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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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
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Items 2,901–3,000 of 13,333
Digital twin as risk-free experimentation aid for techno-socio-economic systems
Barat, S., Kulkarni, V., Clark, T., & Barn, B. S. (2022) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS) · doi:10.1145/3550355.3552409 · Cited by 23
Ibrahim, B. A., Elamer, A. A., & Abdou, H. A. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-05024-4 · Cited by 23
Predicting Patent Quality Based on Machine Learning Approach
Erdoğan, Z., Altuntaş, S., & Dereli, T. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2022.3207376 · Cited by 23
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Skrobek, D., Krzywański, J., Sosnowski, M., Uddin, G. M., Ashraf, W. M., Grabowska, K., Żyłka, A., Kulakowska, A., & Nowak, W. (2023) · Energies · doi:10.3390/en16083441 · Cited by 23
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Jeong, M. S., Jang, J. H., & Lee, E. S. (2023) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2023.3297740 · Cited by 23
Deep Learning in Maritime Autonomous Surface Ships: Current Development and Challenges
Ye, J., Li, C., Wen, W., Zhou, R., & Reppa, V. (2023) · Journal of Marine Science and Application · doi:10.1007/s11804-023-00367-1 · Cited by 23
A data-driven, machine learning scheme used to predict the structural response of masonry arches
Motsa, S. M., Stavroulakis, G. Ε., & Drosopoulos, G. Α. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.116912 · Cited by 23
Geraghty, R. M., Thakur, A., Howles, S., Finch, W., Fowler, S., Rogers, A. G., Sriprasad, S., Smith, D., Dickinson, A., Gall, Z., et al. (2024) · European Urology Focus · doi:10.1016/j.euf.2024.01.011 · Cited by 23
Tian, J., Tian, Z., Zhang, M., Meng, Q., Zhang, A., Liu, C., & Jia, L. (2024) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2024.105329 · Cited by 23
Woo, S. E., Tay, L., & Oswald, F. L. (2024) · Personnel Psychology · doi:10.1111/peps.12643 · Cited by 23
Neural Machine Translation for Low-Resource Languages from a Chinese-centric Perspective: A Survey
Zhang, J., Su, K., Li, H., Mao, J., Tian, Y., Wen, F., Guo, C., & Matsumoto, T. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3665244 · Cited by 23
Experimental Comparisons of Multi-class Classifiers
Li, L., Wu, Y., & Ye, M. (2015) · Informatica · Cited by 23
Madjid Tavana, D., Fazlollahtabar, H., & Hassanzadeh, R. (2014) · International Journal of Production Research · doi:10.1080/00207543.2014.887232 · Cited by 23
Real-Time Detection and Filtering of Chaff Clutter from Single-Polarization Doppler Radar Data
Kim, Y. H., Kim, S., Han, H.-Y., 허복행, & You, C. (2013) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-12-00158.1 · Cited by 23
Utilizing artificial neural network approach for solving two-dimensional integral equations
Asady, B., Hakimzadegan, F., & Nazarlue, R. (2014) · Mathematical sciences · doi:10.1007/s40096-014-0117-6 · Cited by 23
A Better Approximation for Balls
Cheang, G. H. L., & Barron, A. R. (2000) · Journal of Approximation Theory · doi:10.1006/jath.1999.3441 · Cited by 23
Optimization of fermentation processes using evolutionary algorithms - A review
Adeyemo, J. A., & Enitan, A. M. (2011) · Scientific Research and Essays · doi:10.5897/sre11.106 · Cited by 23
Self-organization and Emergence of Dynamical Structures in Neuromorphic Atomic Switch Networks
Stieg, A. Z., Avizienis, A. V., Sillin, H. O., Aguilera, R., Shieh, H.-H., Martin‐Olmos, C., Sandouk, E. J., Aono, M., & Gimzewski, J. K. (2013) · Springer eBooks · doi:10.1007/978-3-319-02630-5_10 · Cited by 23
Gouveia, A., & Correia, M. P. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59153-7_38 · Cited by 23
Inage, S. (2017) · Solar Energy · doi:10.1016/j.solener.2017.05.019 · Cited by 23
Learning from Data Stream Based on Random Projection and Hoeffding Tree Classifier
Pham, X. C., Dang, T., Sang, D. V., Hoang, S., Nguyen, T. T., & Liew, A. W. (2017) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2017.8227456 · Cited by 23
García-Gómez, J., Gil‐Pita, R., Rosa-Zurera, M., Romero-Camacho, A., Jiménez-Garrido, J. A., & García-Benavides, V. (2018) · Sensors · doi:10.3390/s18030802 · Cited by 23
Margari, V., Κανελλοπούλου, Α., & Zaraphonitis, G. (2018) · Ocean Engineering · doi:10.1016/j.oceaneng.2018.07.035 · Cited by 23
Wang, Y.-C. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0997-7 · Cited by 23
Improved Expressivity Through Dendritic Neural Networks.
Wu, X., Liu, X., Li, W., & Wu, Q. (2018) · neural information processing systems · Cited by 23
Mid-quantile regression for discrete responses
Geraci, M., & Farcomeni, A. (2022) · Statistical Methods in Medical Research · doi:10.1177/09622802211060525 · Cited by 23
Investigation of machine learning techniques on proteomics: A comprehensive survey
Sonsare, P. M., & Gunavathi, C. (2019) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2019.09.004 · Cited by 23
Applications of Deep Learning to Audio Generation
Zhao, Y., Xia, X., & Togneri, R. B. (2019) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2019.2945210 · Cited by 23
Hassanzadeh, A., Murphy, S. P., Pethybridge, S. J., & van Aardt, J. (2020) · Remote Sensing · doi:10.3390/rs12223809 · Cited by 23
New Zealand honey botanical origin classification with hyperspectral imaging
Zhang, G., & Abdulla, W. H. (2022) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2022.104511 · Cited by 23
Henseler, J. (1995) · Lecture notes in computer science · doi:10.1007/bfb0027022 · Cited by 23
Deep Spike Learning With Local Classifiers
Ma, C., Yan, R., Yu, Z., & Yu, Q. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3188015 · Cited by 23
Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron
Ng, C. S. W., & Ghahfarokhi, A. J. (2022) · Applied Computing and Geosciences · doi:10.1016/j.acags.2022.100103 · Cited by 23
Enslin, S., & Kaul, V. (2024) · Gastrointestinal Endoscopy Clinics of North America · doi:10.1016/j.giec.2024.09.003 · Cited by 23
Chalup, S. K., & Mitschele, A. (2008) · Springer eBooks · doi:10.1007/978-3-540-49487-4_27 · Cited by 23
CATEGORIZATION IN COGNITIVE COMPUTER SCIENCE
Sowa, J. F. (2005) · Elsevier eBooks · doi:10.1016/b978-008044612-7/50061-5 · Cited by 23
A Novel Kernel for RBF Based Neural Networks
Aftab, W., Moinuddin, M., & Shaikh, M. S. (2014) · Abstract and Applied Analysis · doi:10.1155/2014/176253 · Cited by 23
From Online to Batch Learning with Cutoff-Averaging
Dekel, O. (2008) · Cited by 23
Large Scale Kernel Methods for Online AUC Maximization
Ding, Y., Liu, C., Zhao, P., & Hoi, S. C. H. (2017) · Proceedings · doi:10.1109/icdm.2017.18 · Cited by 23
Interest of neural networks for the optimization of the crossflow filtration process
Dornier, M., Decloux, M., Trystram, G., & Lebert, A. (1995) · LWT · doi:10.1016/s0023-6438(95)94364-1 · Cited by 23
Artificial Neural Networks and Job-specific Modules to Assess Occupational Exposure
Black, J., Benke, G., Smith‐Miles, K., & Fritschi, L. (2004) · The Annals of Occupational Hygiene · doi:10.1093/annhyg/meh064 · Cited by 23
Universal learning network and its application to robust control
Hirasawa, K., Murata, J., Hu, J., & Jin, C. (2000) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.846231 · Cited by 23
Evora, N., Tapsoba, D., & Sève, D. D. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.916632 · Cited by 23
Biological Clues for Up-to-Date Artificial Neurons
Peláez, J. R., & Piqueira, J. R. C. (2007) · Springer eBooks · doi:10.1007/0-387-37452-3_6 · Cited by 23
Symbolic AI versus Connectionism in Music Research.
Toiviainen, P. (2000) · doi:10.4324/9780203059746-9 · Cited by 23
Kocabaș, Ș. (1991) · The Knowledge Engineering Review · doi:10.1017/s0269888900005804 · Cited by 23
Ferneda, E. (2003) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.27.2003.tde-15032004-130230 · Cited by 23
Conditional control transfer mechanisms in the neocortex : 1. The basic model
Scott, P. D. (1979) · Journal of Theoretical Biology · doi:10.1016/0022-5193(79)90045-6 · Cited by 23
VerIDeep: Verifying Integrity of Deep Neural Networks through Sensitive-Sample FingerprintingPreprint
He, Z., Zhang, T., & Lee, R. B.-L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.03277 · Cited by 23
Deep Learning in SciencePreprint
Bianchini, S., Müller, M., & Pelletier, P. (2020) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2009.01575 · Cited by 23
Surrogate-Assisted Evolutionary Algorithms
Loshchilov, I. (2013) · theses.fr (ABES) · doi:10.70675/fac18ba3zbc98z4d27za6b5zc98229c5b253 · Cited by 23
A coherent perceptron for all-optical learning
Tezak, N., & Mabuchi, H. (2015) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-015-0023-3 · Cited by 22
Discussion: McCulloch-Pitts and related neural nets from 1943 to 1989
Cowan, J. D. (1990) · Bulletin of Mathematical Biology · doi:10.1007/bf02459569 · Cited by 22
Dendritic Neural Network: A Novel Extension of Dendritic Neuron Model
Tang, C. C., Ji, J., Todo, Y., Shimada, A., Ding, W., & Hirata, A. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3367819 · Cited by 22
Zhu, T., Jiang, M., Wang, Y., & Wang, Y.-S. (2024) · Thin-Walled Structures · doi:10.1016/j.tws.2024.112255 · Cited by 22
Iaboni, C., & Abichandani, P. (2024) · IEEE Access · doi:10.1109/access.2024.3479968 · Cited by 22
Prediction of cryptocurrency’s price using ensemble machine learning algorithms
Balijepalli, N. S. S. K., & Thangaraj, V. (2024) · European Journal of Management and Business Economics · doi:10.1108/ejmbe-08-2023-0244 · Cited by 22
Wu, T., Qian, L., Zeng, L., Zhu, J., Gao, H., Deng, Y., Han, Y., Qu, L., & Yi, W. (2025) · BMC Surgery · doi:10.1186/s12893-025-02802-2 · Cited by 22
Deokar, S. U., Kumar, N., & Singh, R. P. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104941 · Cited by 22
Learning the complexity of urban mobility with deep generative network
Yuan, Y., Ding, J., Jin, D., & Li, Y. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf081 · Cited by 22
Application of UAV remote sensing for vegetation identification: a review and meta-analysis
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Ullah, S. A., Hassan, S. A., Abou-Zeid, H., Qureshi, H. K., Jung, H., Mahmood, A., Gidlund, M., Imran, M. A., & Hossain, E. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3576571 · Cited by 22
Modeling Mechanisms of Cognition-emotion Interaction in Artificial Neural Networks, since 1981
Bozinovski, S. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.11.111 · Cited by 22
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Gelfand, A. E., Chen, Y., van der Maaten, L. J. P., & Welling, M. (2010) · Cited by 22
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Engineer, C. T., Perez, C., Carraway, R. S., Chang, K. Q., Roland, J. L., Sloan, A. M., & Kilgard, M. P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0078607 · Cited by 22
Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity
Osogami, T., & Otsuka, M. (2015) · Scientific Reports · doi:10.1038/srep14149 · Cited by 22
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Meyer‐Lindenberg, A. (2018) · Der Nervenarzt · doi:10.1007/s00115-018-0557-6 · Cited by 22
Bhardwaj, K. K., Banyal, S., & Sharma, D. K. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817356-5.00009-7 · Cited by 22
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Peuna, A., Thevenot, J., Saarakkala, S. S., Nieminen, M. T., & Lammentausta, E. (2021) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2021.02.561 · Cited by 22
Woodhead, J. D., & Landry, M. (2021) · SEG Discovery · doi:10.5382/geo-and-mining-13 · Cited by 22
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Li, S., Coraddu, A., & Oneto, L. (2022) · Engineering Structures · doi:10.1016/j.engstruct.2022.114423 · Cited by 22
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He, L., Xu, Y., He, W., Lin, Y., Tian, Y., Wu, Y., Wang, W., Zhang, Z., Han, J., Tian, Y., et al. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00674-9 · Cited by 22
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Allen-Zhu, Z., Gelashvili, R., Micali, S. M., & Shavit, N. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419100111 · Cited by 22
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Li, D., Ju, W., Fan, W., & Gu, Z. (2014) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.8.083670 · Cited by 22
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Wang, J., Zhao, P., & Hoi, S. C. H. (2012) · Proceedings · doi:10.1109/icdm.2012.116 · Cited by 22
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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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