-04700.1993

Cites

86 of 86 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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).”
Background
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
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
Two cortical visual systems
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
The Intrinsic Electrophysiological Properties of Mammalian Neurons: Insights into Central Nervous System Function
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

Show all 86 references

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.

Sorted by citations

Items 101–200 of 906

Eye movement statistics in humans are consistent with an optimal search strategy
Najemnik, J., & Geisler, W. S. (2008) · Journal of Vision · doi:10.1167/8.3.4 · Cited by 208
Anterior Inferotemporal Neurons of Monkeys Engaged in Object Recognition Can be Highly Sensitive to Object Retinal Position
DiCarlo, J. J., & Maunsell, J. H. R. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00358.2002 · Cited by 207
The selective tuning model of attention: psychophysical evidence for a suppressive annulus around an attended item
Cutzu, F., & Tsotsos, J. K. (2003) · Vision Research · doi:10.1016/s0042-6989(02)00491-1 · Cited by 203
Neural blackboard architectures of combinatorial structures in cognition
van der Velde, F., & de Kamps, M. (2006) · Behavioral and Brain Sciences · doi:10.1017/s0140525x06009022 · Cited by 198
The Psychophysical Evidence for a Binding Problem in Human Vision
Wolfe, J. M., & Cave, K. R. (1999) · Neuron · doi:10.1016/s0896-6273(00)80818-1 · Cited by 197
Neural dynamics in a model of the thalamocortical system. I. Layers, loops and the emergence of fast synchronous rhythms
Lumer, E. (1997) · Cerebral Cortex · doi:10.1093/cercor/7.3.207 · Cited by 186
Are Cortical Models Really Bound by the “Binding Problem”?
Riesenhuber, M., & Poggio, T. (1999) · Neuron · doi:10.1016/s0896-6273(00)80824-7 · Cited by 194
Comparison of feature combination strategies for saliency-based visual attention systems
Itti, L., & Christof Koch, A. (1999) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.348467 · Cited by 191
Brain dystrophin, neurogenetics and mental retardation
Mehler, M. F. (2000) · Brain Research Reviews · doi:10.1016/s0165-0173(99)00090-9 · Cited by 192
Evidence for nonreciprocal organization of the mouse auditory thalamocortical‐corticothalamic projection systems
Llano, D. A., & Sherman, S. M. (2008) · Journal of Comparative Neurology · doi:10.1002/cne.21602 · Cited by 186
Attention to Stimulus Features Shifts Spectral Tuning of V4 Neurons during Natural Vision
David, S. V., Hayden, B. Y., Mazer, J. A., & Gallant, J. L. (2008) · Neuron · doi:10.1016/j.neuron.2008.07.001 · Cited by 192
Deconvolution of 40 Hz steady-state fields reveals two overlapping source activities of the human auditory cortex
Gutschalk, A., Mase, R., Roth, R., Ille, N., Rupp, A., Hähnel, S., Picton, T. W., & Scherg, M. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00019-x · Cited by 183
Synaptic Properties of Thalamic and Intracortical Inputs to Layer 4 of the First- and Higher-Order Cortical Areas in the Auditory and Somatosensory Systems
Lee, C. C., & Sherman, S. M. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90391.2008 · Cited by 180
Learning Where to Attend with Deep Architectures for Image Tracking
Denil, M., Bazzani, L., Larochelle, H., & de Freitas, N. (2012) · Neural Computation · doi:10.1162/neco_a_00312 · Cited by 199
The FeatureGate model of visual selection
Cave, K. R. (1999) · Psychological Research · doi:10.1007/s004260050050 · Cited by 177
Neural Person Search Machines
Liu, H., Feng, J., Jie, Z., Jayashree, K., Zhao, B., Qi, M., Jiang, J., & Yan, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.61 · Cited by 179
Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet
Rolls, E. T. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00035 · Cited by 174
Intermodal Selective Attention in Monkeys. II: Physiological Mechanisms of Modulation
Mehta, A. D. (2000) · Cerebral Cortex · doi:10.1093/cercor/10.4.359 · Cited by 175
An oscillation-based model for the neuronal basis of attention
Niebur, E., Christof Koch, A., & Rosin, C. (1993) · Vision Research · doi:10.1016/0042-6989(93)90236-p · Cited by 171
Visual-Patch-Attention-Aware Saliency Detection
Jian, M., Lam, K., Dong, J., & Shen, L. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2356200 · Cited by 170
Human primary visual cortex and lateral geniculate nucleus activation during visual imagery
Chen, W., Kato, T., Zhu, X., Ogawa, S., Tank, D. W., & Uǧurbil, K. (1998) · Neuroreport · doi:10.1097/00001756-199811160-00019 · Cited by 169
Receptive Field Shift and Shrinkage in Macaque Middle Temporal Area through Attentional Gain Modulation
Womelsdorf, T., Anton-Erxleben, K., & Treue, S. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.4030-07.2008 · Cited by 172
The Role of Eye Gaze During Natural Social Interactions in Typical and Autistic People
Cañigueral, R., & Hamilton, A. F. D. C. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00560 · Cited by 190
A model for the neuronal implementation of selective visual attention based on temporal correlation among neurons
Niebur, E., & Christof Koch, A. (1994) · Journal of Computational Neuroscience · doi:10.1007/bf00962722 · Cited by 165
A CNN-Transformer Hybrid Approach for Crop Classification Using Multitemporal Multisensor Images
Li, Z., Chen, G., & Zhang, T. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2971763 · Cited by 164
Engagement of Pulvino-cortical Feedforward and Feedback Pathways in Cognitive Computations
Jaramillo, J., Mejías, J. F., & Wang, X. (2018) · Neuron · doi:10.1016/j.neuron.2018.11.023 · Cited by 166
SE-ResUNet: A Novel Robotic Grasp Detection Method
Yu, S. W., Zhai, D., Xia, Y., Wu, H., & Liao, J. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3145064 · Cited by 162
Neural responses to salient visual stimuli
Morris, J. S., Friston, K. J., & Dolan, R. J. (1997) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1997.0109 · Cited by 164
Synchrony: a neuronal mechanism for attentional selection?
Niebur, E., Hsiao, S. S., & Johnson, K. O. (2002) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(02)00310-0 · Cited by 157
A Feedback Model of Visual Attention
Spratling, M., & Johnson, M. H. (2004) · Journal of Cognitive Neuroscience · doi:10.1162/089892904322984526 · Cited by 153
On the Complexity of Computing and Learning with Multiplicative Neural Networks
Schmitt, M. (2002) · Neural Computation · doi:10.1162/08997660252741121 · Cited by 154
Perceptual grouping and the interactions between visual cortical areas
Murray, S. O., Schrater, P., & Kersten, D. (2004) · Neural Networks · doi:10.1016/j.neunet.2004.03.010 · Cited by 153
Attentional effects of noradrenaline vary with arousal level: selective activation of thalamic pulvinar in humans
Coull, J. T., Jones, M. E. P., Egan, T. D., Frith, C., & Maze, M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.12.022 · Cited by 150
The contribution of spatial remapping impairments to unilateral visual neglect.
Pisella, L., & Mattingley, J. (2004) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2004.03.003 · Cited by 161
A dendritic disinhibitory circuit mechanism for pathway-specific gating
Yang, G. R., Murray, J. D., & Wang, X.-J. (2016) · Nature Communications · doi:10.1038/ncomms12815 · Cited by 148
Modeling the Temporal Dynamics of IT Neurons in Visual Search: A Mechanism for Top-Down Selective Attention
Usher, M., & Niebur, E. (1996) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1996.8.4.311 · Cited by 147
Attentional selection by distractor suppression
Caputo, G., & Guerra, S. (1998) · Vision Research · doi:10.1016/s0042-6989(97)00189-2 · Cited by 154
Attention, spatial representation, and visual neglect: Simulating emergent attention and spatial memory in the selective attention for identification model (SAIM).
Heinke, D. G., & Humphreys, G. W. (2003) · Psychological Review · doi:10.1037/0033-295x.110.1.29 · Cited by 150
Neuronal Responses in Area 7a to Multiple-stimulus Displays: I. Neurons Encode the Location of the Salient Stimulus
Constantinidis, C., & Steinmetz, M. A. (2001) · Cerebral Cortex · doi:10.1093/cercor/11.7.581 · Cited by 141
A dynamic model of how feature cues guide spatial attention
Hamker, F. H. (2003) · Vision Research · doi:10.1016/j.visres.2003.09.033 · Cited by 142
How many pixels make an image?
Torralba, A. B. (2009) · Visual Neuroscience · doi:10.1017/s0952523808080930 · Cited by 143
Neurochemical and connectional organization of the dorsal pulvinar complex in monkeys
Gutiérrez, C., Cola, M. G., Seltzer, B., & Cusick, C. G. (2000) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(20000327)419:1 · Cited by 142
Emergence of Complex Computational Structures From Chaotic Neural Networks Through Reward-Modulated Hebbian Learning
Hörzer, G. M., Legenstein, R. A., & Maass, W. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs348 · Cited by 143
Towards Precise End-to-End Weakly Supervised Object Detection Network
Yang, K., Li, D., & Dou, Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00846 · Cited by 141
The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain
Zylberberg, A., Slezak, D. F., Roelfsema, P. R., Dehaene, S., & Sigman, M. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000765 · Cited by 141
The importance of being agranular: a comparative account of visual and motor cortex
Shipp, S. D. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1630 · Cited by 133
Spatial selection via feature-driven inhibition of distractor locations
Cepeda, N. J., Cave, K. R., Bichot, N. P., & Kim, M.-S. (1998) · Perception & Psychophysics · doi:10.3758/bf03206059 · Cited by 138
Frontal Eye Field Activity Before Visual Search Errors Reveals the Integration of Bottom-Up and Top-Down Salience
Thompson, K. G., Bichot, N. P., & Sato, T. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00330.2004 · Cited by 136
Colocalization of ionotropic glutamate receptor subunits with NADPH-diaphorase-containing neurons in the rat mesopontine tegmentum
Inglis, W. L., & Semba, K. (1996) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(19960422)368:1 · Cited by 49
Cadherin-13, a risk gene for ADHD and comorbid disorders, impacts GABAergic function in hippocampus and cognition
Rivero, O., Selten, M. M., Sich, S., Popp, S., Bacmeister, L., Amendola, E., Negwer, M., Schubert, D., Proft, F., Kiser, D. P., et al. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.152 · Cited by 135
The macaque face patch system: a turtle’s underbelly for the brain
Hesse, J. K., & Tsao, D. Y. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-020-00393-w · Cited by 143
Depth-aware salient object detection using anisotropic center-surround difference
Ju, R., Liu, Y., Ren, T., Ge, L., & Wu, G. (2015) · Signal Processing Image Communication · doi:10.1016/j.image.2015.07.002 · Cited by 129
Regional and laminar differences in synaptic localization of NMDA receptor subunit NR1 splice variants in rat visual cortex and hippocampus
Johnson, R. R., Jiang, X., & Burkhalter, A. (1996) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(19960506)368:3 · Cited by 62
Context-sensitive binding by the laminar circuits of V1 and V2: A unified model of perceptual grouping, attention, and orientation contrast
Raizada, R. D. S., & Grossberg, S. (2001) · Visual Cognition · doi:10.1080/13506280143000070 · Cited by 127
Invariant Visual Responses From Attentional Gain Fields
Salinas, E., & Abbott, L. F. (1997) · Journal of Neurophysiology · doi:10.1152/jn.1997.77.6.3267 · Cited by 129
The thalamus as a monitor of motor outputs.
Guillery, R. W., & Sherman, S. M. (2002) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2002.1171 · Cited by 128
Brain Changes in the Development of Expertise: Neuroanatomical and Neurophysiological Evidence about Skill-Based Adaptations
Hill, N. M., & Schneider, W. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816796.037 · Cited by 134
The sifting of visual information in the superior colliculus
Lee, K. H., Tran, A., Turan, Z., & Meister, M. (2020) · eLife · doi:10.7554/elife.50678 · Cited by 120
Connectivity of GABAergic Calretinin-immunoreactive Neurons in Rat Primary Visual Cortex
Gonchar, Y. (1999) · Cerebral Cortex · doi:10.1093/cercor/9.7.683 · Cited by 117
Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119
What Do Words Do? Toward a Theory of Language-Augmented Thought
Lupyan, G. (2012) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/b978-0-12-394293-7.00007-8 · Cited by 128
How position dependent is visual object recognition?
Kravitz, D. J., Vinson, L. D., & Baker, C. I. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.12.006 · Cited by 122
A multiscale dynamic routing circuit for forming size- and position-invariant object representations
Olshausen, B. A., Anderson, C. H., & Essen, D. C. V. (1995) · Journal of Computational Neuroscience · doi:10.1007/bf00962707 · Cited by 119
Self-citation
Invariance of Angular Threshold Computation in a Wide-Field Looming-Sensitive Neuron
Gabbiani, F., Mo, C., & Laurent, G. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-01-00314.2001 · Cited by 112
An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World
Rasolzadeh, B., Björkman, M., Huebner, K., & Kragić, D. (2009) · The International Journal of Robotics Research · doi:10.1177/0278364909346069 · Cited by 110
Modeling perceptual learning: difficulties and how they can be overcome
Herzog, M. H., & Fahle, M. W. (1998) · Biological Cybernetics · doi:10.1007/s004220050418 · Cited by 110
Visual Search and Dual Tasks Reveal Two Distinct Attentional Resources
Rufin Vanrullen, A., Reddy, L., & Christof Koch, A. (2004) · Journal of Cognitive Neuroscience · doi:10.1162/089892904322755502 · Cited by 109
The role of attention in priming for left-right reflections of object images: Evidence for a dual representation of object shape.
Stankiewicz, B. J., Hummel, J. E., & Cooper, E. E. (1998) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.24.3.732 · Cited by 108
Visual Receptive Field Modulation in the Lateral Intraparietal Area during Attentive Fixation and Free Gaze
Ben Hamed, S. (2002) · Cerebral Cortex · doi:10.1093/cercor/12.3.234 · Cited by 105
View-based object recognition using saliency maps
Shokoufandeh, A., Marsic, I., & Dickinson, S. (1999) · Image and Vision Computing · doi:10.1016/s0262-8856(98)00124-3 · Cited by 107
B RAIN R EPRESENTATION OF O BJECT -C ENTERED S PACE IN M ONKEYS AND H UMANS
Olson, C. R. (2003) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131405 · Cited by 107
Brain-Based Devices for the Study of Nervous Systems and the Development of Intelligent Machines
Krichmar, J. L., & Edelman, G. M. (2005) · Artificial Life · doi:10.1162/1064546053278946 · Cited by 106
Two Distinct Types of Corticothalamic EPSPs and Their Contribution to Short-Term Synaptic Plasticity
Li, J., Guido, W., & Bickford, M. E. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00456.2003 · Cited by 106
Corticocortical and thalamocortical information flow in the primate visual system
Essen, D. C. V. (2005) · Progress in brain research · doi:10.1016/s0079-6123(05)49013-5 · Cited by 110
Moving to higher ground: The dynamic field theory and the dynamics of visual cognition
Johnson, J. S., Spencer, J. P., & Schöner, G. (2007) · New Ideas in Psychology · doi:10.1016/j.newideapsych.2007.07.007 · Cited by 104
Organizing principles of pulvino-cortical functional coupling in humans
Arcaro, M. J., Pinsk, M. A., Chen, J., & Kästner, S. (2018) · Nature Communications · doi:10.1038/s41467-018-07725-6 · Cited by 98
Principles of Corticocortical Communication: Proposed Schemes and Design Considerations
Kohn, A., Jasper, A. I., Semedo, J. D., Gokcen, E., Machens, C. K., & Yu, B. M. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.07.001 · Cited by 108
Redundant spoken labels facilitate perception of multiple items
Lupyan, G., & Spivey, M. J. (2010) · Attention Perception & Psychophysics · doi:10.3758/bf03196698 · Cited by 106
Visual saliency and spike timing in the ventral visual pathway
VanRullen, R. (2003) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2003.09.010 · Cited by 106
Fuzzy lattice neural network (FLNN): a hybrid model for learning
Petridis, V., & Kaburlasos, V. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.712161 · Cited by 101
Fractionating the binding process: neuropsychological evidence distinguishing binding of form from binding of surface features
Humphreys, G. W., Cinel, C., Wolfe, J. M., Olson, A., & Klempen, N. (2000) · Vision Research · doi:10.1016/s0042-6989(00)00042-0 · Cited by 101
Functional imaging of the thalamus in language
Llano, D. A. (2012) · Brain and Language · doi:10.1016/j.bandl.2012.06.004 · Cited by 102
MULTIPLE SYSTEMS OF PERCEPTUAL CATEGORY LEARNING: THEORY AND COGNITIVE TESTS
Ashby, F. G., & Valentin, V. V. (2005) · Elsevier eBooks · doi:10.1016/b978-008044612-7/50080-9 · Cited by 104
Chapter 11 Coordinate transformations in the visual system: how to generate gain fields and what to compute with them
Salinas, E., & Abbott, L. F. (2001) · Progress in brain research · doi:10.1016/s0079-6123(01)30012-2 · Cited by 100
Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition
Wang, X.-J. (2022) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-110920-035434 · Cited by 83
Effect of Saccadic Adaptation on Localization of Visual Targets
Awater, H., Burr, D. C., Lappe, M., Morrone, M. C., & Goldberg, M. E. (2004) · Journal of Neurophysiology · doi:10.1152/jn.01013.2003 · Cited by 100
Effect of Attentive Fixation in Macaque Thalamus and Cortex
Bender, D. B., & Youakim, M. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.85.1.219 · Cited by 101
Hierarchical Paired Channel Fusion Network for Street Scene Change Detection
Lei, Y., Peng, D., Zhang, P., Ke, Q., & Li, H. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3031173 · Cited by 98
How does attention attenuate target–distractor interference in vision?
Hopf, J., Boelmans, K., Schoenfeld, M. A., Heinze, H. –., & Luck, S. J. (2002) · Cognitive Brain Research · doi:10.1016/s0926-6410(02)00213-6 · Cited by 96
Global Inhibition and Stimulus Competition in the Owl Optic Tectum
Mysore, S. P., Asadollahi, A., & Knudsen, E. I. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.3740-09.2010 · Cited by 96
Differential cellular expression of isoforms of inositol 1,4,5-triphosphate receptors in neurons and glia in brain
Sharp, A. H., Nucifora, F. C., Blondel, O., Sheppard, C. A., Zhang, C., Snyder, S. H., Russell, J. T., Ryugoand, D. K., & Ross, C. A. (1999) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(19990405)406:2 · Cited by 213
Near-optimal integration of orientation information across saccades
Ganmor, E., Landy, M. S., & Simoncelli, E. P. (2015) · Journal of Vision · doi:10.1167/15.16.8 · Cited by 96
Interareal synchronization in the visual cortex
Bressler, S. L. (1996) · Behavioural Brain Research · doi:10.1016/0166-4328(95)00187-5 · Cited by 95
A Neurofunctional Theory of Consciousness
PRINZ, J. J. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511610608.012 · Cited by 95
Coordinate transformations in object recognition.
Graf, M. (2006) · Psychological Bulletin · doi:10.1037/0033-2909.132.6.920 · Cited by 91
Cortical Region Interactions and the Functional Role of Apical Dendrites
Spratling, M. (2002) · Behavioral and Cognitive Neuroscience Reviews · doi:10.1177/1534582302001003003 · Cited by 102
Interactions of visual attention and object recognition : computational modeling, algorithms, and psychophysics.
Walther, D. B. (2006) · doi:10.7907/p5ny-vc91 · Cited by 89
Top-down selective visual attention: A neurodynamical approach
Deco, G., & Zihl, J. (2001) · Visual Cognition · doi:10.1080/13506280042000054 · Cited by 90
Attentive mechanisms for dynamic and static scene analysis
Milanese, R., Gil, S., & Pun, T. (1995) · Optical Engineering · doi:10.1117/12.205668 · Cited by 89
Determinants and mechanisms of attentional modulation of neural processing.
Schroeder, C. (2001) · Frontiers in bioscience · doi:10.2741/schroed · Cited by 87

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
DOI: 10.1523/jneurosci.13-11-04700.1993
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: cc-by-nc-sa
creativecommons.org/licenses/by-nc-sa/4.0/ (vor, 181 d)
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

A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks