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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Ulinski, P. S. (1999) · Cerebral cortex · doi:10.1007/978-1-4615-4903-1_1 · Cited by 1
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Allemand, S., Blanc, F., Burnod, Y., Dufossé, M., & Lavayssière, L. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3180-7_27 · Cited by 1
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da Silva, A. M. M. (2012) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.11.2012.tde-23102012-163809 · Cited by 1
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de Andrés, A. R. (2014) · doi:10.20868/upm.thesis.33641 · Cited by 1
Ariffin, K. (2007) · Cited by 1
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Duong, N. D., Ruh-Ming, L. I., & Simons, D. B. (1979) · Purdue e-Pubs (Purdue University System) · Cited by 1
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Xu, X., & Niu, Z. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10684-2_38 · Cited by 1
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Federmann, C. (2013) · Publications of the UdS (Saarland University) · doi:10.22028/d291-26553 · Cited by 1
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Reeke, G. N., & Sporns, O. (1991) · NATO ASI series. Series B : Physics · doi:10.1007/978-1-4615-3778-6_14 · Cited by 1
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Menken, G.-J. (1997) · European Control Conference (ECC) · doi:10.23919/ecc.1997.7082223 · Cited by 1
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Kulkarni, S. R., & Harman, G. H. (2011) · Wiley series in probability and statistics · doi:10.1002/9781118023471.ch9 · Cited by 1
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Averett, R. D. (2008) · SMARTech Repository (Georgia Institute of Technology) · Cited by 1
Gimenez, C. M. (2011) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.74.2011.tde-24052011-085146 · Cited by 1
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Scott, R. J. (1993) · Lecture notes in computer science · doi:10.1007/3-540-56798-4_125 · Cited by 1
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Patuelli, R., Reggiani, A., & Nijkamp, P. J. (2006) · RePEc: Research Papers in Economics · Cited by 1
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Pujalte, C. L., & Guerrero‐Bote, V. P. (2001) · Investigación Bibliotecológica Archivonomía Bibliotecología e Información · doi:10.22201/iibi.0187358xp.2001.30.3968 · Cited by 1
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(2012) · Elsevier eBooks · doi:10.1016/b978-1-84334-667-8.50015-7 · Cited by 1
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Tavoularis, S. (2010) · Annual Review of Fluid Mechanics · doi:10.1146/annurev.fl.42.122109.100001 · Cited by 1
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Biswas, N. N., & Bhattacharyya, S. K. (1991) · Electronics Letters · doi:10.1049/el:19910751 · Cited by 1
Mori-Okamoto, J., & Okamoto, K. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081802 · Cited by 1
Finkel, L. H., & Reeke, G. N. (1986) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00021609 · Cited by 1
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Gallo, I. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.843829 · Cited by 1
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Latash, L. P., & Latash, M. L. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081784 · Cited by 1
Crippen, G. M. (2006) · Journal of the American Chemical Society · doi:10.1021/ja069715d · Cited by 1
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HUDAK, M. J., & Marcellus, D. H. (1986) · Cybernetics & Systems · doi:10.1080/01969728608927443 · Cited by 1
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Ayestarán, R. G., & Las‐Heras, F. (2005) · International Journal of Numerical Modelling Electronic Networks Devices and Fields · doi:10.1002/jnm.595 · Cited by 1
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Ghanemi, S., & Mohamed, B. A. Y. (2001) · Kybernetes · doi:10.1108/03684920110363879 · Cited by 1
Frenger, P. (2005) · ACM SIGPLAN Notices · doi:10.1145/1089851.1089853 · Cited by 1
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Sultan, F. R., Heck, D. H., & Harold Bekkering, A. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081863 · Cited by 1
Computational neuroscience, from multiple levels to multi-level
Daucé, E., & Perrinet, L. (2009) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2009.11.001 · Cited by 1
Entropy and symmetry — their relation to thought processes in the biological system
Barrett, T. W. (1969) · Kybernetik · doi:10.1007/bf00288486 · Cited by 1
A Learning System with the Ability to Grasp Its Situation
Nagano, T. (1975) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1975.5409157 · Cited by 1
Asymptotic Inferential Capabilities of Feed-Forward Neural Networks
Ferrán, E. A., & Perazzo, R. P. J. (1991) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/14/2/014 · Cited by 1
Cortical architectures and value unit encoding
Gilbert, C. D. (1986) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00021622 · Cited by 1
Connectionist value units: Some concerns
Barnden, J. A. (1986) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00021580 · Cited by 1
Study of multi-branch structure of Universal Learning Networks
Mabu, S., Shimada, K., Hirasawa, K., & Hu, J. (2008) · Applied Soft Computing · doi:10.1016/j.asoc.2008.01.012 · Cited by 1
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Miyoshi, S., Ikeda, K., & Nakayama, K. (1998) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1998.687227 · Cited by 1
PREDICTING NOISE-INDUCED HEARING LOSS (NIHL) IN TNB WORKERS USING GDAM ALGORITHM
Gillani, R., & Zubair, S. M. (2012) · UTHM Institutional Repository (Universiti Tun Hussein Onn Malaysia) · Cited by 1
Dynamic digital photorefractive memory for optoelectronic neural network learning modules
Sasaki, H., Mauduit, N., Ma, J., Fainman, Y., Lee, S. H., & Gray, M. S. (1996) · Applied Optics · doi:10.1364/ao.35.004641 · Cited by 1
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Koch, C. (1986) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00021671 · Cited by 1
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González, A. H., Russel, G., Marquez, A., Moreno, J. A., García–Cardona, C., Domínguez, C., Colmenares, O., & Machado, J. J. (2007) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2007.4423561 · Cited by 1
Matsubara, E. T. (2004) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2004.tde-19082004-092311 · Cited by 1
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Graepel, T., Herbrich, R., Kharechko, A., & Shawe-Taylor, J. S. (2003) · ePrints Soton (University of Southampton) · Cited by 1
Dawson, C. (2011) · UA Campus Repository (The University of Arizona) · Cited by 1
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Maggo, S., & Gupta, C. (2014) · International Journal of Modern Education and Computer Science · doi:10.5815/ijmecs.2014.08.06 · Cited by 1
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Hartono, P., & Hashimoto, S. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.247248 · Cited by 1
We know a lot about the cerebellum, but do we know what motor learning is?
Swinnen, S. P., Walter, C. B., & Dounskaia, N. V. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081875 · Cited by 1
Quo Vadis neurocomputing? Neural computation at the edge to new perspectives
Goerke, N. (2004) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2003.1223442 · Cited by 1
Analysis to Two Detecting Methods of Non-Linear Analogue Quantity
Chen, G., Wang, E., & Sun, B. (2009) · International Conference on Intelligent Computation Technology and Automation · doi:10.1109/icicta.2009.325 · Cited by 1
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Chen, H.-C., Lin, Y.-L., Sun, Y., & Hsieh, J. (2003) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2002.1189361 · Cited by 1
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Lee, C. H. (2012) · UA Campus Repository (The University of Arizona) · Cited by 1
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Baird, J. C. (1986) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00021579 · Cited by 1
A paradigm of intelligent fuzzy networks
Kawamura, H., & Tani, A. (2003) · IEEE International Conference on Systems Engineering · doi:10.1109/icsyse.1992.236918 · Cited by 1
Layered inductive learning algorithms and their computational aspects
Madala, H. (2003) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/tai.1989.65353 · Cited by 1
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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