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 12,401–12,500 of 13,333
Deep learning methods for visual content creation and understanding
Sbai, O. (2021) · theses.fr (ABES) · doi:10.70675/aa60bca2zdda6z45cazbd02z4275271c264c
Artificial neural networks with radio-frequency spintronic nano-devices
Leroux, N. (2022) · theses.fr (ABES) · doi:10.70675/acc48b6dzf6c5z436dzbb4az611c0b52d106
Computing Tools for HPDA : a Cache-Oblivious and SIMD Approach
Peou, K. (2021) · theses.fr (ABES) · doi:10.70675/8f135146za2c5z4357z8eddzf685f7c46d08
Apprentissage profond robuste pour la conduite autonome
Corbière, C. (2022) · theses.fr (ABES) · doi:10.70675/5c346aa5z97a7z4ac6zbd7bzaea201403fd4
Voillemin, T. (2021) · theses.fr (ABES) · doi:10.70675/dfefad89z222fz4df2z8717z6f090aa100f7
On the interpretation of higher-order principal components
Mancoo, A. (2021) · theses.fr (ABES) · doi:10.70675/8753410dze43dz4436z95a8z3797457c62a8
Bonnaire, T. (2021) · theses.fr (ABES) · doi:10.70675/553b007dz2008z47fez9d3az684a68a11cb4
Memory Saving Strategies for Deep Neural Network Training
Shilova, A. (2021) · theses.fr (ABES) · doi:10.70675/6e1199d8z5998z46e3z9082z49b78a47d92c
Models and algorithms for automatic text simplification
Issouani, E. M. (2023) · theses.fr (ABES) · doi:10.70675/61c92463z4d90z46b7zb912zff7ec306af9d
Castillo-Navarro, J. (2022) · theses.fr (ABES) · doi:10.70675/f7f70aafz9f1dz4239zae57zb81d76ff3d48
Machine learning for human behavioral discovery
Aniszewska-Stępień, A. (2023) · theses.fr (ABES) · doi:10.70675/4f6849cczfa20z4484za0aez161e22e2e63c
Analysis and control of online interactions through neural natural language processing
Laugier, L. (2022) · theses.fr (ABES) · doi:10.70675/4743aa15z209az4addz8f29z95150aa230b8
Analyse statistique de l'évolution des sinistres graves pour une garantie risque corporel
Sabban, I. C. (2022) · theses.fr (ABES) · doi:10.70675/a68932d8z0d26z457dz9767ze0a7cf4e0ef5
On the evaluation and generalization of visual representations
Sarıyıldız, M. B. (2023) · theses.fr (ABES) · doi:10.70675/3a865f57z2943z4e75z8ecdz20502b52d317
Détection d'objets industriels à l'aide de modèles 3D dans des images égocentriques.
Cohen, J. (2022) · theses.fr (ABES) · doi:10.70675/0a2e2dc8zd16cz475ez9732z3a3979899b56
Analyzing markets with online data : mixing machine learning and econometrics
Hacheme, G. (2022) · theses.fr (ABES) · doi:10.70675/014940bczb01bz495azae2bzfcb58ead787f
Deep latent representations for visual servoing
Felton, S. (2022) · theses.fr (ABES) · doi:10.70675/f44cf2dcz3f18z4481z8b8bzd66ff5442b83
François, B. (2022) · theses.fr (ABES) · doi:10.70675/89b41f86z55c7z4177z8153zf59e6eab464e
Exploring the Intersection of Bayesian Deep Learning and Gaussian Processes
Kozyrskiy, B. L. (2023) · theses.fr (ABES) · doi:10.70675/780ab2d3z9288z42a8z98d5z58523527ec55
Online machine learning-based predictive maintenance for the railway industry
Nguyen, M. H. L. (2023) · theses.fr (ABES) · doi:10.70675/173d7e4fz2dcdz49d1z846cz5fe2ba50424a
Statistical learning in high dimensions : a rigorous statistical physics approach
Gerbelot, C. (2022) · theses.fr (ABES) · doi:10.70675/38971412z2276z4fc5za453zae35e71d53ec
Hong, B., Tianjiao, L., & Ying, Z. (2026) · Journal of Image and Graphics · doi:10.11834/jig.260112
Quantum algorithms for reinforcement learning
Cherrat, E. A. (2023) · theses.fr (ABES) · doi:10.70675/ec6ab96az80c1z473az8098z76724c736a49
Analyse de la posture par apprentissage automatique profond
Réby, K. (2022) · theses.fr (ABES) · doi:10.70675/1bf55499z84e5z4e1dz9936z094cc45628e4
Fenneteau, A. (2022) · theses.fr (ABES) · doi:10.70675/a86f491az16f9z469fzaff6zce551932f3bc
Accelerating fluid solvers using artificial intelligence techniques
Illarramendi, E. A. (2022) · theses.fr (ABES) · doi:10.70675/48a1951bzeff0z4c94zb8b9z15c42888efa9
Implicit parallelism for neural network acceleration
Wang, H. (2022) · theses.fr (ABES) · doi:10.70675/f756135cz8644z4865z91b2z67f75cd834e0
Constrained deep learning for MEMS sensors-based applications
Lê, M. T. (2023) · theses.fr (ABES) · doi:10.70675/cd0d8c3fze723z48f0zba6czeaaab993828c
Assistance virtuelle pour la prévention des risques cardiovasculaires
Rjiba, S. (2022) · theses.fr (ABES) · doi:10.70675/3eae137fz0e3az4e55za75ezf0faef982202
Neurophysiological and computational bases of goal-directed behavior
Basanisi, R. (2021) · theses.fr (ABES) · doi:10.70675/2b7d6591z8d6ez4ea4zbaefz320275b736b1
Learned and Hybrid Strategies for Control and Planning of Highly Automated Vehicles
Ghosn, A. B. (2023) · theses.fr (ABES) · doi:10.70675/f1936b83z847cz4005z9c43z4dad4611cb2b
Synthetic learning for neural image restoration methods
Achddou, R. (2023) · theses.fr (ABES) · doi:10.70675/8e38560azdc15z433bza0b0zd85660fd4b73
Système de vision embarqué pour l'amélioration de l'autonomie des drones sous-marins
Pennec, T. L. (2022) · theses.fr (ABES) · doi:10.70675/9167a0dez5d78z4491z884dz866b379fec2a
Potential and challenges of reinforcement learning for flow control
Paris, R. (2022) · theses.fr (ABES) · doi:10.70675/1577d7adz3155z4ba6z9a2dz05f8e4785540
Radiomics and Artificial Intelligence for personalized treatment of HPV-induced cancers
Niyoteka, S. (2023) · theses.fr (ABES) · doi:10.70675/307e872ezea33z470az8362z2fa498f30500
Reaching beyond the Standard Model in times of crises
Moutafis, R. (2022) · theses.fr (ABES) · doi:10.70675/2c08ee69zfb50z49fdzbc1azd830d6921b7d
Modeling and simulations of skyrmionic neuromorphic applications
Chen, X. (2022) · theses.fr (ABES) · doi:10.70675/652af953z1771z4795zb4cbz5598fdc45e86
Towards lighter and faster deep neural networks with parameter pruning
Hubens, N. (2022) · theses.fr (ABES) · doi:10.70675/c018d7baz3aa3z43b2z9f61z0d9bb05ff94e
Statistical learning methods combining the Bayesian approach and deep learning
Royer, H. (2022) · theses.fr (ABES) · doi:10.70675/ea05e3b8z1d57z46d7zad76z0dfd1b62730f
Regularized deep learning models for multi-anatomy segmentation in pediatric imaging
Boutillon, A. (2022) · theses.fr (ABES) · doi:10.70675/9b91c712z37e6z425dz9844zd2bb83d59f6b
Etude expérimentale et numérique de l'emboutissage à chaud des aciers Usibor®1500 et Usibor®2000
Morel, X. (2023) · theses.fr (ABES) · doi:10.70675/ce963d48zc223z4b75za054z82020745a959
Caractérisation de la qualité des raisins par imagerie
Lefevre, S. (2023) · theses.fr (ABES) · doi:10.70675/5c54917fzdacdz4302z9328za7f12b0553d3
How to train your global workspace? Evaluating and efficiently learning multimodal representations
Devillers, B. (2022) · theses.fr (ABES) · doi:10.70675/101815c7zb6faz411czaa4bz7149c56f0e5d
Non causal models : A closer look at unit root testing and forecasting
Saïdi, S. (2023) · theses.fr (ABES) · doi:10.70675/6153dfb3z1f25z40d9z8361z587cf96f211f
A Complex Wavelet Approach for Shift-Invariant Convolutional Neural Networks
Leterme, H. (2023) · theses.fr (ABES) · doi:10.70675/8c1279e4zdad3z4ab2za1eczc5fe1ed2d986
Apprentissage automatique interactif pour les opérateurs du réseau électrique
Crochepierre, L. (2022) · theses.fr (ABES) · doi:10.70675/2c5da813zbafaz4c5dzacb8z7ee964e419a5
Lasguignes, T. (2023) · theses.fr (ABES) · doi:10.70675/4c8ccd36z1853z4edfzbc13zf79277b7522e
Interactive semantic segmentation of aerial images with deep neural networks
Lenczner, G. (2022) · theses.fr (ABES) · doi:10.70675/ee3ef5caz8533z4cc0zae24z7acef8f26be8
Low energy excitations of vector spin glasses
Nicoletti, F. (2023) · theses.fr (ABES) · doi:10.70675/a707611fzc5e2z4622zb127za1d92e50310f
Dutrieux, H. (2022) · theses.fr (ABES) · doi:10.70675/da6b20afz322ez4814z9deazc07e377609ed
Convolutional neural networks pruning and its application to embedded vision systems
Tessier, H. (2023) · theses.fr (ABES) · doi:10.70675/73a5734fz6c44z4d68zb238z8d30e4e00f1a
Neuro-Symbolic Reinforcement Learning for RoboticsPreprint
Bisták, T., & Farkaš, I. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6537226
Nouvelles approches de prédiction de la pathogénicité des mutations
Corcuff, M. (2022) · theses.fr (ABES) · doi:10.70675/c935311cz1cc8z4cfdzbe8az384966c33808
Dosimétrie in-vivo et contrôle qualité en radiothérapie externe par réseaux de neurones
Chatrie, F. (2021) · theses.fr (ABES) · doi:10.70675/f61a8e7bzf9b8z435czb28azc51dcac991de
Réseaux de neurones à l'échelle nano. Quels modèles d'apprentissage ?
Martin, E. (2022) · theses.fr (ABES) · doi:10.70675/667a2206z88d8z4e84z9bc4z23781d5d862f
Embedded anomaly detection - Machine learning based radiation hardening of space electronics
Dorise, A. (2022) · theses.fr (ABES) · doi:10.70675/51a4a0b7z4663z4d76za49az08c5fa6ed40e
Highly imbalanced learning, application to fraud detection
Viola, R. (2022) · theses.fr (ABES) · doi:10.70675/48313a6dz4f32z48b6za7aaz09cef33295de
Approximation de flots géométriques : des méthodes de champ de phase aux réseaux de neurones
Terii, G. (2022) · theses.fr (ABES) · doi:10.70675/1c9139e3z2dccz48e6zbda3zc7805dea9918
Apprentissage profond pour la prévision de l'activité géomagnétique
Bernoux, G. (2022) · theses.fr (ABES) · doi:10.70675/c9a46ee6z9472z4dcbza68bzacdc8c072bb2
Pierré, W. (2022) · theses.fr (ABES) · doi:10.70675/c2a7bcb9z85cdz473cz9236z4ed68ae5ba87
3D scene reconstruction from images
Ramamonjisoa, M. (2022) · theses.fr (ABES) · doi:10.70675/b55ee9f9z4bc1z401fz800ez119f7f7ceaa3
Learning sub-grid dynamics in idealized turbulent systems
Frezat, H. (2022) · theses.fr (ABES) · doi:10.70675/dc02efcdzc60bz4cafzaf95z7bdf147c5955
Développement d'un système d'imagerie haute énergie et haute résolution
Maulin, M. (2022) · theses.fr (ABES) · doi:10.70675/b1560a9cz0ae1z446ez99cfz95c51d49ce5a
Synthèse de code virgule fixe pour les réseaux de neurones
Benmaghnia, H. (2022) · theses.fr (ABES) · doi:10.70675/27a5d0d4z766dz4b6fz9953z0e62b444668a
Expressivity transfer in deep learning based text-to-speech synthesis
Kulkarni, A. (2022) · theses.fr (ABES) · doi:10.70675/b44b847azf274z4851za996z397c3d49ca73
Deep learning multi-modal fusion based 3D object detection
Zhang, H. (2023) · theses.fr (ABES) · doi:10.70675/b9b489ffz335dz4ff4zbb03z08b285d48435
Modeling of neuron-astrocyte interaction : application to signal and image processing
Lorenzo, J. (2022) · theses.fr (ABES) · doi:10.70675/9e7d026ezff73z4f89z8b1azef7f2e29fb41
Analyse automatique sans annotation de manuscripts vietnamiens anciens
Scius-Bertrand, A. (2022) · theses.fr (ABES) · doi:10.70675/b2793da4zabb6z4210z9614zffce5a4b0465
Circuit design of an innovative logic/memory CUBE for in-memory-computing
Ezzadeen, M. (2022) · theses.fr (ABES) · doi:10.70675/9071f2dcz727cz434az98d7zbe5271bca998
Fortin, E. M. (2022) · theses.fr (ABES) · doi:10.70675/91648fc8zd1e6z4178zab11zd557ad1ab662
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