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 5,701–5,800 of 13,333
L'évolution disciplinaire des sciences de l'information : des technologies à l'ingénierie des usages
Claverie, B., & Fouillat, P. (2011) · Projectics / Proyéctica / Projectique · doi:10.3917/proj.006.0079 · Cited by 6
Feldman, A. G., & Levin, M. F. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081607 · Cited by 6
The Structure of Version Space
Herbrich, R., Graepel, T., & Williamson, R. C. (2006) · Springer eBooks · doi:10.1007/3-540-33486-6_9 · Cited by 6
Regularized Statistical Analysis of Anatomy
Sjöstrand, K. (2009) · Cited by 6
Domany, E., & Meir, R. (1991) · Physics of neural networks · doi:10.1007/978-3-642-97171-6_9 · Cited by 6
Learning Sparse Confidence-Weighted Classifier on Very High Dimensional Data
Tan, M., Yan, Y., Wang, L., van den Hengel, A., Tsang, I. W.-H., & Shi, Q. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10281 · Cited by 6
Data Input and Output Representations
Jackson, T. O. (1996) · Taylor & Francis eBooks · doi:10.1201/9781420050646.ptb4 · Cited by 6
Multi-branch structure of layered neural networks
Yamashita, T., Hirasawa, K., Hu, J., & Murata, J. (2003) · SICE Annual Conference (SICE) · doi:10.1109/sice.2002.1195252 · Cited by 6
History of the Study of Gesture
Kendon, A. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0004 · Cited by 6
Adaptive Kernels for Support Vector Classification
Burbidge, R. (2003) · Lecture notes in statistics · doi:10.1007/978-0-387-21579-2_22 · Cited by 6
CNN Based Handwritten Character Recognition
Wang, S., Chen, L., Wu, C., Fan, W., Sun, J., & Naoi, S. (2017) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789813143685_0003 · Cited by 6
Şen, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-29555-3_2 · Cited by 6
Pattern Analysis and Classification
Coomans, D. H., & de Vel, O. Y. (1995) · The handbook of environmental chemistry · doi:10.1007/978-3-540-49148-4_9 · Cited by 6
Pabico, J. P., Gonzales, A. M. V., Villanueva, M. J. S., & Mendoza, A. A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.05546 · Cited by 6
The EEG of the neonatal brain : classification of background activity
Löfhede, J. (2009) · Chalmers Publication Library (Chalmers University of Technology) · Cited by 6
Semi-supervised structured prediction models
Brefeld, U. (2008) · edoc Publication server (Humboldt University of Berlin) · doi:10.18452/15748 · Cited by 6
Artificial neural networks and geographic information systems for inland excess water classification
van Leeuwen, B. (2012) · doi:10.14232/phd.1520 · Cited by 6
Redes neuronales y preprocesado de variables para modelos y sensores en bioingeniería
Mateo, F. (2012) · doi:10.4995/thesis/10251/16702 · Cited by 6
THE CLOSED LOOP ANTAGONISTIC NETWORK (CLAN)
Hartmann, G. (1990) · Elsevier eBooks · doi:10.1016/b978-0-444-88400-8.50037-6 · Cited by 6
Neural network based learning of local compatibilities for segment grouping
Rivière, D., Mangin, J.-F., Martinez, J., Chavand, F., & Frouin, V. (1998) · Lecture notes in computer science · doi:10.1007/bfb0033253 · Cited by 6
Artificial intelligence and animal psychology
Boden, M. A. (1983) · New Ideas in Psychology · doi:10.1016/0732-118x(83)90026-0 · Cited by 6
From Brains to Neural Nets to Brains
Harth, E. M. (1997) · Neural Networks · doi:10.1016/s0893-6080(97)00048-8 · Cited by 6
Fast high-dimensional Bayesian classification and clustering
Nia, V. P. (2009) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-4482 · Cited by 6
Advances in discriminative dependency parsing
Collins, M., & Koo, T. (2010) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 6
Approximately periodic time series and nonlinear structures
Baier, N. U. (2005) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-3113 · Cited by 6
Learning discrete mappings-Athena's approach
Koutsougeras, C., & Papachristou, C. (2003) · IEEE Workshop on Languages for Automation · doi:10.1109/lfa.1988.24948 · Cited by 6
THE PHASE-SPACE DYNAMICS OF SYSTEMS OF SPIKING NEURONS
Banerjee, A., & Hirsh, H. (2001) · Cited by 6
The psychology of connectionism
Massaro, D. W. (1990) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00079450 · Cited by 6
A Novel Mathematical Framework for the Analysis of Neural Networks
Caterini, A. L. (2017) · UWSpace (University of Waterloo) · Cited by 6
Instantaneous Learning Neural Networks.
Tang, K. (1999) · doi:10.31390/gradschool_disstheses.7130 · Cited by 6
Using interior point methods for large-scale support vector machine training
Woodsend, K. (2010) · ERA · Cited by 6
Complexity Theoretic Limitations on Learning HalfspacesPreprint
Daniely, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05800 · Cited by 6
Convex Set Disjointness, Distributed Learning of Halfspaces, and LP FeasibilityPreprint
Braverman, M., Kol, G., Moran, S., & Saxena, R. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03547 · Cited by 6
Closure Properties for Private Classification and Online PredictionPreprint
Alon, N., Beimel, A., Moran, S., & Stemmer, U. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04509 · Cited by 6
Deep learning the atmospheric boundary layer heightPreprint
Vivas, D. R., Sánchez, E., & Reina, J. H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.04353 · Cited by 6
Shen, J., & Zhang, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.03781 · Cited by 6
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language ModellingPreprint
Munkhdalai, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.01803 · Cited by 6
Agnostic Learning of Halfspaces with Gradient Descent via Soft MarginsPreprint
Frei, S., Cao, Y., & Gu, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00539 · Cited by 6
A partition-based similarity for classification distributionsPreprint
Helm, H. S., Mehta, R., Duderstadt, B., Yang, W., White, C. M., Geisa, A., Vogelstein, J. T., & Priebe, C. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06557 · Cited by 6
The Barrier of meaning in archaeological data sciencePreprint
Casini, L., Roccetti, M., Delnevo, G., Marchetti, N., & Orrù, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06022 · Cited by 6
A new spin on optimal portfolios and ecological equilibriaPreprint
Garnier-Brun, J., Benzaquen, M., Ciliberti, S., & Bouchaud, J. (n.d.) · RePEc: Research Papers in Economics · Cited by 6
Active data selection in supervised and unsupervised learning
Hasenjäger, M. (2000) · Cited by 6
Kanal, L. N. (2001) · Elsevier eBooks · doi:10.1016/b0-08-043076-7/00572-6 · Cited by 6
Investigating the detection of Tortured Phrases in Scientific LiteraturePreprint
Lay, P., Lentschat, M., & Labbé, C. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.13024 · Cited by 6
Livne, O. E. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-818630-5.10093-4 · Cited by 6
Rahimi, R., Vahedizadeh, S., & Ebtehaj, A. M. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.02236 · Cited by 6
Techniques d'anormalité appliquées à la surveillance de santé structurale
Cury, A. (2010) · theses.fr (ABES) · doi:10.70675/4bf5ad50z6f36z44cazb501zee64e3b6c223 · Cited by 6
Regression and Classification with Single-Qubit Quantum Neural NetworksPreprint
de Souza, L. C., Guingo, B. C., Giraldi, G. A., & Portugal, R. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2412.09486 · Cited by 6
Xu, H., & Dinev, T. (2022) · MIS Quarterly · doi:10.25300/misq/2022/464e2 · Cited by 6
Ultrasonic flaw classification: an approach using modeling, signal processing, and adaptive learning
Koo, L. S. (1987) · doi:10.31274/rtd-180813-8676 · Cited by 6
Ferroelectric tunnel junctions : memristors for neuromorphic computing
Boyn, S. (2016) · theses.fr (ABES) · doi:10.70675/e7c2cd52z53cdz4426z801dz58e6a2127289 · Cited by 6
Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring
Pol, A. A. (2020) · theses.fr (ABES) · doi:10.70675/2568cf6bzdeaez426aza0c2z579be998f544 · Cited by 6
Complex-valued neural networks for radar applications
Barrachina, J. A. (2022) · theses.fr (ABES) · doi:10.70675/1f11f260zfaa9z4a2dz8186za3b7d9ef2032 · Cited by 6
A priori Knowledge from Non-Examples
Sinz, F. H. (2007) · MPG.PuRe (Max Planck Society) · Cited by 6
Foundations and Advances in Deep Learning
Cho, K. (2014) · Aaltodoc (Aalto University) · Cited by 6
Zeki Güngördü, M., Kung, P., & Kim, S. M. (2023) · Optics Express · doi:10.1364/oe.481094 · Cited by 5
Downs, T., & Gaynier, R. J. (1995) · Mathematical and Computer Modelling · doi:10.1016/0895-7177(95)00180-a · Cited by 5
Online neural sliding mode control with guaranteed stability
Elkins, J. G., & Fahimi, F. (2024) · International Journal of Control · doi:10.1080/00207179.2024.2332521 · Cited by 5
Physics-informed loss functions for vertical total electron content forecast
Asamoah, E. N., Cafaro, M., Epicoco, I., Franceschi, G. D., & Cesaroni, C. (2024) · Earth Science Informatics · doi:10.1007/s12145-024-01294-7 · Cited by 5
Boukhris, A., Antari, J., & Asri, H. (2024) · Revue d intelligence artificielle · doi:10.18280/ria.380209 · Cited by 5
An Adaptive Graph Neural Networks Based on Cost-sensitive Learning for Fraud Detection
Yan, Q., Sun, Y., Cao, Y., Yang, J., Zhang, A., Ju, J., Shi, W., Yang, X., Yin, J., & Wang, Z. (2024) · International Symposium on Autonomous Systems (ISAS) · doi:10.1109/isas61044.2024.10552392 · Cited by 5
Automatic recognition of active landslides by surface deformation and deep learning
Wang, X., Chen, W., Ren, H., & Guo, H. (2024) · Progress in Physical Geography Earth and Environment · doi:10.1177/03091333241276523 · Cited by 5
Temperature fields prediction for the casting process by a conditional diffusion model
Kang, J., Zhu, J., & Zhao, Q. (2024) · China Foundry · doi:10.1007/s41230-024-4016-7 · Cited by 5
Yang, F., Ye, Z., Chen, Y., Zhou, P., & Gou, F. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c03250 · Cited by 5
Mun, J., Jo, H.-G., & Park, C. S. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115359 · Cited by 5
Hansen’s Accessibility Theory and Machine Learning: a Potential Merger
Hadjidimitriou, N. S., Reggiani, A., Östh, J., & Mamei, M. (2025) · Networks and Spatial Economics · doi:10.1007/s11067-025-09674-2 · Cited by 5
Machine Learning Solar Full Disk Flare Operational Forecasting
Li, M., Cui, Y., & Luo, B. (2025) · Chinese Journal of Space Science · doi:10.11728/cjss2025.01.2024-0021 · Cited by 5
Sakka, M., & Bahrami, M. R. (2025) · Applied Sciences · doi:10.3390/app15042008 · Cited by 5
Excitable dynamics simplify neural connectomes
Messé, A., Hütt, M. ‐., & Hilgetag, C. C. (2025) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2025.102510 · Cited by 5
Profile of John Hopfield and Geoffrey Hinton: 2024 Nobel laureates in Physics
McClelland, J. L. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2423094122 · Cited by 5
Environmental factors impacting phytoplankton response to the cold water mass in the Yellow Sea
Ding, L., Li, J., Zhang, Q., Fu, M., & Yang, S. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.145556 · Cited by 5
Hong, S. (2025) · Micromachines · doi:10.3390/mi16050557 · Cited by 5
Agrawal, K. K., & Kumar, G. (2025) · Research in Veterinary Science · doi:10.1016/j.rvsc.2025.105703 · Cited by 5
Brcković, A., Malvić, T., Orešković, J., & Kapuralić, J. (2025) · Geosciences · doi:10.3390/geosciences15060206 · Cited by 5
Foundational approaches to post-hoc explainability for image classification
Santis, A. D., Campi, R., Bianchi, M., Tocchetti, A., & Brambilla, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-340553-2.00008-3 · Cited by 5
Li, Z., Xia, T., Chang, Y., & Wu, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130711 · Cited by 5
Abdalazeem, M., & Oke, J. B. (2025) · Data Science for Transportation · doi:10.1007/s42421-025-00128-2 · Cited by 5
TSE-APT: An APT Attack-Detection Method Based on Time-Series and Ensemble-Learning Models
Cheng, M., Xiang, G., Yang, Q., Ma, Z., & Zhang, H. (2025) · Electronics · doi:10.3390/electronics14152924 · Cited by 5
Artificial intelligence in drug design: why a ‘one-size-fits-all’ approach remains out of reach
Almeida, R. L., Campera, G. M., Pöhner, I., & Maltarollo, V. G. (2025) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2025.2543802 · Cited by 5
Grey clustering machine learning model for predicting voluntary employee turnover
Nowak, M. (2025) · Grey Systems Theory and Application · doi:10.1108/gs-02-2025-0020 · Cited by 5
Lee, T. K., Choi, I., & Kim, W. C. (2025) · Symmetry · doi:10.3390/sym17091372 · Cited by 5
Soria-López, A., Simal‐Gándara, J., & Mejuto, J. C. (2025) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2025.128401 · Cited by 5
Auby, H., Shivagunde, N., Deshpande, V., Rumshisky, A., & Koretsky, M. (2025) · Journal of Engineering Education · doi:10.1002/jee.70032 · Cited by 5
AI-based hardware and software tools in microscopy to boost research in immunology and virology
Morone, D., & D’Antuono, R. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1610345 · Cited by 5
Deep Learning and the Internet of Things
Byabazaire, J., Tirana, J., Chouliaras, A., Koutsos, V., Aslanidis, T., Panagiotidis, I., & Chatzopoulos, D. (2025) · Wiley eBooks · doi:10.1002/9781394280490.ch8 · Cited by 5
Electron neural closure for turbulent magnetosheath simulations: Energy channels
Miloshevich, G., Vranckx, L., Lopes, F. N. D. O., Dazzi, P., Arrò, G., & Lapenta, G. (2026) · Physics of Plasmas · doi:10.1063/5.0300009 · Cited by 5
Implementation to identify the properties of multiple populations of gravitational wave sources
Qazalbash, M., Zeeshan, M., & O'Shaughnessy, R. W. (2026) · Physical review. D/Physical review. D. · doi:10.1103/krnm-3vrf · Cited by 5
Zhang, B., Zhang, H., Li, W., Chen, N., & Jiang, X. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3626524 · Cited by 5
Shack‒Hartmann波前传感器研究进展及应用(特邀)
Qianqian, 于. Y., Yangyang, 李. L., Ming, 秦. Q., & Xiang, 郝. H. (2025) · Acta Optica Sinica · doi:10.3788/aos251187 · Cited by 5
Human in the loop chain of code prompting for deterministic tool development with generative AI
Atkinson, C. F. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00704-z · Cited by 5
Mirzaei, A. M. (2026) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2026.109497 · Cited by 5
Anomaly detection with spiking neural networks for LHC physics
Dillon, B. M., Harkin, J., & Javed, A. (2026) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ae4a85 · Cited by 5
r -process heating implementation in hydrodynamic simulations with neural networks
Just, O., Xiong, Z., & Martínez-Pinedo, G. (2026) · Physical review. D/Physical review. D. · doi:10.1103/gl2l-7f3g · Cited by 5
The evolution of AI in city planning
Batty, M. (2026) · Discover Cities · doi:10.1007/s44327-026-00224-0 · Cited by 5
Pishro, A. A. (2026) · Structures · doi:10.1016/j.istruc.2026.112103 · Cited by 5
Nakada, M., & Osana, Y. (2008) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.20.423 · Cited by 5
Ensemble of Linear Experts as an Interpretable Piecewise Linear Classifier
Hartono, P. (2008) · Cited by 5
Ohmic Weave: Memristor-Based Threshold Gate Networks
Mountain, D. J., McLean, M. M., Palmer, D., Prucnal, J. D., & Krieger, C. D. (2015) · Computer · doi:10.1109/mc.2015.369 · Cited by 5
Perception evolution network: adapting to the emergence of new sensory receptor
Xing, Y., Shen, F., & Zhao, J. (2015) · International Conference on Artificial Intelligence · Cited by 5
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