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A massively parallel architecture for a self-organizing neural pattern recognition machine
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“Note that the top-down messages can utilize the same recurrent excitatory mechanism that has been proposed for implementing biased competition for attentional selection. 10 -12”
From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [11]
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Metadata
- Authors
- Carpenter, G. A., & Grossberg, S.
- Affiliations
- Boston University (US)
- ORCID
- Grossberg, Stephen
- Venue
- Computer Vision, Graphics, and Image Processing
- Publisher
- Elsevier BV
- Published
- 1987-01
- Volume
- 37
- Issue
- 1
- Pages
- 54-115
- ISSN
- 0734-189X (ISSN-L)
- OpenAlex
- W2144219012
- Cited by
- 2,525
- References
- 44
- Citations by year
- 2026: 13 · 2025: 33 · 2024: 23 · 2023: 26 · 2022: 24 · 2021: 41 · 2020: 37 · 2019: 54 · 2018: 37 · 2017: 44 · 2016: 42 · 2015: 58 · 2014: 57 · 2013: 57 · 2012: 77
- FWCI
- 66.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Vision Graphics and Image Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- self-organizing neural networks, pattern recognition, category learning, top-down attention, prototype, attentional priming, gain control, sustained attention, intermodal competition, parallel search, template learning, invariant features, emergent properties
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