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 10,701–10,800 of 13,333
Das, N. C. (2015) · Springer eBooks · doi:10.1007/978-81-322-2364-1_7
It may be time to perfect the neuron of artificial neural networkPreprint
Liu, G. (2020) · doi:10.36227/techrxiv.12477266.v5
Lynch, S. (2018) · Springer eBooks · doi:10.1007/978-3-319-78145-7_20
Lynch, S. (2017) · Springer eBooks · doi:10.1007/978-3-319-61485-4_20
Naoe, K., Sasaki, H., & Takefuji, Y. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-1767-4.ch006
REDUCING TEACHER-STUDENT INTERACTIONS BETWEEN TWO NEURAL NETWORKS
KRIEGER, G. M. (2019) · doi:10.17771/pucrio.acad.45743
Fakhr, W., & Elmasry, M. I. (1994) · Springer eBooks · doi:10.1007/978-1-4615-2766-4_1
(2021) · The MIT Press eBooks · doi:10.7551/mitpress/11765.003.0010
Cios, K. J., Witold Pedrycz, C., & Świniarski, R. W. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5589-6_7
Berthold, M. R., Borgelt, C., Höppner, F., & Klawonn, F. (2010) · Texts in computer science · doi:10.1007/978-1-84882-260-3_9
Μπιζόπουλος, Π. (2019) · doi:10.12681/eadd/46590
Lange, C. (2004) · Wirtschaftswissenschaftliche Beiträge · doi:10.1007/978-3-7908-2696-8_1
The Turing Machine Constructed by Trainable Threshold Elements
(1977) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1977.4309649
Naoe, K., & Takefuji, Y. (2011) · Advances in IT standards and standardization research (AISSR) book series/Advances in IT standards and standardization research series · doi:10.4018/978-1-61350-135-1.ch004
Stavroulakis, P. P. (2004) · Signals and communication technology · doi:10.1007/978-3-642-18762-9_1
Axelson-Fisk, M. (2010) · Computational biology · doi:10.1007/978-1-84996-104-2_2
Danzer, K., Fischbacher, C., Hobert, H., & Jagemann, K.-U. (2001) · Springer eBooks · doi:10.1007/978-3-642-56584-7_7
Optimization of Arc Welding Processes using Neural Network
YAMANE, S. (2008) · JOURNAL OF THE JAPAN WELDING SOCIETY · doi:10.2207/jjws.77.34
(2014) · Wiley eBooks · doi:10.1002/9781118691786.refs
(2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817216-2.00016-8
de Biaz, B. M., Lopes, T. T. E., Fortes, M. Z., Ferreira, V. H., & de Mello, F. L. (2020) · Editora Poisson eBooks · doi:10.36229/978-65-86127-20-1.cap.09
Reconhecimento de Ações por RNA em Aplicações de Robótica Social
de Carvalho, K. B., Basílio, V. T., & Brandao, A. S. (2020) · Editora Poisson eBooks · doi:10.36229/978-65-86127-20-1.cap.11
(2005) · Elsevier eBooks · doi:10.1016/b978-012206093-9/50035-6
Piccinini, G. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198866282.003.0013
Cappy, A. (2020) · Wiley eBooks · doi:10.1002/9781119721802.refs
(2010) · Chapman & Hall/CRC data mining and knowledge discovery series · doi:10.1201/b10328-7
Les signatures neurobiologiques de la conscience
Sciences, A. E. I. D. (2021) · EDP Sciences eBooks · doi:10.1051/978-2-7598-2612-4
Haun, M. (2014) · Springer eBooks · doi:10.1007/978-3-662-44075-9_2
(1990) · Elsevier eBooks · doi:10.1016/b978-0-12-228640-7.50022-2
Runkler, T. A. (2020) · Springer eBooks · doi:10.1007/978-3-658-29779-4_6
(2004) · Elsevier eBooks · doi:10.1016/b978-044450208-7/50013-7
(2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809284-2.00041-1
Intellectual Property Regulation, and Software Piracy, a Predictive Model
D’Rosario, M. (2020) · IGI Global eBooks · doi:10.4018/978-1-7998-3016-0.ch076
Herrera, M., Meniconi, S., Alvisi, S., & Izquierdo, J. (2018) · MDPI eBooks · doi:10.3390/books978-3-03842-954-8
Pattern Classification by a Learning Algorithm Similar to Hebb's Modifiable Synapse
(1977) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1977.4309630
Najarian, K., & Eichelberger, C. N. (2006) · Wiley eBooks · doi:10.1002/9780471740360.ebs1458
Identificação de gênero e contorno corporal para estimativa de somatotipo
Neto, G. A., Ciarelli, P. M., & Salles, E. O. T. (2020) · Editora Poisson eBooks · doi:10.36229/978-65-86127-20-1.cap.10
Machine Learning in Morphological Segmentation
Lézoray, O., Lebrun, G., Meurie, C., Charrier, C., Elmotataz, A., & Lecluse, M. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-818-7.ch309
(2021) · The MIT Press eBooks · doi:10.7551/mitpress/11765.003.0009
Lynch, S. (2014) · Springer eBooks · doi:10.1007/978-3-319-06820-6_18
Berk, R. A. (2020) · Springer texts in statistics · doi:10.1007/978-3-030-40189-4_8
Zahner, D. A., & Micheli-Tzanakou, E. (1999) · The electrical engineering handbook/Electrical engineering handbook series/Electrical engineering handbook · doi:10.1201/9781420049510.ch182
Mou, L., & Jin, Z. H. (2018) · SpringerBriefs in computer science · doi:10.1007/978-981-13-1870-2_1
ANOTAÇÃO PROFUNDA DE PAPÉIS SEMÂNTICOS PARA O PORTUGUÊS
VARELA, G. S. A. (2019) · doi:10.17771/pucrio.acad.42944
Baragona, R., Battaglia, F., & Poli, I. (2010) · Statisctics and computing/Statistics and computing · doi:10.1007/978-3-642-16218-3_1
Cesa‐Bianchi, N. (2002) · Springer eBooks · doi:10.1007/978-3-7091-2568-7_3
Runkler, T. A. (2012) · Vieweg+Teubner eBooks · doi:10.1007/978-3-8348-2589-6_6
(2012) · Elsevier eBooks · doi:10.1016/b978-0-12-386908-1.00039-2
(1991) · Elsevier eBooks · doi:10.1016/b978-0-08-036979-2.50015-x
Sun, R. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816772.004
PREVISÃO DO ÍNDICE BURSATIL IBEX 35 USANDO REDES NEURAIS ARTIFICIAIS
Canillas, S. F., & MINUSSI, C. R. (2022) · doi:10.22533/at.ed.16222120519
da Cruz, C. M., de Jesus, A. C. G., & Ferreira, N. C. (2022) · doi:10.22533/at.ed.16222120514
ANÁLISE ESPECTRAL SINGULAR BASEADA NA FUNÇÃO DE HUBER
Cornejo, M. L., & Molinares, F. A. F. (2022) · doi:10.22533/at.ed.16222120513
O USO DA COMPUTAÇÃO GRÁFICA NO ENSINO DA GEOMETRIA ANALÍTICA
Porto, G., Hauenstein, D. M., & Ferreira, A. L. A. (2022) · doi:10.22533/at.ed.1622212056
Silva, A. J. D. J., Medeiros, B. D. V., Ferreira, P. L. V., & Moraes, L. L. M. (2022) · doi:10.22533/at.ed.1622212058
LUDICIDADE NO ENSINO APRENDIZAGEM: UMA ALIADA DA INTERVENÇÃO PEDAGÓGICA NA MATEMÁTICA
de Araújo, M. C. R. (2022) · doi:10.22533/at.ed.16222120512
DE LOS REALES A LOS COMPLEJOS, SÓLO HAY UN PEQUEÑO PASO
Radillo, M., Efremov, V., & González, J. M. C. (2022) · doi:10.22533/at.ed.1622212057
MODELOS MATEMÁTICOS E EPIDEMIAS
Franco, C. M. R., de Araújo, I. D., da Silva, M. F. C., & Lima, E. D. S. (2022) · doi:10.22533/at.ed.1622212053
ABORDAGENS NO ENSINO DE MATEMÁTICA: OS DESAFIOS DA SALA DE AULA NA EDUCAÇÃO BÁSICA
Burak, D., & Zontini, L. D. R. S. (2022) · doi:10.22533/at.ed.16222120517
Geração de transições curtas de animações humanas 3D
Rocha, F. N. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.100.2021.tde-26112021-123041
ESTUDO DOS FRACTAIS NAS SÉRIES E CÁLCULO NUMÉRICO
Belarmino, E. M., Milani, D. I. C., & Dalposso, G. H. (2022) · doi:10.22533/at.ed.1622212055
Filho, R. S., dos Santos, P. B., Correia, V. C. P., & da Silva, A. J. N. (2022) · doi:10.22533/at.ed.1622212052
INTRODUÇÃO ÀS IDENTIDADES FUNCIONAIS
Salomão, M. E. (2022) · doi:10.22533/at.ed.1622212059
GEOGEBRA: A TECNOLOGIA NO ENSINO DE MATEMÁTICA PARA ALUNOS SURDOS
da Silva, G. H., & Neigrames, W. P. (2022) · doi:10.22533/at.ed.16222120518
METODOLOGIA AULA INVERTIDA EN EL APRENDIZAJE DE LAS MATEMATICAS: UNA APROXIMACION CONCEPTUAL
Aguirre, M. M. V., Caballero, N. M. P., González, S. A. L., & Pineda, S. A. O. (2022) · doi:10.22533/at.ed.16222120520
DESDE LA FORMACIÓN PERMANENTE A LA COMPETENCIA PROFESIONAL
Sala, N. R., & Torrens, Y. C. (2022) · doi:10.22533/at.ed.16222120510
Applications of deep learning in single-cell analysis
Hollandi, R. (2021) · SZTE Repository of Dissertations (University of Szeged) · doi:10.14232/phd.10801
The Application of Machine Learning for Computational Electromagnetic Solver Selection
de la Bat, W. F., Ludick, D. J., & Grobler, T. (2022) · European Conference on Antennas and Propagation · doi:10.23919/eucap53622.2022.9769456
Automatic Prediction of Semantic Labels for French Medical Terms
Thierry Hamon, C., & Grabar, N. (2022) · Studies in health technology and informatics · doi:10.3233/shti220610
Artifial Intelligence in Interventional Radiology
Khakwani, S., Fawad, M. W. B., Ahmed, T., Hussain, T., & Arif, M. (2022) · Pakistan Journal of Medical and Health Sciences · doi:10.53350/pjmhs22165380
Benster, T., Babino, D., Thickstun, J., Hunt, M., Liu, X., Harchaoui, Z., Oh, S., & Gelder, R. N. V. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.10.482188
Tenório, R. B. D. A., Fernández, J. H., MENDES, D. G., & da Silva, J. P. (2022) · Anuário do Instituto de Geociências · doi:10.11137/1982-3908_2022_45_40763
ZoDIAC: Zoneout Dropout Injection Attention CalculationPreprint
Zohourianshahzadi, Z., & Kalita, J. K. (2022) · Research Square · doi:10.21203/rs.3.rs-1798795/v1
Kim, J.-Y. (2022) · Springer eBooks · doi:10.1007/978-3-030-98781-7_1
Role and Influence of Artificial Intelligence in Healthcare, Hybrid Imaging, and Molecular Imaging
Davidzon, G., & Franc, B. L. (2022) · Springer eBooks · doi:10.1007/978-3-031-00119-2_1
Burgos, J. E. (2022) · Springer eBooks · doi:10.1007/978-3-319-55065-7_793
Vector Learning: Digit Recognition by Learning the Abstract Idea of Curves
Sharma, D., Singh, A. J., & Sharma, D. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-07012-9_2
Deep Learning for Image/Video Restoration and Super-resolution
Tekalp, A. M. (2022) · doi:10.1561/9781680839739
An Improvement of CNN Model for Traffic Sign Recognition and Classification
Mekhaznia, T., & Fares, I. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-08277-1_30
(2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822952-1.00029-2
Marlo’s Networks of Expectations
Aznar, M. B. L., Acosta, G. C., & Gadea, W. F. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-10464-0_17
Kumar, K. N., & Aarti, A. (2022) · AIP conference proceedings · doi:10.1063/5.0081714
How Technológos "Responds" to What Used to Be Called "Images"
Ernst, W. (2021) · The Nordic Journal of Aesthetics · doi:10.7146/nja.v30i61-62.127863
Jeddah City Ride-hailing Company Bus Routes Planning by Using Traffic Neural Network Design
Najem, W. N., & Alqahtani, A. Y. (2021) · International Journal of Advanced Engineering Research and Applications · doi:10.46593/ijaera.2021.v06i10-11-12.001
A Quantamental Approach to Bitcoin Trading: Are We Swinging for the Fences?Preprint
Chicar, A. A., Roccatagliata, P., & Zabaljauregui, M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4148816
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