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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
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
Cited by
Sorted by citations
Items 10,601–10,700 of 13,333
Allan, K. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0001
Sistemas inteligentes de navegação autonoma
Crestani, P. R. (2001) · doi:10.47749/t/unicamp.2001.229167
Inter-Class Boundaries: Linear and Polynomial Classifiers
Kubát, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-81935-4_4
Compliance with International Soft Law
D’Rosario, M., & Zeleznikow, J. (2019) · Natural Language Processing · doi:10.4018/978-1-7998-0951-7.ch004
From Biomedical Image Analysis to Biomedical Image Understanding Using Machine Learning
Romero, E., & Gonzalez, F. A. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-818-7.ch809
Howarth, R. J. (2017) · Springer eBooks · doi:10.1007/978-3-319-57315-1_1
Rivest, R. L., & Remmele, W. (1991) · Springer eBooks · doi:10.1007/978-3-642-93501-5_16
Soares, P. L. B., & da Silva, J. P. (2011) · Revista Brasileira de Computação Aplicada · doi:10.5335/rbca.2011.1803
Burgos, J. E. (2020) · Springer eBooks · doi:10.1007/978-3-319-47829-6_793-1
Sirois, S. (2020) · Wiley eBooks · doi:10.1002/9781119171492.wecad103
Learning a Lightweight Ontology for Semantic Retrieval in Patient-Centered Information Systems
Reimer, U., Maier, E., Streit, S., Diggelmann, T., & Hoffleisch, M. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-2485-6.ch011
Szuster, M., & Hendzel, Z. (2017) · Studies in systems, decision and control · doi:10.1007/978-3-319-68826-8_1
Naoe, K., Sasaki, H., & Takefuji, Y. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-818-7.ch316
(1975) · Elsevier eBooks · doi:10.1016/b978-0-12-362340-9.50022-7
Haken, H. (1991) · Springer series in synergetics · doi:10.1007/978-3-662-22450-2_3
Some Example Meta-Artifacts Inspired by Science and Nature
(2012) · IGI Global eBooks · doi:10.4018/978-1-4666-0131-4.ch012
Zheng, M. (2021) · Springer eBooks · doi:10.1007/978-981-16-5399-5_2
Complex Systems and the Evolution of Artificial Intelligence
Mainzer, K. (1994) · Springer eBooks · doi:10.1007/978-3-662-03014-1_5
Dominich, S. (2001) · Mathematical modelling: theory and applications · doi:10.1007/978-94-010-0752-8_2
IDENTIFICAÇÃO DA LOCALIZAÇÃO SUBCELULAR DE PROTEÍNAS POR MEIO DE TÉCNICAS DE DEEP LEARNING
SILVA, R. B. D. M. M. D. (2019) · doi:10.17771/pucrio.acad.48222
Data-Mining and Knowledge Discovery, Neural Networks in
Brameier, M. (2009) · Springer eBooks · doi:10.1007/978-0-387-30440-3_116
Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., & Steinbrecher, M. (2016) · Texts in computer science · doi:10.1007/978-1-4471-7296-3_3
Shalev‐Shwartz, S. (2016) · Springer eBooks · doi:10.1007/978-1-4939-2864-4_287
Becker, K., & Thomson, I. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.060
Chapitre 10. Les machines pensantes : un panorama de l’intelligence artificielle
Haton, J. (2021) · EDP Sciences eBooks · doi:10.1051/978-2-7598-2612-4.c014
(2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374551-4.00037-3
Runkler, T. A. (2015) · Springer eBooks · doi:10.1007/978-3-8348-2171-3_6
Auer, P. L. (2017) · Springer eBooks · doi:10.1007/978-1-4899-7687-1_618
Predicting Credit Rating Migration Employing Neural Network Models
D’Rosario, M., & Hsieh, C. (2019) · Natural Language Processing · doi:10.4018/978-1-7998-0951-7.ch005
(2020) · Elsevier eBooks · doi:10.1016/b978-0-12-823337-5.00025-1
Ansari, M. S. (2013) · Studies in computational intelligence · doi:10.1007/978-81-322-1563-9_1
Garg, D. P., & Kumar, M. (2007) · Wiley eBooks · doi:10.1002/9780470050118.ecse270
(2004) · Elsevier eBooks · doi:10.1016/b978-012493290-6/50015-8
(2013) · Elsevier eBooks · doi:10.1016/b978-0-12-383836-0.00052-7
Applying Principles from Medicine Back to Artificial Intelligence
Schneider, H. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_289-1
Burgos, J. E. (2020) · Springer eBooks · doi:10.1007/978-3-319-47829-6_775-1
Andre, C., Pinheiro, R., & McNeill, F. (2012) · Wiley eBooks · doi:10.1002/9781118434260.biblio
Torres, J. J. (2020) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_679-2
(2008) · Springer eBooks · doi:10.1007/978-1-84800-189-3_8
Tuning weight values by resolving imbalances at nodes in an augmented artificial neural networkPreprint
Shinkai, G. (2021) · Research Square · doi:10.21203/rs.3.rs-370478/v2
Herbin, H., & Dubuisson, P. (2015) · Wiley eBooks · doi:10.1002/9781119018490.biblio
Hammel, U., Naujoks, B., & Schwefel, H. (2003) · Natural computing series · doi:10.1007/978-3-662-05609-7_1
Haykin, S. (2005) · Wiley eBooks · doi:10.1002/0470018860.s00002
Shalev‐Shwartz, S. (2015) · Springer eBooks · doi:10.1007/978-3-642-27848-8_287-2
Identifying the Antecedents of Children's Market Using Neural Network
(2016) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-5225-0993-6.ch006
Optimizing Convolutional Neural Network Parameters for Better Image ClassificationPreprint
Dhingra, M., Rawat, S. S., & Fiaidhi, J. (2020) · doi:10.36227/techrxiv.12089358
(1990) · Elsevier eBooks · doi:10.1016/b978-0-273-08817-2.50014-5
ESTIMAÇÃO DE REDES NEURAIS ARTIFICIAIS ATRAVÉS DO MÉTODO GENERALIZADO DOS MOMENTOS
da Cunha, J. M. B. (2015) · doi:10.17771/pucrio.acad.26922
(2001) · Elsevier eBooks · doi:10.1016/b978-155860595-4/50015-2
Jiao, L., Shang, R., Liu, F., & Zhang, W. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819795-0.00001-3
It may be time to perfect the neuron of artificial neural networkPreprint
Liu, G. (2020) · doi:10.36227/techrxiv.12477266.v1
A Bi-Directional Multilayer Perceptron
Jedra, M., Ouardighi, A. E., Essaı̈d, A., & Limouri, M. (1999) · Neural Processing Letters · doi:10.1023/a:1018777200501
Tong, Y., Feng, S., & Zhang, R. (2020) · Research Square · doi:10.21203/rs.3.rs-106013/v1
Eureka Team (2014) · Eureka · doi:10.29173/eureka22830
IDENTIFICAÇÃO DE CARACTERES PARA RECONHECIMENTO AUTOMÁTICO DE PLACAS VEICULARES
ALVARENGA, E. P. D. (2014) · doi:10.17771/pucrio.acad.28690
(2019) · Elsevier eBooks · doi:10.1016/b978-0-12-811842-9.16001-7
Rzempoluck, E. J. (1998) · Springer eBooks · doi:10.1007/978-1-4612-1746-6_3
Feldman, J. A., & Shastri, L. (2005) · Wiley eBooks · doi:10.1002/0470018860.s00033
An Adaptive Neural Network for the Cost Estimation of E-Learning Projects in the United Kingdom
Valverde, R. (2019) · IGI Global eBooks · doi:10.4018/978-1-7998-0414-7.ch081
Carteiras de Black-Litterman com análises baseadas em redes neurais.
Bernardes, D. G. (2019) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.3.2019.tde-17072019-104836
Ane, B. K., & Roller, D. H. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-3994-2.ch041
ANÁLISE DE ROBUSTEZ DE UMA CNN CRIADA PARA MEDIÇÕES PIV
NETO, J. P. B. D. M. (2019) · doi:10.17771/pucrio.acad.46592
(2007) · Springer eBooks · doi:10.1007/978-0-387-22463-3_10
Applying the MIDAS Touch: An Accurate and Scalable Approach to Imputing Missing DataPreprint
Lall, R., & Robinson, T. S. (2020) · doi:10.33774/apsa-2020-3tk40
(2018) · Elsevier eBooks · doi:10.1016/b978-0-08-102445-4.09998-7
Inter-Class Boundaries: Linear and Polynomial Classifiers
Kubát, M. (2017) · Springer eBooks · doi:10.1007/978-3-319-63913-0_4
(2017) · Elsevier eBooks · doi:10.1016/b978-0-08-100659-7.00020-8
(1988) · Elsevier eBooks · doi:10.1016/b978-0-08-050930-3.50019-1
Moss, D. (2021) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/jf4kr
Piccinini, G. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198866282.003.0014
Baryshnikov, E. S., Kanin, E. A., Osiptsov, A. A., Vainshtein, A. L., Burnaev, E. V., Paderin, G. V., Prutsakov, A. S., & Ternovenko, S. O. (2020) · SPE Russian Petroleum Technology Conference · doi:10.2118/201942-ru
Application of Artificial Neural Network and Genetic Programming in Civil Engineering
Samui, P., Choubisa, D., & Sharda, A. (2016) · IGI Global eBooks · doi:10.4018/978-1-4666-9619-8.ch044
Ribeiro, E. G. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.18.2020.tde-25092020-134758
Fair Use Defences During Copyright Litigation
D’Rosario, M. (2019) · Natural Language Processing · doi:10.4018/978-1-7998-0951-7.ch027
REFERENCES FROM ALL CONTRIBUTIONS
(1990) · Elsevier eBooks · doi:10.1016/b978-0-444-88400-8.50051-0
Torres, J. J. (2015) · Springer eBooks · doi:10.1007/978-1-4614-6675-8_679
Berthold, M. R., Borgelt, C., Höppner, F., Klawonn, F., & Silipo, R. (2020) · Texts in computer science · doi:10.1007/978-3-030-45574-3_9
Haikonen, P. O. A. (2007) · Wiley eBooks · doi:10.1002/9780470517871.refs
Information Hiding by Machine Learning
Naoe, K., Sasaki, H., & Takefuji, Y. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-818-7.ch205
A Review of Methodological Integration in Land-Use Change Models
Dang, A. N., & Kawasaki, A. (2019) · IGI Global eBooks · doi:10.4018/978-1-5225-8054-6.ch080
Munro, P. (2005) · Wiley eBooks · doi:10.1002/0470018860.s00041
Generative Adversarial Network For synthetic data on Bitcoin returnsPreprint
Pfenninger, M. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3864867
UM METACLASSIFICADOR PARA ENCONTRAR AS K-CLASSES MAIS RELEVANTES
MARQUES, D. D. R. (2015) · doi:10.17771/pucrio.acad.27696
Piccinini, G. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198866282.003.0001
(2014) · Elsevier eBooks · doi:10.1016/b978-0-12-409545-8.00024-8
Machine Learning in Healthcare, Introduction and Real World Application Considerations
Pitoglou, S. (2020) · IGI Global eBooks · doi:10.4018/978-1-7998-2460-2.ch002
Torres, J. J. (2014) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_679-1
Artificial Neural Network for Data Assimilation by WRF Model in Rio de Janeiro, Brazil
de Almeida, V. A., França, G. B., Velho, H. F. D. C., & Ebecken, N. F. F. (2020) · Brazilian Journal of Geophysics · doi:10.22564/brjg.v38i2.2042
Russ, J. C. (1990) · Springer eBooks · doi:10.1007/978-1-4613-0563-7_9
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review