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.
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“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]
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The attention system of the human brain
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“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
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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
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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
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