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
Cited by
Sorted by citations
Items 5,301–5,400 of 13,333
A Paragraph Boundary Detection System
Genzel, D. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-30586-6_92 · Cited by 7
de Almeida, M. B., Braga, A. P., Braga, J. P., Belchior, J. C., & Yared, G. F. G. (2000) · Physical Chemistry Chemical Physics · doi:10.1039/a906489c · Cited by 7
Learning with stochastic inputs and adversarial outputs
Lazaric, A., & Munos, R. (2012) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2011.12.027 · Cited by 7
On the History of Fuzzy Clustering: An Interview with Jim Bezdek and Enrique Ruspini [History]
Seising, R. (2015) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2015.2395653 · Cited by 7
Smale, S. T., & Yao, Y. (2008) · Cited by 7
EFFICIENT LANE DETECTION BASED ON ARTIFICIAL NEURAL NETWORKS
Arce, F., Zamora, E., Hernández, G., & Sossa, H. (2017) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-4-w3-13-2017 · Cited by 7
Prediction of Car Accidents Using a Maximum Sensitivity Neural Network
Contreras, E., Torres‐Treviño, L., & Torres, F. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-73323-4_9 · Cited by 7
Binary classification in unstructured space with hypergraph case-based reasoning
Quemy, A. (2019) · Information Systems · doi:10.1016/j.is.2019.03.005 · Cited by 7
On a quadratic equation of state and a universe mildly bouncing above the Planck temperature
Berteaud, J., Pasquet, J., Schücker, T., & Tilquin, A. (2019) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2019/10/069 · Cited by 7
Future of Artificial Intelligence and Nuclear Cardiology
Kikuchi, A., & Kawakami, T. (2018) · Annals of Nuclear Cardiology · doi:10.17996/anc.18-00055 · Cited by 7
Nazari, S., & Karim Faez, A. (2018) · Information Sciences · doi:10.1016/j.ins.2018.09.059 · Cited by 7
Hashimoto, M., & NAKAMOTO, K. (2019) · International Journal of Automation Technology · doi:10.20965/ijat.2019.p0067 · Cited by 7
Evaluating User Actions as a Proxy for Email Significance
Alrashed, T., Lee, C.-J., Bailey, P., Lin, C. H., Shokouhi, M., & Dumais, S. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313624 · Cited by 7
Support vector machines and generalisation in HEP
Bevan, A. J., Goni, R. G., Hays, J., & Stevenson, T. (2017) · Journal of Physics Conference Series · doi:10.1088/1742-6596/898/7/072021 · Cited by 7
A study of properties of palladium metal as a component of fuel cells
Nigussa, K. N. (2019) · Materials Research Express · doi:10.1088/2053-1591/ab3fd4 · Cited by 7
swFLOW: A Dataflow Deep Learning Framework on Sunway TaihuLight Supercomputer
Lin, H., Lin, Z., Diaz, J. M. M., Li, M., An, H., & Gao, G. R. (2019) · 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/hpcc/smartcity/dss.2019.00345 · Cited by 7
Online Bayesian max-margin subspace learning for multi-view classification and regression
He, J., Du, C., Zhuang, F., Yin, X., He, Q., & Long, G. (2019) · Machine Learning · doi:10.1007/s10994-019-05853-8 · Cited by 7
Automated Isotope Identification and Quantification Using Artificial Neural Networks
Kamuda, M., & Huff, K. (2019) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · doi:10.13012/b2idb-4860767_v1 · Cited by 7
Intelligent Industrial Process Control Based on Fuzzy Logic and Machine Learning
Zermane, H., & Kasmi, R. (2019) · International Journal of Fuzzy System Applications · doi:10.4018/ijfsa.2020010104 · Cited by 7
Alijagić, J., & Šajn, R. (2020) · Minerals · doi:10.3390/min10020120 · Cited by 7
Even Simpler Real-Time Model of Neuron
Leukhin, A., Talanov, M. O., Suleimanova, A., Toschev, A. S., & Lavrov, I. A. (2020) · BioNanoScience · doi:10.1007/s12668-020-00721-5 · Cited by 7
Quand l’intelligence artificielle théorisera les organisations
Baumard, P. (2019) · Revue française de gestion · doi:10.3166/rfg.2020.00409 · Cited by 7
Non-linear Modeling: Neural Networks
Marini, F. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-409547-2.14893-0 · Cited by 7
Reghin, D., & Lopes, F. S. (2019) · Latin American Computer Conference (CLEI) · doi:10.1109/clei47609.2019.235095 · Cited by 7
Ikaros: A framework for controlling robots with system-level brain models
Balkenius, C., Johansson, B., & Tjøstheim, T. A. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420925002 · Cited by 7
Baryshnikov, E. S., Kanin, E. A., Osiptsov, A. A., Vainshtein, A. L., Burnaev, E. V., Paderin, G. V., Prutsakov, A. S., & Ternovenko, S. O. (2020) · SPE Russian Petroleum Technology Conference · doi:10.2118/201942-ms · Cited by 7
Cheng, C., & Lin, Y.-T. (2020) · Energies · doi:10.3390/en13195164 · Cited by 7
Qin, T. (2020) · Springer eBooks · doi:10.1007/978-981-15-8884-6_3 · Cited by 7
Budgeted Passive-Aggressive Learning for Online Multiclass Classification
Wu, C.-H., Lu, H. H., & Hang, H. (2020) · IEEE Access · doi:10.1109/access.2020.3040816 · Cited by 7
Deep Learning in Medical Imaging Survey.
Cheikh, G. A., Mbacké, A. B., & Ndiaye, S. (2019) · Cited by 7
Ontology, neural networks, and the social sciences
Strohmaier, D. (2020) · Synthese · doi:10.1007/s11229-020-03002-6 · Cited by 7
The Simulation of Cognitive Processes: An Annotated Bibliography
Simmons, P. L., & Simmons, R. F. (1961) · IRE Transactions on Electronic Computers · doi:10.1109/tec.1961.5219236 · Cited by 7
Matchev, K. T., & Shyamsundar, P. (2021) · Journal of High Energy Physics · doi:10.1007/jhep03(2021)291 · Cited by 7
APPLICATION OF NEURAL NETWORK TECHNOLOGIES FOR MANAGEMENT DEVELOPMENT OF SYSTEMS
Lisovsky, A. L. (2021) · Strategic decisions and risk management · doi:10.17747/2618-947x-923 · Cited by 7
Artificial intelligence in process systems engineering
Shi, T., Yang, A., Jin, Y., Ren, J., Shen, W., Dong, L., & Man, Y. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821092-5.00010-3 · Cited by 7
ML-CLOCK: Efficient Page Cache Algorithm Based on Perceptron-Based Neural Network
Cho, M., & Kang, D. H. (2021) · Electronics · doi:10.3390/electronics10202503 · Cited by 7
Wang, S., Ma, Q., Qian, L., Zhao, M., Wang, Z., & Shi, L. (2022) · Neuroscience · doi:10.1016/j.neuroscience.2021.12.042 · Cited by 7
Supervised Learning by Support Vector Machines
Steidl, G. (2010) · Springer eBooks · doi:10.1007/978-0-387-92920-0_22 · Cited by 7
Savaş, T., & Savaş, S. (2022) · Journal of Polytechnic · doi:10.2339/politeknik.1035286 · Cited by 7
Artificial Intelligence: An Introduction
Kejriwal, M. (2022) · Future of business and finance · doi:10.1007/978-3-031-19039-1_1 · Cited by 7
Evolution of neural activity in circuits bridging sensory and abstract knowledge
Mastrogiuseppe, F., Hiratani, N., & Latham, P. E. (2023) · eLife · doi:10.7554/elife.79908 · Cited by 7
A survey of classification learning algorithm
Zeng, F., & Qui, Z.-D. (2005) · International Conference on Signal Processing · doi:10.1109/icosp.2004.1441612 · Cited by 7
An ensemble average classifier for pattern recognition machines
Zaki, F. W., el-Fattah, A. I. A., Enab, Y. M., & El-Konyaly, S. H. (1988) · Pattern Recognition · doi:10.1016/0031-3203(88)90046-5 · Cited by 7
A Cognitive Approach to the Mechanism of Intelligence
Zhong, Y. X. (2008) · International Journal of Cognitive Informatics and Natural Intelligence · doi:10.4018/jcini.2008010101 · Cited by 7
Forecasting House Rental Levels: Analytical Rent Model versus Neural Network
Li, H., & Li, V. (1996) · Journal of Urban Planning and Development · doi:10.1061/(asce)0733-9488(1996)122:4(118 · Cited by 7
Applications of GMDH-type modeling in manufacturing
Chao, P.-Y., FERREIRA, P. M., & Liu, C. R. (1988) · Journal of Manufacturing Systems · doi:10.1016/0278-6125(88)90008-8 · Cited by 7
Cross-Morphemic Predictability and the Lexical Access of Compounds in Mandarin Chinese
Myers, J., & Gong, S.-P. (2002) · Folia Linguistica · doi:10.1515/flin.2002.36.1-2.65 · Cited by 7
Behavioral simulation of biological neuron systems using VHDL and VHDL-AMS
Bailey, J. A., Wilson, P. R., Brown, A. D., & Chad, J. E. (2007) · IEEE International Behavioral Modeling and Simulation Workshop (BMAS) · doi:10.1109/bmas.2007.4437543 · Cited by 7
Rutkowska, D. (2003) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz.2002.1006671 · Cited by 7
Behavioral simulation of biological neuron systems in SystemC
Modi, S., Wilson, P., Brown, A. D., & Chad, J. E. (2005) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/bmas.2004.1393978 · Cited by 7
Tufail, M. (2006) · UKnowledge (University of Kentucky) · Cited by 7
REPRESENTING STYLE BY FEATURE SPACE ARCHETYPES
Hanna, S. (2007) · Springer eBooks · doi:10.1007/978-1-4020-5131-9_1 · Cited by 7
Prediction Games in Infinitely Rich Worlds
Madani, O. (2007) · Cited by 7
Neural Mechanisms of Visuomotor Coordination: The Evolution of Rana computatrix
Arbib, M. A. (1991) · Research notes in neural computing · doi:10.1007/978-3-642-84545-1_1 · Cited by 7
Artificial neural networks: definitions, methods, applications
Zahner, D. A., & Micheli-Tzanakou, E. (2000) · CRC Press eBooks · doi:10.1201/9781315219660-2 · Cited by 7
Adaptive Split Spectrum Processing Using a Neural Network
Gustafsson, M. G., & Stepinski, T. (1993) · Research in Nondestructive Evaluation · doi:10.1080/09349849309409542 · Cited by 7
Computational intelligence standards: motivation, current activities and progress
Padgett, M. L., Karplus, W. J., Deiss, S., & Shelton, R. O. (1994) · Computer Standards & Interfaces · doi:10.1016/0920-5489(94)90011-6 · Cited by 7
A hybrid expert system combining AI techniques with a neural-net
Ulug, M. E. (1989) · Industrial and Engineering Applications of Artificial Intelligence and Expert Systems · doi:10.1145/66617.66654 · Cited by 7
Optimizing the mathematical model for prediction of energy production in wind power plants
Mišák, S., Prokop, L., & Dvorský, J. (2011) · DSpace VŠB-TUO (VŠB-TUO) · Cited by 7
Perceptron learning with random coordinate descent
Li, L. (2005) · CaltechAUTHORS (California Institute of Technology) · doi:10.7907/z9pr7szq · Cited by 7
Distance-Based Phylogenetic Methods
Xia, X. (2018) · Springer eBooks · doi:10.1007/978-3-319-90684-3_15 · Cited by 7
Lampinen, J., Laaksonen, J. T., & Erkki Oja, A. (1998) · Neural network systems techniques and applications · doi:10.1016/s1874-5946(98)80025-4 · Cited by 7
A tree-to-tree model for statistical machine translation
Collins, M., & Cowan, B. (2008) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 7
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Trianni, V., & Dorigo, M. (2006) · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · Cited by 7
Anderson, J. A. (2005) · Wiley eBooks · doi:10.1002/0470018860.s00353 · Cited by 7
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Ramdas, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.5295 · Cited by 7
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Xu, J., Zhang, X., & Li, Y. (2002) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2001.939581 · Cited by 7
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Brouwer, R. K. (1997) · Neural Networks · doi:10.1016/s0893-6080(96)00087-1 · Cited by 7
Patuelli, R., Reggiani, A., Nijkamp, P. J., & Schanne, N. (2009) · RePEc: Research Papers in Economics · Cited by 7
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Muthukumar, S. (2008) · SMARTech Repository (Georgia Institute of Technology) · Cited by 7
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Zhang, N. (2007) · OakTrust (Texas A&M University Libraries) · Cited by 7
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Cadène, R., Thome, N., & Cord, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.05567 · Cited by 7
Kyselo, M. (2013) · osnaDocs (Osnabrück University) · Cited by 7
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Hajihassani, M. (2013) · Cited by 7
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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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