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 11,501–11,600 of 13,333
Cui, P., Xue, Y., & Li, J. (2025) · Materials Today Sustainability · doi:10.1016/j.mtsust.2025.101199
Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding
Kulecki, B., & Belter, D. (2025) · Foundations of Computing and Decision Sciences · doi:10.2478/fcds-2025-0015
How psychologists kick-started AI by studying the human mind
Ludlow, C., & Zarnegar, A. (2025) · The Conversation · doi:10.64628/aa.ndn96dths
BOOST: Batch Ordering for Training Deep Forecasting Models
Anaraki, J. R., & Guo, L. (2025) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac65507.2025.00043
Manuel, S., & Taschereau‐Dumouchel, V. (2025) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/pgfyw_v1
Cognitive Architecture Analysis of LargePreprint
Moghadasi, M. N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5412641
Checkpoint Cascading Prompt: A Novel Approach to Text RewritingPreprint
mahmood, A., Ahmad, Z., Mahmood, M., & Farid, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5413943
XPreprint
Bonal, E. (2025) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/j36dq_v1
Classification by Separating Hypersurfaces: An Entropic ApproachPreprint
Arratia, A., Daou, M. E., & Gzyl, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5427695
An Overview of Artificial Intelligence in Neurology
Parker, T., & Brush, B. (2025) · Seminars in Neurology · doi:10.1055/a-2693-0547
Emerging innovation, artificial intelligence, and digital technology in esophagology
Shah, E. D., Chiang, A. L., Yip, H. C., Marescaux, J., Swanström, L. L., Reddy, R. M., Visaggi, P., & Lynch, K. L. (2025) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.70031
Győri, R., & Withers, C. W. J. (2025) · Földrajzi Közlemények · doi:10.32643/fk.149.1.1
AZ ÚJ GENERÁCIÓS MESTERSÉGES INTELLIGENCIA ÉS A DIGITALIZÁCIÓ KAPCSOLATA ÉS HATÁSUK A GAZDASÁGRA
Zhang, J. (2025) · Földrajzi Közlemények · doi:10.32643//fk.149.1.4
Przystalski, K., OGORZAŁEK, M. J., Argasiński, J. K., & Chmielnicki, W. (2025) · Springer eBooks · doi:10.1007/978-3-031-91816-2_8
Supervised Machine Learning and Clinical Decision Support
Bukowiec, L. G., & Lu, Y. (2025) · Hand Clinics · doi:10.1016/j.hcl.2025.08.001
Randomly sparsified Richardson iteration: A dimension‐independent sparse linear solver
Weare, J., & Webber, R. J. (2025) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.70012
Heidari, I., & Sepehri, M. M. (2025) · American Journal of Infection Control · doi:10.1016/j.ajic.2025.09.004
KANLoc: WiFi Localization with A Lightweight KAN
Gu, Y., Xu, M., Li, M., Li, J., Li, J., Guan, L., & Huang, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04555-3_22
Schweizer-Schubert, S., von Waldeyer-Hartz, G., Schütze, S.-E., Mahringer, D., Ruhl, A. S., Graf, M., & Kuckelkorn, J. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1583203
New Convex-Based Metamorphic Relations and Large-Scale Machine Learning Model Evaluation
Colonval, J., & Bouquet, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05188-2_23
Vision-Guided Robotic System for Automated Waste Sorting
Kosal, C., Chham, V., Voeurn, Y. A., & Prum, S. (2025) · International Conference on Software, Knowledge, Information Management and Applications (SKIMA) · doi:10.1109/skima66621.2025.11155547
Advanced Predictive Models for Saturated Flow Boiling in Horizontal Small-Scale Channels
Zeroual, Z., Akdemir, Ö., Turgut, O. E., & Çoban, M. T. (2025) · Heat Transfer Engineering · doi:10.1080/01457632.2025.2553978
Uso inteligente de las tecnologías de inteligencia artificial
Useda, M. E. G. (2025) · Revista Educación en Ingeniería · doi:10.26507/rei.v20n40.1380
Rossi, D. L., & Ueyama, J. (2024) · doi:10.11606/003228528
Human–AI Collaboration for Scientific Discovery
Zhao, S., Liu, Y., Wan, J., Tang, T., & Li, X. S. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009587877.011
Özsoy, K., & Aksoy, B. (2025) · Fırat Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.35234/fumbd.1739022
Optimization Basics: A Machine Learning View
Aggarwal, C. C. (2025) · Springer eBooks · doi:10.1007/978-3-031-98619-2_4
Backward Signal Propagation: A Symmetry-Based Training Method for Neural Networks
Jiang, K., & Fu, Z. (2025) · Algorithms · doi:10.3390/a18100594
Sujjada, A., Rahmatulloh, R., Suganda, & Maulana, A. (2025) · International Global Conference Series on ICT Integration in Technical Education & Smart Society · doi:10.3390/engproc2025107100
Elavarasi., A., & PeriyaNayaki, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2297-1.ch003
ELECTRUM: An Electron Configuration-based Universal Metal Fingerprint for Transition Metal CompoundsPreprint
Frei, A., & Orsi, M. (2025) · ChemRxiv · doi:10.26434/chemrxiv-2024-vqktn-v2
Climate Change Impacts on Crop Yield Prediction: A Machine Learning Perspective
Elreedy, D., & Atiya, A. F. (2025) · Sustainability Sciences in Asia and Africa · doi:10.1007/978-981-96-9630-7_15
Zhao, T., Zhang, C., & Jiang, B. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-28953-8.00010-8
From statistics to machine learning: principles of data analysis
Oliva, P., Fantacci, M. E., & Retico, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29242-2.00006-9
An Ablation Study on ZoDIAC Via Utilization of Convolutional Features
Zohourianshahzadi, Z., Boult, T. E., & Kalita, J. K. (2025) · International Journal of Semantic Computing · doi:10.1142/s1793351x25440039
Earley, A., Chhabra, A., Ozmeral, E. J., Lertpoompunya, A., Eddins, D. A., & Higgins, N. C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.26.678869
de Almeida, V. A. D. (2025) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.18.2025.tde-30092025-110906
Masunungure, T., & Engelbrecht, A. P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02725-2_47
Optimizing ECG Classification with Artificial Neural Networks for Enhanced Cardiac Care
Shahzad, M. R., & Raj, V. F. D. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-96-7059-8_43
Barbosa, W. F., da Silva, A. C., de Sousa, I. C., Chagas, F. E. D. O., Siqueira, M. J. S., Bhering, L. L., Nascimento, M., & Cruz, C. D. (2025) · Acta Scientiarum Agronomy · doi:10.4025/actasciagron.v47i1.72552
Deriving the Gradients of Some Popular Optimal Transport AlgorithmsPreprint
Xie, F. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2504.08722
A Critical Review for Application of Artificial Intelligence in Predicting Sand Production.
Naji, J. M. A. S., Ahmed, M. A., Ali, ِ. M., & Hammad, H. M. (2024) · Iraqi journal of oil and gas researches · doi:10.55699/ijogr.2024.0402.1069
Introduction to Conceptual Networks applied to Automatic Machine LearningPreprint
Boucher, P., & Cardinal, P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5576874
Artificial Neural Networks, Postdigital Infrastructures and the Politics of Temporality
Sudmann, A. (2025) · Routledge eBooks · doi:10.5117/9789463727426-19
Lean network strategies to ensure resilience: the role of account giving
Vahedian, A., Woldt, J., & Prasad, S. (2025) · Supply Chain Management An International Journal · doi:10.1108/scm-12-2024-0855
Self-supervised learning with BYOL for anterior cruciate ligament tear detection from knee MRI
Aidarkhan, A., Mukhamediya, A., Askhatova, A., Beisembekova, R., Alimbayeva, Z. N., Ozhikenova, A. K., Ozhikenov, К., & Jamwal, P. K. (2025) · MethodsX · doi:10.1016/j.mex.2025.103664
Künstliche Intelligenz – ein Überblick
Werner, S., & Arzig, C. (2025) · Springer eBooks · doi:10.1007/978-3-662-71567-3_3
Souza, R. C., Rosa, N. M. P., Duarte, J. C., & Borges, I. (2025) · Química Nova · doi:10.21577/0100-4042.20250228
Blockchain-Integrated Support Vector Machine Framework for Privacy-Preserving Predictive Analytics
Yojana, Y., & Chaba, Y. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.4055
Park, S., Nguyen, M. T., Jeon, J., Yoo, K., Oh, J.-E., Shin, J.-H., & Bae, H. (2025) · Environmental Engineering Research · doi:10.4491/eer.2025.240
Maxwell, A. E., Ramezan, C. A., & He, Y. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29306-1.00012-0
On quantum perceptron learning via quantum search
Sun, X., Roget, M., Molfetta, G. D., & Kadri, H. (2026) · Quantum Machine Intelligence · doi:10.1007/s42484-026-00406-4
Polyra Swarms: A Shape-Based Approach to Machine LearningPreprint
Klüttermann, S., & Müller, E. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2506.13217
Rennó, C. A. (2025) · Revista Educação C&T / Secretaria de Estado de Ciência Tecnologia e Inovação de Mato Grosso · doi:10.29327/2686685.1.4-1
Neural networks for dengue forecasting: a systematic reviewPreprint
Lober, L., Rodrigues, F. A., & Roster, K. I. O. (2025) · medRxiv · doi:10.1101/2025.10.14.25338016
Forecasting the USD/COP Exchange Rate Using LSTM Networks: A Comparison with ARIMA Models
Romero, R., Leon, D., Sandoval, J., Hernández, G., & Quimbayo, C. A. Z. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08203-9_7
Evidence ratio classifier: A one-pass model for fast incremental learning
Manette, F. L. O. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131797
Amini, M.-R. (2025) · Cognitive technologies · doi:10.1007/978-3-031-99928-4_4
Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous ArchitecturesPreprint
Giasemis, F. I. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2508.07423
Learning by Steering the Neural Dynamics: A Statistical Mechanics PerspectivePreprint
Scardecchia, M. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2510.11984
Fundamentals of Artificial Intelligence
Das, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0598-1.ch002
Grundlagen der Künstlichen Intelligenz
Plafky, C. S., & Badertscher, H. (2025) · Springer eBooks · doi:10.1007/978-3-658-49009-6_3
Ζουρνατζίδου, Γ. (2025) · PLoS ONE · doi:10.1371/journal.pone.0334537
Prediction of Target LWD Modulus of Unbound Geomaterials by Lab LWD TestPreprint
Liu, J. H.-I., & Liu, C. (2025) · doi:10.31224/5608
Big data and artificial intelligence in pediatric heart failure
Nguyen, T. T., Chang, A. C., & Toscano, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13279-7.00018-3
A Systematic Review of Deep Learning Techniques for Detecting Cyberattacks in Network TrafficPreprint
Encarnación, A. C., Velázquez, J. M. S., Alba, E., Carte, S., & Nogales, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5622640
Classification-Based Analysis of Price Pattern Differences between Cryptocurrencies and Stocks
Zhang, Y. Y., Wu, Z., & Tessone, C. J. (2025) · Blockchain Kaigi (BCK) · doi:10.7566/jpscp.44.011012
Online Forecast Model for Urban Flooding and Induced Water Contamination
Berkhahn, S., Graf, T., Neuweiler, I., Peche, A., Sämann, R., Fuchs, L., Krämer, S., & Wahl, J. (2025) · Cambridge University Press eBooks · doi:10.1017/9781316340332.006
Hamming Weight-Based Side Channel Analysis of HLS Kyber Hardware Using Neural Networks
Kharitonov, A., Welling, T., Gay, M., & Polian, I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-01799-4_4
Du, Y., Wang, X., Guo, N., Yu, Z., Qian, Y., Zhang, K., Hsieh, M.-H., Rebentrost, P., & Tao, D. (2025) · Springer eBooks · doi:10.1007/978-981-95-1284-3_4
Deep Learning and Transformers
Mani, I. (2025) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-94058-3_2
Kölmel, M.-J. (2025) · De Gruyter eBooks · doi:10.1515/9783110776881-008
2 PLASTICITY: CONTAGIOUS CODES AND ALGORITHMIC VISIONS
Kölmel, M.-J. (2025) · De Gruyter eBooks · doi:10.1515/9783110776881-005
Guo, H., Xue, L., & Wang, J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26629-4.00102-7
Visual Analytical Methods for Exploring the Algorithmic Infrastructures of Social Media Platforms
Chand, K. (2025) · Queensland University of Technology · doi:10.5204/thesis.eprints.260749
Application of ML-Based XGBoost Model to Predict Pullout Resistance Factor of Geogrid in Sands
Bherde, V., Attara, S. K., & Balunaini, U. (2025) · Lecture notes in civil engineering · doi:10.1007/978-981-96-9529-4_13
Šekrst, K. (2025) · Springer eBooks · doi:10.1007/978-3-032-05562-0_7
Neural Networks and Machine Learning
Šekrst, K. (2025) · Springer eBooks · doi:10.1007/978-3-032-05562-0_8
Kentsel Trafik Analizinde 3D/2D Hibrit CNN Mimarisi ve Dikkat Mekanizmalarının İncelenmesi
Arı, A., & Acar, S. Y. (2025) · Journal of Polytechnic · doi:10.2339/politeknik.1752555
Çolak, Z. (2025) · Ekonomi Politika ve Finans Arastirmalari Dergisi · doi:10.30784/epfad.1813752
Reservoir Computing Models for Slow Electronics
Tanaka, G. (2025) · Springer eBooks · doi:10.1007/978-981-96-8383-3_2
Li, L., Shi, L., Wang, H., Liu, L., Han, Z., & Jiang, L. (2025) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc67675.2025.00075
Acharjee, S., Bhattacharjee, P., Mondal, S., Goswami, S., Sadhukhan, S., Mukherjee, A., & Das, A. (2025) · International Conference on Engineering Innovations and Technologies (ICoEIT) · doi:10.1109/icoeit63558.2025.11211514
Hardware Technologies for Artificial Intelligence
Kawahara, T. (2025) · CRC Press eBooks · doi:10.1201/9781003599067
Nixon, M. S., & Aguado, A. S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36686-4.00010-9
Distance, classification and deep learning
Nixon, M. S., & Aguado, A. S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36686-4.00002-x
Bilinear Dendritic Network Driven by Recurrent Forward-Forward AlgorithmPreprint
魏, 佳., Xu, X., Luo, P., Yu, Z., & Liu, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5688622
Failure Detection in Continuous Glucose Monitors Using Transformers
Barriga, F. G., Montes, D. M. I., Alanís, A. Y., & Martínez-Soltero, G. (2025) · doi:10.61728/ae20255312
Shadow zones of an artificial neuron
Novikov, A., Smyrnov, V., & Yanovsky, V. (2023) · Bulletin of V N Karazin Kharkiv National University series «Mathematical modeling Information technology Automated control systems» · doi:10.26565/2304-6201-2023-60-03
(2023) · Bulletin of V N Karazin Kharkiv National University series «Mathematical modeling Information technology Automated control systems» · doi:10.26565/2304-6201-2023-60
Mapeamento de risco em São Sebastião e Bertioga utilizando ferramentas de machine learning
Obara, T. S., Louro, V. H. A., & Antonelli, T. (2023) · doi:10.11606/003187402
Villacrés, G. E. F., Solano, M. V. R., Brito, E. E. C., Lalama, Á. S. A., & Palacios, Á. (2025) · doi:10.64584/ppmp4563
Woodhead, J. D., & Landry, M. (2025) · Geology and Mining: Evolution of Best Practice · doi:10.5382/com.16.7
Theoretical Bases of Differential Learning
Schöllhorn, W. I., & Slapšinskaitė, A. (2025) · Springer eBooks · doi:10.1007/978-3-031-98787-8_2
Fractal properties of neural networks
Novikov, A., Smyrnov, V., & Yanovsky, V. (2024) · Bulletin of V N Karazin Kharkiv National University series «Mathematical modeling Information technology Automated control systems» · doi:10.26565/2304-6201-2024-64-07
Federated Learning with Blockchain-Enhanced Model Explainability
Kaur, G., Wasson, V., & Kaur, S. (2025) · Blockchain technologies · doi:10.1007/978-981-95-1394-9_5
Fundamentals of Neural Networks and Convolutional Neural Networks
Long, P., & Song, Y. L. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-43950-6.00004-6
Ensemble recursive feature elimination-based ensemble classification for medical diagnosis
Ramanathan, T. T., Hossen, M. J., Mamun, A. A., & Raja, J. E. (2025) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v40.i2.pp758-771
Learning from Similarity Proportion Loss for Classifying Skeletal Muscle Recovery StagesPreprint
Yamaoka, Y., Chan, W. I., Seno, S., Fukada, S., & Matsuda, H. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2505.04150
Giridharan, J., & Jayachandiran, U. (2025) · Research Square · doi:10.21203/rs.3.rs-8074560/v1
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