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 12,701–12,800 of 13,333
Graph Neural Networks for fluid mechanics : data-assimilation and optimization
Quattromini, M. (2024) · theses.fr (ABES) · doi:10.70675/78a75e36z1cbdz4ff0z8bc9z5379e6b8c9b5
Neural networks security under realistic scenario
Maho, T. (2023) · theses.fr (ABES) · doi:10.70675/690e25b2z1fb9z4d7fza6cbzcb09e2b773cf
Un système d'aide à la décision pour les négoces de matériaux
Souply, M. (2024) · theses.fr (ABES) · doi:10.70675/2f2c6bdbz93dfz41c9za45aze9277b54cb47
Machine Learning for Dynamical Systems Modelling
Noto, D. (2024) · theses.fr (ABES) · doi:10.70675/803225a0z13baz4ec1zbe08z87a890834e18
Advancing cosmological field-level inference with physics-informed Bayesian neural networks
Ding, S. (2025) · theses.fr (ABES) · doi:10.70675/b3b47cd9z9b36z4663zb245z1dce12a4a9fa
Pelloin, V. (2024) · theses.fr (ABES) · doi:10.70675/7b83ca8ez7b8dz4317za876zc0ca8f18c5a1
A Foundation For Binary Morphological Neural Networks
Aouad, T. (2024) · theses.fr (ABES) · doi:10.70675/06ca7bf2zc528z44dez88ddz615426690c95
Supervised learning with missing data : a non-asymptotic point of view
Ayme, A. (2024) · theses.fr (ABES) · doi:10.70675/9360885fz23e1z42fcz9814zafdb43b2c7c1
Astronomical Image Time-series Classification Using Deep Learning
Bairouk, A. (2023) · theses.fr (ABES) · doi:10.70675/a73685c6z5013z4ac7zbbfdza5ec8b8e9c73
Modelling coastal evolution with machine learning
Najar, M. A. (2023) · theses.fr (ABES) · doi:10.70675/5f260cf6zb572z4f99zb280z45190cf6712d
Probabilistic exponential smoothing for explainable AI in the supply chain domain
Cifonelli, A. (2023) · theses.fr (ABES) · doi:10.70675/e9f09ae2ze4f2z4b83z95d8zd60278b2d390
Domain Adaptation of Named Entity Recognition for Plant Health Monitoring
Borovikova, M. (2024) · theses.fr (ABES) · doi:10.70675/aa08ea7az594ez48adz814bz17ff1279b349
Deep Supervision of the Vocal Tract Shape for Articulatory Synthesis of Speech
Ribeiro, V. (2023) · theses.fr (ABES) · doi:10.70675/32fb3a74z320dz4f6aza740z06c496c14b41
Real-Time Anomaly Detection in OT Networks Using GRU-Based Autoencoders
Cash, T., Dumond, J., Corbitt, A., Schwartz, W. G., & Farnell, C. (2025) · IEEE Design Methodologies Conference (DMC) · doi:10.1109/dmc65958.2025.11475298
Chalvidal, M. (2023) · theses.fr (ABES) · doi:10.70675/d35a8a98zd3cfz4df4za6e9zf26f90d6f965
Large-scale atomistic modeling of complex materials and application to advanced memories
Lee, S.-H. (2023) · theses.fr (ABES) · doi:10.70675/4c2b88bcz7962z4614zb000z7e0d8a80081f
Leveau-Vallier, A. (2023) · theses.fr (ABES) · doi:10.70675/81a17aedz9606z4a43zbae4z06803d03897a
Siglidis, I. (2025) · theses.fr (ABES) · doi:10.70675/c09ef138zb2dfz4a0bz973fz1e85fb419f7c
Distributed and Parallel Computing for very Large Neural Networks
Petit, Q. R. (2025) · theses.fr (ABES) · doi:10.70675/b6bee80czf209z49b2z8063z0e05ef0d1c8c
Denakpo, E. (2025) · theses.fr (ABES) · doi:10.70675/2009394ez41a7z4b5cza4e7ze7b9167ee5c5
Maeda, T., Musa, M. A. B., Osada, T., & OZAKI, S. (2026) · Science and Technology of Advanced Materials Methods · doi:10.1080/27660400.2026.2656050
Deep learning methods for long RNA secondary structure prediction
Omnes, L. (2025) · theses.fr (ABES) · doi:10.70675/9694695ez729fz4193z8f2fzd5f35a61f7cd
Transfer Learning for Facial Analysis with Limited and Inconsistent Annotations
Dornier, M. (2023) · theses.fr (ABES) · doi:10.70675/26892aeez44caz4503z8f45z155477f1e86b
Laï-king, M. (2025) · theses.fr (ABES) · doi:10.70675/e9422bf7zf52dz461bza1a5z2593be8e49c3
Scaling up the static analysis of neural networks using affine forms
Soualah, A. (2024) · theses.fr (ABES) · doi:10.70675/8d07b5b1z3d4bz49e0zaa8az0014f4fd70ff
Estimation theory for learning processes
Aubert, J. (2025) · theses.fr (ABES) · doi:10.70675/4ecfc81cz002fz4804z8682zcc28e0074677
Computing with spiking photonic neural networks leveraging sparsity
Talukder, R. (2025) · theses.fr (ABES) · doi:10.70675/a58790d8zfe48z428az9b8dzc69e9174279f
Application of Machine Learning Tools for the Evaluation of Diabetic Retinopathy and Maculopathy.
Boité, H. L. (2025) · theses.fr (ABES) · doi:10.70675/162a1a98zb0bcz4fa6z8b7dz71a0fabc3a23
Atienza, N. (2025) · theses.fr (ABES) · doi:10.70675/81053b26z6d53z4f04zacc2zb3dd0156bba4
Improving control of neural networks facing visual perturbations
Cordier, T. (2025) · theses.fr (ABES) · doi:10.70675/a4edd425ze27ez4df1z890dz111029659b3b
Enhancing surrogate regression methods for structured prediction : An odyssey with loss functions
Junjie, Y. A. N. G. (2025) · theses.fr (ABES) · doi:10.70675/8a087025za007z4013zaa7dz6fde23f91948
Exploring learning techniques for edge AI taking advantage of non-volatile memories
Martemucci, M. (2024) · theses.fr (ABES) · doi:10.70675/ec79fa4cz56b6z4c04z86b9zd771c8285800
Robust watermarking for neural networks in multimedia tasks
Trias, C. D. S. (2025) · theses.fr (ABES) · doi:10.70675/875f9c2bz2068z460ez907bzdbe2b7d8453f
Neural network quantization methods for FPGA on-board processing of satellite images
Gernigon, C. (2024) · theses.fr (ABES) · doi:10.70675/889e2f05z3097z4bccz9415z7a6540dd1211
Durrmeyer, A. (2025) · theses.fr (ABES) · doi:10.70675/87f0af2ez14e6z4511za876z71d8710f9584
Leveraging Stochastic properties of Spintronic Nanodevices for Unconventional Computing
Phan, N.-T. (2025) · theses.fr (ABES) · doi:10.70675/d0f813e2z914bz4492z9198z52d8ecea47ec
37512 Grundlagen des Maschinellen Lernens
Bauckhage, C., Hübner, W., Hüllermeier, E., Hug, R., Paaß, G., & Stefan Rüping, A. (2026) · doi:10.1515/9783111386690-015
Semi-supervised deep learning for robust anomaly detection in smart manufacturing systems
Le, T. (2024) · theses.fr (ABES) · doi:10.70675/19d23228zf030z4df7za3abz596d36577818
Décimatiοn hiérarchique pοur l'apprentissage sur graphes
Stanovic, S. (2025) · theses.fr (ABES) · doi:10.70675/a0861641z921cz48d8zb8a0z0830edcef2b8
Adaptive predictive maintenance strategies for smart buildings
Hossini, A. (2025) · theses.fr (ABES) · doi:10.70675/020eecffzb76cz489az9e2az41368185a172
Deep learning for partial volume correction in SPECT imaging
Kaprélian, T. (2025) · theses.fr (ABES) · doi:10.70675/7fb45775zf8c4z4961z9589z9be8710187f8
Transcriptomics data generation with deep generative models
Lacan, A. (2025) · theses.fr (ABES) · doi:10.70675/4bbb4d4fzed5ez4f41z86f4zf6f892f9e3e8
Hübner, W., & Hug, R. (2026) · doi:10.1515/9783111386690-016
Plaza, F., Eleuterio Yáñez, R., Fréon, P., Araya, H., & Aranís, A. (2026) · Deep Sea Research Part II Topical Studies in Oceanography · doi:10.1016/j.dsr2.2026.105645
The symmetric perceptron: A teacher–student scenario
Catania, G., Decelle, A., & Korpe, S. (2026) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2026.131544
Wang, J., Zhou, X. H., Xie, J., Tie, J., Hu, K., Wan, Y., Liu, J., & Zhang, W. (2026) · Research Square · doi:10.21203/rs.3.rs-9127892/v1
A Multivariate Time Series Forecasting Model Based on Exponential Moving Average
Bai, D. (2025) · International Conference on Computational Modeling, Simulation and Data Analysis (CMSDA) · doi:10.1145/3796731.3796798
Takahashi, J., & Fujii, H. (2026) · Journal of Information Processing · doi:10.2197/ipsjjip.34.309
Niu, H., Westphal, A., & Chen, Y. (2026) · Nematology · doi:10.1163/15685411-bja10478
Machine Learning and Covariance Matrices
Lan, W., & Tsai, C. (2026) · Springer eBooks · doi:10.1007/978-3-032-08796-6_9
Wu, M. (2026) · Springer eBooks · doi:10.1007/978-3-032-15872-7_2
Functional modeling of lithium-ion battery degradation for enhanced capacity prediction
Lee, D. Y., & Ahn, K. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.122145
Geometry parameters estimation in Monte Carlo simulation using machine learning
Yoon, S., Kunkle, J. M., & Liu, B. (2026) · Applied Radiation and Isotopes · doi:10.1016/j.apradiso.2026.112633
A comprehensive survey of pattern recognition classification algorithms
Wang, J., Wang, Z., Dong, Q., Zhang, S., Liu, M., & Zhang, J. (2026) · International Conference on Power Electronics and Artificial Intelligence (PEAI) · doi:10.1117/12.3111366
From Black Box to Symbolic Insight
Márquez-Vera, M. A., Ma'arif, A., & Balderrama-Hernández, B. D. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407567
Alagbada, A. V., Iroko, T. C., & Tchoneteck, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407565
Nakayama, N., Yokomaku, K., Matsunaga, N., Fujisya, K., & Nakamura, T. (2026) · Diagnostic Microbiology and Infectious Disease · doi:10.1016/j.diagmicrobio.2026.117423
Shaping digital transition in water engineering
Giustolisi, O. (2026) · Journal of Hydraulic Research · doi:10.1080/00221686.2026.2643704
Behbahani, A. A., & Shahbandarzadeh, H. (2026) · Journal of Ecotourism · doi:10.1080/14724049.2026.2637943
The Quest for Conscious, Aligned, Artificial General Intelligence (AGI)
Edwards, D. J. (2026) · Springer eBooks · doi:10.1007/978-3-032-15628-0_1
Topology-Enhanced Deep Learning
Bautista, R. B. (2026) · Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya)
The Role of Artificial Intelligence for Drug Repurposing in Alzheimer’s Disease
Guntupalli, S., Bykova, M., Martin, W., & Cheng, F. (2026) · Topics in medicinal chemistry · doi:10.1007/7355_2026_191
Canonical MachinesPreprint
Hermosis, E. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6387761
Mistake gating leads to energy and memory efficient continual learningPreprint
Pache, A., & van Rossum, M. C. W. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.16.718919
Harnessing Deep Learning in Searching Wild Relatives of Domestic Animals
Cai, Z., Liu, B., Yin, T.-T., Ma, C., Han, Z., Gu, L., Zhang, C., Wu, F., Liu, L., Shi, X., et al. (2026) · Molecular Ecology Resources · doi:10.1111/1755-0998.70133
Sun, M., Wei, C., Hu, Q., & Han, X. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.125595
Leveraging Apache Storm for Real-Time Data Stream Processing and Online Machine Learning
Sayani, P. K. (2025) · Conference on AI x Software Engineering (AIxSE) · doi:10.1109/aixse64906.2025.00022
Cross-Docking vs Warehousing: A Machine Learning-based DecisionPreprint
Zhang, F., Pirnia, M., Bookbinder, J. H., & Nasiriyani, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6625687
Integração de Chatbots de Recursos Humanos- Um estudo de caso numa empresa no setor automóvel
Neves, M. F. P. (2025) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Artificial Intelligence in Medical Informatics: From Algorithms to Clinical Practice—A Survey
Luo, Y.-F., Bu, D., Wu, Y., & ZHAO, Y. (2026) · Journal of Computer Science and Technology · doi:10.1007/s11390-026-6030-2
Forecasting with simultaneous equation models: a synthetic data generator for structural evaluation
González, M., Peñalver, A., & López-Espín, J. J. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01117-6
Roobaert, L., & Claeys, D. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-19495-4_7
Olivas, E. S., Belda, E. A., Salvador, D. B., Cámara, P. H., Isla, M. A. S.-M., & Belenguer, P. R. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-15854-3_1
Optimization of weighted ensembles of genomic prediction models in maize
Tomura, S., Powell, O. M., Wilkinson, M. J., Lefevre, J., & Cooper, M. (2026) · in silico Plants · doi:10.1093/insilicoplants/diag010
How do autonomous software agents learn? A systematic literature review
Hernandez, J., Peluffo-Ordóñez, D. H., & Flórez, H. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3756
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