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 9,901–10,000 of 13,333
ALGORITHMIC, GAME THEORETIC AND LEARNING THEORETIC ASPECTS OF DISTRIBUTED OPTIMIZATION
Ehrlich, S. (2016) · SMARTech Repository (Georgia Institute of Technology)
Técnico, S. L. D. (2004)
Cost-based shop control using artificial neural networks
Wiegmann, L. (1992) · VTechWorks (Virginia Tech)
Sistema de hardware reconfigurável para navegação visual de veículos autônomos
Dias, M. A. (2017) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.55.2017.tde-13012017-164142
Intelligence in formal machines
Vieira, P. A. A. (2015) · doi:10.14201/gredos.128366
A Whole that may be Greater than the Sum of its Parts: A Balanced Mind from Biased Processes
Hasinski, A. E. (2010) · OhioLink ETD Center (Ohio Library and Information Network)
Perspectives en matrices aléatoires et grands réseaux
Couillet, R., & Wainrib, G. (2016) · Traitement du signal · doi:10.3166/ts.33.351-376
Some models and measures for learning on a budget
Venkatasubramanian, S., & Saha, A. (2012)
Diabetic prediction using fuzzy back propagation and analysis
Mittal, L., & Masilamani, A. (2017) · International Journal of Recent Scientific Research · doi:10.24327/ijrsr.2017.0803.0062
de Almeida, A. P. G. S. (2017) · doi:10.26512/2017.01.d.23186
Burtini, G. (2015) · cIRcle (University of British Columbia) · doi:10.14288/1.0166786
An Adaptive Neural Network for the Cost Estimation of E-Learning Projects in the United Kingdom
Valverde, R. (2017) · International Journal of Decision Support System Technology · doi:10.4018/ijdsst.2017070104
Lewis, S. N. (2016) · doi:10.25560/43539
Aboudib, A., Gripon, V., & Tessiau, B. (2015) · HAL
Sensoriamento automático e participativo em cidades.
da Silva, A. F. (2016) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.3.2016.tde-28062016-074016
Ordóñez, P. F. (2017) · DigitalCommons - Kennesaw State University (Kennesaw State University)
Cabestaing, F., Vaughan, T. M., McFarland, D. J., & Wolpaw, J. R. (2006) · LillOA (Université de Lille (University Of Lille))
Modeling Financial Time Series with Artificial Neural Networks
Wong, C., & Massimiliano Versace, A. (2009) · OpenBU (Boston University)
A Family of Large Margin Linear Classifiers and Its Application in Dynamic Environments
Shen, J., & Dietterich, T. G. (2009) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972795.15
Improved Learning in Constructive Neural Networks for Pattern Classification using Genetic Algorithm
Vijayan, M., Sridhar, S. S., & John, V. M. (2017) · Indian Journal of Science and Technology · doi:10.17485/ijst/2017/v10i18/92674
Sautter, G. (2011) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5281/zenodo.11066340
Lipiński, E. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0013
Fundamental Theories and Techniques
Hontani, H., Hirano, Y., Dong, X. M., Shimizu, A., & Hanaoka, S. (2017) · Springer eBooks · doi:10.1007/978-4-431-55976-4_2
Using LASSO regularization as a feature selection tool.
Torstensson, E. (2017) · Lund University Publications Student Papers (Lund University)
Comparative, Historical, and Typological Linguistics since the Eighteenth Century
Jankowsky, K. R. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0029
Towards a Continuous User Authentication Using Haptic Information
Alsulaiman, F. A. (2013) · uO Research (University of Ottawa) · doi:10.20381/ruor-4279
Redes Neurais Aplicadas ao Reconhecimento de Regiões Promotoras na Família Mycoplasmataceae
Valiati, J. F. (2006) · Americanae (AECID Library)
Akca, E. (2016) · Southeast Europe Journal of Soft Computing · doi:10.21533/scjournal.v5i2.122
PP and PP Multi-Particles Production Investigation Based on CCNN Black-Box Approach
El‐Dahshan, E. A. (2017) · Journal of Applied Mathematics and Physics · doi:10.4236/jamp.2017.56115
Learning Algorithms for driver attitude determination
Hjartarson, H. (2017) · Chalmers Publication Library (Chalmers University of Technology)
OMKT: Projection based bounded on-line multiple kernel tracker
Kumarasinghe, P., & Sundaram, S. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966209
Lima, F. R. (2017) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.18.2017.tde-16032017-141008
Gronau, N., & Grum, M. (2017) · International Symposium on Business Modeling and Software Design (BMSD) · doi:10.5220/0006527000110018
Preliminary Investigations Toward a Theory of Pattern for Technology Design
Aguilar, A. (2012) · UNM’s Digital Repository (University of New Mexico)
Analysis of Part-Of-Speech Tagging of Historical German Texts
Paluch, M., Rotari, G., Steding, D., Weß, M., Moritz, M., & Büchler, M. (2017) · International Conference on Digital Access to Textual Cultural Heritage (DATeCH) · doi:10.1145/3078081.3078111
Measurements Data Analysis and Defect Classification Using NN
Radelja, H., Kršulja, M., Šikulec, L., & Car, Z. (2011)
THEORETICAL NOTE A Neuroconstructivist Model of Past Tense Development and Processing
Westermann, G., & Ruh, N. (2012)
Neural Networks for Analysing Music and Environmental Audio
Sigtia, S. (2017) · Queen Mary Research Online (Queen Mary University of London)
Arnborg, S., & Csc, K. (2010)
A Theory of Cortical Neural Processing.
Young, D. T. (1997) · doi:10.31390/gradschool_disstheses.6534
RECONFIGURAÇÃO DE CONTROLE EM UM DRONE QUADRIMOTOR AUXILIADA POR UM CLASSIFICADOR DE FALHAS
Silva, R. B., de Souza, J. S., & Maia, R. D. (2017) · Revista de Informática Aplicada · doi:10.13037/ria.vol13n1.171
Castillo, L. A. F. (2017) · RICEA Revista Iberoamericana de Contaduría Economía y Administración · doi:10.23913/ricea.v6i12.95
Wave Overtopping Prediction Using Global-Local Artificial Neural Networks
Wedge, D. C. (2006) · OpenGrey (Institut de l'Information Scientifique et Technique)
Predicting reliability in multidisciplinary engineering systems under uncertainty
Hwang, S. (2016) · SMARTech Repository (Georgia Institute of Technology)
Mazibaş, M., & Küçüközmen, C. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3008578
A Statistical Approach To View Synthesis
Berkowitz, P. (2009)
Application of Neural Network and Genetic Algorithm in Architecture
Tsutsumi, K., & Aoki, Y. (1997) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jfuzzy.9.2_190
Spiking neurons with short-term synaptic plasticity form superior generative networksPreprint
Leng, L., Martel, R., Breitwieser, O., Bytschok, I., Senn, W., Schemmel, J., Meier, K., & Petrovici, M. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.08166
Emerging Stock Exchanges in a Global Map: A Network ApproachPreprint
Santamaría, T. C., Estévez, P. G., & Ferrari, S. R. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.3051115
Computer Aided Surgery and Artificial Intelligence/Machine Learning
Mori, K. (2017) · Journal of Japan Society of Computer Aided Surgery · doi:10.5759/jscas.19.147
Lai, W. K., Chan, K. S., Chan, C. S., Goh, K. M., & Raymond, W. J. K. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70093-9_54
Neural Networks for Document Image and Text Processing
Pastor-Pellicer, J. (2017) · doi:10.4995/thesis/10251/90443
Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks
Flekova, L., & Schott, M. (2017) · Journal of Physics Conference Series · doi:10.1088/1742-6596/898/3/032054
Gori, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-08-100659-7.00003-8
Per-Pixel Feedback for improving Semantic SegmentationPreprint
Ganeshan, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.02861
Prehospital Diagnosis and Oxygen Treatment in ST Elevation Myocardial Infarction
Khoshnood, A. (2017) · Lund University Publications (Lund University) · doi:10.13140/rg.2.2.33773.82409
Modèle profond pour le contrôle vocal adaptatif d'un habitat intelligent
Brenon, A. (2017) · theses.fr (ABES) · doi:10.70675/50ef2cb9z4d4az495bzbd87za7e17e688a95
Πολυτροπική αναγνώριση και κατάτμηση δράσεων λεπτομέρειας σε βίντεο
Gkanatsios, N. (2017) · DSpace - NTUA (National Technical University of Athens) · doi:10.26240/heal.ntua.14727
Castillo, L. A. F., & Osorio, M. C. R. (2017) · RICEA Revista Iberoamericana de Contaduría Economía y Administración · doi:10.23913/ricea.v6i12.102
Hall, J. W. (2017) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
Falvo, M. (2017) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2017.tde-19122017-105229
Benchmarking Decoupled Neural Interfaces with Synthetic GradientsPreprint
Bisong, E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.08314
Das ICH, die Optimierung und die Gesellschaft
Küppers, E. W. U. (2017) · Springer eBooks · doi:10.1007/978-3-658-17920-5_4
Sparsity Meets Dynamics: Robust Solutions to Neuronal Identification and Inverse Problems
Babadi, B. (2017) · Springer eBooks · doi:10.1007/978-3-319-71976-4_5
Automated Morphology-based Analysis of Neural Cells
Tuxworth, G. (2017) · Griffith Research Online · doi:10.25904/1912/1283
Intelligent Predictive Models for Water resources Engineering
Joorabchi, A. (2009) · Griffith Research Online · doi:10.25904/1912/1646
Measure valued differentiation for stochastic neural networks
Flynn, T. R. (2017) · Winter Simulation Conference · doi:10.1109/wsc.2017.8248235
RBF-FIRMLP Architecture for Digit Recognition
Codrescu, C. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.0-125
Bioinspired Associative Memories
Vazquez, R. A., & Sossa, H. (2009) · IGI Global eBooks · doi:10.4018/978-1-59904-849-9.ch039
Informationstheorie, Fuzzy Sets und Informatik aus wissenschaftshistorischer Perspektive
Seising, R. (2018) · HMD Praxis der Wirtschaftsinformatik · doi:10.1365/s40702-018-0393-y
A perceptron-based optical quadratic neural network using a photorefractive crystal
Huynh, A. V. (1991) · ThinkTech (Texas Tech University)
Neural Networks for Semantic Segmentation in the Food Packaging Industry
Carlsson, M. J. L. (2018) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
Multistep Second Order Training For The Multilayer Perceptron
Robinson, M. (2013) · UTA ResearchCommons (University of Texas Arlington)
Learning Future Classifiers without Additional Data
Kumagai, A., & Iwata, T. (2018) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.d-h92
Real-Time Noise Classification of Medical Image via Online Machine Learning Algorithm
Chang, Y. (2017) · Advances In Image and Video Processing · doi:10.14738/aivp.56.4084
Thinking Out of the Box. Non-typical Research Methods in Business
Aytouni, K. A., & Naddeh, K. M. (2018) · Progress in IS · doi:10.1007/978-3-319-74173-4_8
Scaling to Multiple Graphics Processing Units (GPUs) in TensorFlow
Park, S. J. (2018)
Mobility Modeling, Prediction and Resource Allocation in Wireless Networks
Prasad, P. R. (2010)
Examining neuronal connectivity and its role in learning and memory
Gala, R. (2017) · Digital Repository Service (Northeastern University) · doi:10.17760/d20253061
Scalable online kernel learning
Lu, J. J. (2017) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University)
Perspectives in Education for Sound and Music Computing
Avanzini, F., Baratè, A., Haus, G. M., Ludovico, L. A., Ntalampiras, S., & Presti, G. (2018)
The window least mean square error algorithm
Degtyarena, A. S. (2003) · CSUSB ScholarWorks (California State University, San Bernardino)
Complementing South African inflation surveys: A suitable forecasting tool
van Heerden, C., Heymans, A., & Seetharam, Y. (2018) · Journal of Economic and Financial Sciences · doi:10.4102/jef.v11i1.191
Novel feature selection algorithms for improving neural network performance
Zhao, Z. (2017) · UTAS Research Repository · doi:10.25959/23240009
Bouaziz, M. (2017) · theses.fr (ABES) · doi:10.70675/f26ad36bzc509z4bf3z9ebfz8e4ccfc081e1
Analysis of Multilayer Perceptron with Rectifier Linear Unit Activation FunctionPreprint
Mukhametkhan, M., Krestinskaya, O., & James, A. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.07734
Pereira, J. G. D. C. (2018) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.95.2018.tde-03052018-095932
Perpendicular Bisector Constraint on Artificial Neural Network
Chen, Y., Song, B., & -, Y. R. (2017) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2017.00057
Review of mapping neural networks in classification systems
Hing, R. (1990) · Unisa Institutional Repository (University of South Africa)
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