The DOI matches its Crossref record; 14 of the 15 references it cites were found in a registry or an index.
The perceptron: A probabilistic model for information storage and organization in the brain.
Abstract
If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the
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A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Representation of Events in Nerve Nets and Finite Automata
Kleene, S. (1951) · Princeton University Press eBooks · doi:10.1515/9781400882618-002 · Cited by 1,927
Probabilistic Logic and the Synthesis of Reliable Organisms from Unreliable Components
von Neumann, J. (1956) · Cited by 1,652
(1952) · Medical Entomology and Zoology · Cited by 1,436
Kneale, M., & von Hayek, F. A. (1954) · The Philosophical Quarterly · doi:10.2307/2216396 · Cited by 494
Human Nature in the Light of Psychopathology
Goldstein, K. (1940) · Harvard University Press eBooks · doi:10.4159/harvard.9780674492103 · Cited by 286
Milner, P. M. (1957) · Psychological Review · doi:10.1037/h0042287 · Cited by 235
Human Nature in the Light of Psychopathology.
L., H. A., & Goldstein, K. (1941) · The Journal of Philosophy · doi:10.2307/2017294 · Cited by 126
McCulloch, W. S. (1950) · dialectica · doi:10.1111/j.1746-8361.1950.tb01019.x · Cited by 73
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Items 4,601–4,700 of 13,333
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Béroule, D. (1990) · Springer eBooks · doi:10.1007/978-3-642-76153-9_30 · Cited by 10
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Wang, D., Wu, P., Zhao, P., & Hoi, S. C. H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.07146 · Cited by 10
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Bartlow, N. (2005) · doi:10.33915/etd.1576 · Cited by 10
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Naumov, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.06080 · Cited by 10
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Young, W. A., Bihl, T., & Weckman, G. R. (2014) · IGI Global eBooks · doi:10.4018/978-1-4666-5202-6.ch019 · Cited by 10
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Ferguson, R., & Green, A. D. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3244821 · Cited by 10
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Friedland, G. H., (NNSA), U. N. N. S. A., Krell, M. M., & Metere, A. (2018) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.2172/1476219 · Cited by 10
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Zeng, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.03415 · Cited by 10
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Li, M., Sonoda, S., Cao, F., Wang, Y. G., & Liang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.08427 · Cited by 10
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Metadata
- Authors
- Rosenblatt, F.
- Corresponding author
- Rosenblatt, F.
- Venue
- Psychological Review
- Published
- 1958
- Volume
- 65
- Issue
- 6
- Pages
- 386-408
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2040870580
- Semantic Scholar
- 5d11aad09f65431b5d3cb1d85328743c9e53ba96
- Cited by
- 12,280
- References
- 15
- Citations by year
- 2026: 659 · 2025: 1019 · 2024: 969 · 2023: 933 · 2022: 945 · 2021: 965 · 2020: 898 · 2019: 826 · 2018: 494 · 2017: 411 · 2016: 318 · 2015: 313 · 2014: 282 · 2013: 332 · 2012: 255
- FWCI
- 14.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- perceptron, probabilistic models, information storage, memory, perceptual identification, generalization, memory retrieval
- MeSH terms
- Brain, Humans, Perception, Models, Statistical, Information Storage and Retrieval, Neural Networks, Computer
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