Cited by 13,333Neural Networks and ApplicationsMemory and Neural MechanismsNeural dynamics and brain function

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The perceptron: A probabilistic model for information storage and organization in the brain.

Rosenblatt, F.·1958·Psychological Review

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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
The Organization of Behavior
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
Untitled work
(1952) · Medical Entomology and Zoology · Cited by 1,436
The Sensory Order.
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
The cell assembly: Mark II.
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
Why the Mind is in the Head?
McCulloch, W. S. (1950) · dialectica · doi:10.1111/j.1746-8361.1950.tb01019.x · Cited by 73

Show all 15 references

Sorted by citations

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Zendrikov, D., Solinas, S. M. G., & Indiveri, G. (2023) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ace64c · Cited by 67
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Luo, R. (2013) · Journal of Optics · doi:10.1088/2040-8978/15/7/075708 · Cited by 67
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Tarasov, D. A., Buevich, A., Sergeev, A., & Shichkin, A. V. (2017) · Applied Geochemistry · doi:10.1016/j.apgeochem.2017.07.007 · Cited by 67
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Yuen, B., Hoang, M. T., Dong, X., & Lű, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-96723-8 · Cited by 66
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Luo, X.-Q., Yan, P., Zhang, N.-Y., Luo, B., Wang, M., Deng, Y.-H., Wu, T., Wu, X., Liu, Q., Wang, H., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-99840-6 · Cited by 66
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Deon, B., Cotta, K. P., Silva, R. F. V., Batista, C. B., Justino, G. T., Freitas, G. C., Cordeiro, A. M., Barbosa, A. S., Loução, F. L., Simioni, T., et al. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109578 · Cited by 66
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Fernández, J. M. M., Cabal, J. V. Á., Montequín, V. R., & Balsera, J. M. V. (2008) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2007.11.008 · Cited by 66
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Whitten, D. D., Placco, V. M., Beers, T. C., Chies-Santos, A. L., Bonatto, C., Varela, J., Cristòbal-Hornillos, D., Ederoclite, A., Masseron, T., Lee, Y. S., et al. (2018) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201833368 · Cited by 66
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Kim, J., Yu, J., Kim, M., Kim, K., & Han, S. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.11.734 · Cited by 66
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Ajmi, C., Zapata, J., Martínez-Álvarez, J. J., Doménech‐Asensi, G., & Ruı́z, R. (2020) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-020-00719-9 · Cited by 66
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Edelman, S., & Intrator, N. (1997) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/s0079-7421(08)60288-1 · Cited by 66
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Chung, K. S. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0011 · Cited by 65
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Qing-sheng, J., Tan, D., Li, Y., Ji, S., Cai, C., & Zheng, Q. (2019) · Applied Sciences · doi:10.3390/app10010087 · Cited by 65
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Tripathi, S., Augustin, A. I., Dunlop, A., Sukumaran, R., Dheer, S., Zavalny, A., Haslam, O., Austin, T. C., Donchez, J., Tripathi, P. K., et al. (2022) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2022.100045 · Cited by 67
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He, L., Li, Y., Torrent, D., Zhuang, X., Rabczuk, T., & Jin, Y. (2023) · Microstructures · doi:10.20517/microstructures.2023.29 · Cited by 65
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Karimzadeh, M., Basvoju, D., Vakanski, A., Charit, I., Xu, F. J., & Zhang, X. (2024) · Materials · doi:10.3390/ma17153673 · Cited by 65
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Lee, S., Sim, W., Shin, D., Seo, W., Park, J., Lee, S. C., Hwang, S., Kim, S., & Kim, S. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3712701 · Cited by 66
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Sturaro, A., Silvestri, S., Conti, M., & Das, S. K. (2018) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2018.2872034 · Cited by 65
Adaptive characterization of microstructure dataset using a two stage machine learning approach
Baskaran, A., Kane, G. A., Biggs, K., Hull, R. V., & Lewis, D. (2020) · Computational Materials Science · doi:10.1016/j.commatsci.2020.109593 · Cited by 65
Hebb synaptic plasticity
VIANADIPRISCO, G. (1984) · Progress in Neurobiology · doi:10.1016/0301-0082(84)90021-2 · Cited by 65
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Baldi, P., Frasconi, P., & Smyth, P. (2003) · Cited by 65
Theories of associative recall
Longuet-Higgins, H. C., Willshaw, D. J., & Buneman, O. P. (1970) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583500004583 · Cited by 65
Computer simulation of thinking.
Hovland, C. I. (1960) · American Psychologist · doi:10.1037/h0044165 · Cited by 64
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Agnati, L. F., Guidolin, D., Leo, G., Carone, C., Genedani, S., & Fuxé, K. (2009) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2009.10.004 · Cited by 64
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Engilberge, M., Chevallier, L., Pérez, P., & Cord, M. (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.2019.01105 · Cited by 64
Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online LearningPreprint
Zhao, P., Wang, J., Wu, P., Jin, R., & Hoi, S. C. H. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.4633 · Cited by 64
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Kass, R. E., Amari, S., Arai, K., Brown, E. N., Diekman, C. O., Diesmann, M., Doiron, B., Eden, U. T., Fairhall, A. L., Fiddyment, G. M., et al. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-041715-033733 · Cited by 64
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Goodarzi, H., Torabi, N., Najafabadi, H. S., & Archetti, M. (2007) · Gene · doi:10.1016/j.gene.2007.09.020 · Cited by 62
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Chattopadhyay, S. (2014) · Applied Computing and Informatics · doi:10.1016/j.aci.2014.01.001 · Cited by 62
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Mencía, E. L., & Fürnkranz, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12837-0_11 · Cited by 62
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Lu, S. C.-Y. (1990) · Computers in Industry · doi:10.1016/0166-3615(90)90088-7 · Cited by 62
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Authors: Rosenblatt, F.
Corresponding author: Rosenblatt, F.
Venue: Psychological Review
Publisher: American Psychological Association (APA)
Published: 1958
Volume: 65
Issue: 6
Pages: 386-408
ISSN: 0033-295X (ISSN-L)
1939-1471
DOI: 10.1037/h0042519
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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The perceptron: A probabilistic model for information storage and organization in the brain.

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