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
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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
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Oliveira, H. F., de Oliveira, R., Faria, E. R., da Gama, J. M. P., Khan, L. R., & Cerri, R. (2026) · Research Square · doi:10.21203/rs.3.rs-9763598/v1 · Cited by 1
Gevorgyan, A., Hayrapetyan, A., Макаренко, В., Tumasyan, A., Adam, W., Benato, L., Bergauer, T., Dragicevic, M., Gundacker, S., Guven, A. K., et al. (2026) · arXiv (Cornell University) · doi:10.3204/pubdb-2026-02425 · Cited by 1
Variational Bounds for Perceptron Learning from Structured DataPreprint
Camilli, F., Contucci, P., Gerace, F., & Mingione, E. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2608.04882 · Cited by 1
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Harks, T. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2609.18342 · Cited by 1
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Xie, X. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2609.29756 · Cited by 1
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Westerhoff, J., Olbrich, S., Oraby, H., Larkum, M. E., & Gers, F. A. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2609.31360 · Cited by 1
Sistema Inteligente Para Previsão de Carga Multinodal em Sistemas Elétricos de Potência
Altran, A. B. (2010) · Americanae (AECID Library) · Cited by 1
A novel neurophysiologically-inspired self-organizing cerebellar memory framework
Sintiani, D. T. (2008) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/41119 · Cited by 1
Machine Learning in Scientometrics
Martín, A. I. (2015) · doi:10.20868/upm.thesis.36488 · Cited by 1
Emergence of Bayesian Structure from Recurrent Networks.
Hélie, S. (2004) · International Conference on Cognitive Modelling · Cited by 1
Computation in Complex Environments;
Pieters, K. P. (2010) · Adaptation, learning, and optimization · doi:10.1007/978-3-642-12775-5_13 · Cited by 1
Real-Time Automatic Linear Feature Detection in Images
Zeng, G. (2008) · TigerPrints (Clemson University) · Cited by 1
Poole, D., & Mackworth, A. K. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9780511794797.013
Kogeyama, R. (2014) · Americanae (AECID Library)
Polkowski, L. (2011) · Intelligent systems reference library · doi:10.1007/978-3-642-22279-5_7
Information processing and learning in spiking neural networks
Ponulak, F., & Kasiński, A. (2010) · Prace Naukowe Politechniki Warszawskiej. Elektronika
Texture Correlation Feature for Support Vector Machine-Based Face Detection
Nguyen, L., & Stacey, D. (2010) · ASME Press eBooks · doi:10.1115/1.859599.paper67
Apport de l'intelligence artificielle à la psychologie
Defays, D., French, R. M., & Sougné, J. P. (1999) · Open Repository and Bibliography (University of Liège)
Mohan, S. K. (2009) · TigerPrints (Clemson University)
Robust Distributed Training of Linear Classifiers Based on Divergence Minimization Principle
Komiyama, J., Oiwa, H., & Nakagawa, H. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44851-9_1
Adaptive, predictive controller for optimal process control
Brown, S. K., Baum, C., Bowling, P. S., Buescher, K. L., Hanagandi, V., Hinde, R. F., Jones, R. D., & Parkinson, W. J. (1995) · University of North Texas Digital Library (University of North Texas)
Häusler, C. J. (2014) · Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver · doi:10.17169/refubium-11600
Support Vector Methods for Higher-Level Event Extraction in Point Data
Devine, J. (2009) · DigitalCommons (California Polytechnic State University)
Eddy, P. (2007) · Open ULeth Scholarship (OPUS) (University of Lethbridge)
Künstliche neuronale Netze im Data Mining
Hilbert, A. (2001) · Gabler Verlag eBooks · doi:10.1007/978-3-322-84461-3_19
A Near Linear Algorithm for Testing Linear Separability in Two Dimensions
Contassot‐Vivier, S., & Elizondo, D. A. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-32909-8_12
Assoziativspeicherung in Neuronalen Netzen
Faißt, J. (1993) · Schriften zur quantitativen Betriebswirtschaftslehre · doi:10.1007/978-3-642-95902-8_5
Optymalizacja statystycznych systemów uczących się
Jankowski, S. (2012) · Prace Naukowe Politechniki Warszawskiej. Elektronika
A Scalable Neural Architecture Combining Unsupervised and Suggestive Learning
Lambert, R. B., Cockshott, W. P., & Fryer, R. J. (1993) · Artificial Neural Nets and Genetic Algorithms · doi:10.1007/978-3-7091-7533-0_26
Neuronowy model doświadczenia Pawłowa. Badanie zespołu odruchów warunkowych
Sokalski, K. (2005) · Bio-Algorithms and Med-Systems
Ignatova, Z., Zimmermann, K.-H., & Martínez-Pérez, I. (2008) · Springer eBooks · doi:10.1007/978-0-387-73637-2_6
Exploiting structure defined by data in machine learning : some new analyses
Lever, G. (2011) · UCL Discovery (University College London)
Tourassi, G. D., Frederick, E. D., & Coleman, R. E. (2001) · International series in intelligent technologies · doi:10.1007/978-94-010-0678-1_11
From Cells to Memories: A Categorical Approach
Gómez‐Ramírez, J. (2013) · Springer eBooks · doi:10.1007/978-94-007-7738-5_9
Applying Fuzzy Logic to Neural Modeling.
Hélie, S., Chartier, S., & Proulx, R. (2004)
MECHANICAL PROPERTIES FORECAST IN COMPOSITES USING NEURAL NETWORKS
Sanchez-Caballero, S., Amorós, J. E. C., Parres, F. J., Selles, M. A., & PLA-FERRANDO, R. (2011) · ANNALS OF THE ORADEA UNIVERSITY Fascicle of Management and Technological Engineering · doi:10.15660/auofmte.2011-1.2161
Krawczuk, J. (2012) · Zeszyty Naukowe / Uniwersytet Ekonomiczny w Poznaniu
A Computing Model Combining Artificial Neural Networks and Petri Nets
Schuster, A. (2007) · Springer eBooks · doi:10.1007/978-1-84628-943-9_4
Real-time Ranking of Electrical Feeders using Expert Advice ⋆
Becker, H., & Arias, M. (2007)
Santana, L. E. A. D. S. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Axelson-Fisk, M. (2015) · Computational biology · doi:10.1007/978-1-4471-6693-1_2
Adaptive rule-based optimisation of combustion in multiple burner installations
Fogarty, T. C. (1990) · Lecture notes in computer science · doi:10.1007/3-540-53104-1_46
Erratum: A Bi-Directional Multilayer Perceptron
Jedra, M., Ouardighi, A. E., Essaid, A., & Limouri, M. (2000) · Neural Processing Letters · doi:10.1023/a:1009671823977
Agents for Market-Based Computational-Resource Allocation
Reggers, B., Wiesman, F., Roos, N., Verbrugge, R., Taatgen, N., & Schomalier, L. (2004)
Memory Convergence Analysis for Different Iterative Procedures in RNN
Pandey, S. C. (2010)
Redes neurais artificiais no apoio ao diagnóstico diferencial de lesões intersticiais pulmonares.
Ambrósio, P. E. (2002) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.59.2002.tde-26102002-155559
The Inferior Olivary System and the Climbing Fibers
Barlow, J. S. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511529771.006
Protein structure and function prediction using kernel methods
Karypis, G., & Rangwala, H. (2008) · University of Minnesota Digital Conservancy (University of Minnesota)
Neural-like thinning processing
Chudý, L., & Chudý, V. (1997) · Lecture notes in computer science · doi:10.1007/3-540-63460-6_161
MACHINE LEARNING: A Critique of Research Efforts and Suggested Research Strategy
Davis, W. S., & Murrell, D. B. (1988)
Self-organizing Flexible Neural Network
Teshnehlab, M., & Watanabe, K. (1999) · Springer eBooks · doi:10.1007/978-94-015-9187-4_8
Video object encoder using region-of-interest based neural network classifiers
Jan, T. (2004) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2003.1341110
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
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