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
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Chen, Z., & Zhu, B. (2000) · Lecture notes in computer science · doi:10.1007/3-540-40996-3_10 · Cited by 20
The computer as a metaphor for psychological inquiry: Considerations and recommendations
Massaro, D. W. (1986) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03201006 · Cited by 20
Induction and polynomial networks
Elder, J. F., & Brown, D. E. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1995.537877 · Cited by 20
Conrads, P., Feaster, T. D., & Harrelson, L. G. (2008) · Scientific investigations report · doi:10.3133/sir20085170 · Cited by 20
The transmission of language: models of biological and cultural evolution
Smith, K. (2003) · ERA · Cited by 20
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Balduzzi, D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1310.6536 · Cited by 20
Evolving visibly intelligent behavior for embedded game agents
Miikkulainen, R. P., & Bryant, B. D. (2006) · Texas ScholarWorks (Texas Digital Library) · Cited by 20
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Shen, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12683 · Cited by 20
High precision coding in visual cortexPreprint
Stringer, C., Michaelos, M., & Pachitariu, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/679324 · Cited by 20
Latent space conditioning for improved classification and anomaly detectionPreprint
Norlander, E., & Sopasakis, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.10599 · Cited by 20
Efficient active learning of sparse halfspaces with arbitrary bounded noisePreprint
Zhang, C., Shen, J., & Awasthi, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04840 · Cited by 20
Towards a machine-learning architecture for lexical functional grammar parsing
Chrupała, G. (2008) · Dublin City University Open Access Institutional Repository (Dublin City University) · Cited by 20
A study on automatic control of wheel loaders in rock/soil loading
Wu, L. (2003) · UA Campus Repository (The University of Arizona) · Cited by 20
Islam, M. M., Li, G.-H., Poly, T. N., & Li, Y.-C. (. (2021) · Healthcare · doi:10.3390/healthcare9121632 · Cited by 19
Materials, processes, devices and applications of magnetoresistive random access memory
Yang, M., Cui, Y., Chen, J., & Luo, J. (2024) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ad87cb · Cited by 19
Shirae, S., Debsarkar, S. S., Kawanaka, H., Aronow, B. J., & Prasath, V. B. S. (2025) · IEEE Access · doi:10.1109/access.2025.3556713 · Cited by 19
Spiking Neural Networks in Imaging: A Review and Case Study
Voudaskas, M., MacLean, J. I., Dutton, N. A. W., Stewart, B. D., & Gyöngy, I. (2025) · Sensors · doi:10.3390/s25216747 · Cited by 19
Coactive Critiquing: Elicitation of Preferences and Features
Teso, S., Dragone, P., & Passerini, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10929 · Cited by 19
Deep learning and Its Development
Zhao, Q., & Shang, Z. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1948/1/012023 · Cited by 19
David, T. W., Soares, G. A., Bristow, N., Bagnis, D., & Kettle, J. (2021) · Progress in Photovoltaics Research and Applications · doi:10.1002/pip.3453 · Cited by 19
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Fransvea, P., Fransvea, G., Liuzzi, P., Sganga, G., Mannini, A., & Costa, G. (2022) · International Journal of Surgery · doi:10.1016/j.ijsu.2022.106954 · Cited by 19
Gruosso, G., & Gajani, G. S. (2022) · IEEE Access · doi:10.1109/access.2022.3222986 · Cited by 19
Dzięcioł, J., & Sas, W. (2023) · Materials · doi:10.3390/ma16041500 · Cited by 19
Online learning using deep random vector functional link network
Shiva, S., Hu, M., & Suganthan, P. N. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106676 · Cited by 19
Ganji, H. T., & Seylabi, E. (2023) · Computers and Geotechnics · doi:10.1016/j.compgeo.2023.105796 · Cited by 19
Wu, J., Liu, Y., Ge, F., & Yu, D. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108227 · Cited by 19
Grigoraș, C. C., Zichil, V., Ciubotariu, V. A., & Cosa, S. M. (2024) · Machines · doi:10.3390/machines12030180 · Cited by 19
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Alnaji, L. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300216 · Cited by 19
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Li, X., Nieber, J. L., & Kumar, V. (2024) · Vadose Zone Journal · doi:10.1002/vzj2.20361 · Cited by 19
Nguyen, H.-H., & Truong, V.-H. (2024) · Structures · doi:10.1016/j.istruc.2024.107369 · Cited by 19
Shukla, C., Mishra, P. P., Dubey, R., & Dash, S. K. (2024) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2024.126327 · Cited by 19
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Koyama, H., Kashio, A., & Yamasoba, T. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13247577 · Cited by 19
Wu, S., Zhu, M., Zhang, Z., Yao, Y., Li, Y., & Li, D. (2025) · International Journal of Mining Reclamation and Environment · doi:10.1080/17480930.2025.2494121 · Cited by 19
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Bahr, D. E., Webster, J. G., Grady, D. G., Kronenberg, F., Creasman, J. M., Macer, J., Shults, M. C., Tyler, M. E., & Zhou, X. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/2/95 · Cited by 19
Select and Sample - A Model of Efficient Neural Inference and Learning
Shelton, J. A., Sheikh, A.-S., Berkes, P., Bornschein, J., & Luecke, J. (2011) · Cited by 19
Representation and Integration: Combining Robot Control, High-Level Planning, and Action Learning
Petrick, R. P. A., Kraft, D., Mourão, K., Geib, C., Pugeault, N., Krüger, N., & Steedman, M. J. (2008) · University of Southern Denmark Research Portal (University of Southern Denmark) · Cited by 19
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Real-Time Hybrid Intrusion Detection System Using Apache Storm
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Advances in geomatic simulations for environmental dynamics
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Peyrard, C., Mamalet, F., & García, C. (2015) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0005297200840091 · Cited by 19
SOL: A library for scalable online learning algorithms
Wu, Y., Hoi, S. C. H., Liu, C., Lu, J., Sahoo, D., & Yu, N. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.03.077 · Cited by 19
Cho, H., Son, H., Seong, K., Kim, B., Park, H.-J., & Sim, J. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2017.2762002 · Cited by 19
Khan, T. A., Alam, M. M., Shahid, Z., & Su’ud, M. M. (2017) · IEEE International Conference on Engineering Technologies and Social Sciences (ICETSS) · doi:10.1109/icetss.2017.8324170 · Cited by 19
An unsupervised data completion method for physically-based data-driven models
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Shain, C., & Elsner, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1007 · Cited by 19
Online Compact Convexified Factorization Machine
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Liu, X., He, S., Gu, Y., Xu, Z., Zhang, Z., Wang, W., & Liu, P. (2019) · ISA Transactions · doi:10.1016/j.isatra.2019.08.069 · Cited by 19
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Chen, J., & Jia, J. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.91953 · Cited by 19
Haklıdır, F. S. T., & Haklıdır, M. (2020) · Environmental Earth Sciences · doi:10.1007/s12665-020-08907-6 · Cited by 19
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Vaishnav, V., & Vajpai, J. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110191 · Cited by 19
Tang, W., Chen, J., & Hong, H. (2020) · Chemosphere · doi:10.1016/j.chemosphere.2020.128567 · Cited by 19
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Mattia, G. M., Sarton, B., Villain, E., Vinour, H., Ferré, F., Buffières, W., Lann, M. V. L., Franceries, X., Péran, P., & Silva, S. (2022) · Neurocritical Care · doi:10.1007/s12028-022-01525-z · Cited by 19
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Nonlinear neurons with human-like apical dendrite activations
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Hsu, D., Karampatziakis, N., Langford, J. C., & Smola, A. J. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9781139042918.015 · Cited by 19
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y Ocerín, J. M. C., & Villamandos, N. C. (2001) · Studies of Applied Economics · Cited by 19
Almeida, C., Baugh, C. M., Lacey, C. G., Frenk, C. S., Granato, G. L., Silva, L., & Bressan, A. (2009) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2009.15920.x · Cited by 19
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Irie, B., & Kawato, M. (1991) · Electronics and Communications in Japan (Part III Fundamental Electronic Science) · doi:10.1002/ecjc.4430741111 · Cited by 19
Qiu, P., Wang, Z., & Liu, K. J. R. (2007) · IEEE Signal Processing Magazine · doi:10.1109/msp.2007.273063 · Cited by 19
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Singh, V. K. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.05.231 · Cited by 19
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Turchetti-Maia, A. L., Shomrat, T., & Hochner, B. (2017) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190456757.013.29 · Cited by 19
Spike neural models (part I): The Hodgkin-Huxley model
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Kriegel, F. L., Köhler, R., Bayat‐Sarmadi, J., Bayerl, S., Hauser, A. E., Niesner, R. A., Luch, A., & Cseresnyés, Z. (2017) · Cytometry Part A · doi:10.1002/cyto.a.23279 · Cited by 19
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Cipollini, F., Oneto, L., Coraddu, A., Savio, S., & Anguita, D. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.503 · Cited by 19
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Greenes, R. A. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-398476-0.00002-6 · Cited by 19
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Cassidy, C. (2020) · World Patent Information · doi:10.1016/j.wpi.2020.101968 · Cited by 19
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Kainen, P. C., Kůrková, V., & Sanguineti, M. (2013) · Intelligent systems reference library · doi:10.1007/978-3-642-36657-4_5 · Cited by 19
Models developed by three techniques did not achieve acceptable prediction of binary trauma outcomes
Wolfe, R. S., McKenzie, D. P., Black, J., Simpson, P. M., Gabbe, B. J., & Cameron, P. A. (2005) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2005.05.007 · Cited by 19
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Hirasawa, K., Mabu, S., & Hu, J. (2006) · Neural Networks · doi:10.1016/j.neunet.2005.10.005 · Cited by 19
Neural networks for perceptual processing: from simulation tools to theories
GURNEY, K. N. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2006.1962 · Cited by 19
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Phillips, P., Davies, F., & Moutinho, L. (1999) · Management Decision · doi:10.1108/00251749910264514 · Cited by 19
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Kim, W., Lee, H., Yoo, S. J., & Baik, S. W. (2005) · Lecture notes in computer science · doi:10.1007/11550822_75 · Cited by 19
Schwefel, H., & Hammel, U. (1997) · Informatik aktuell · doi:10.1007/978-3-642-60831-5_15 · Cited by 19
Haken, H. (1988) · IEEE Circuits and Devices Magazine · doi:10.1109/101.9569 · Cited by 19
The Role of Neural Networks in Reactor Diagnostics and Control
Pázsit, I., & Kitamura, M. (2002) · Advances in nuclear science and technology · doi:10.1007/0-306-47811-0_3 · Cited by 19
López, F. J. A., & Balboa, J. L. G. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.11.009 · Cited by 19
Regeniter, A., Steiger, J. U., Scholer, A., Huber, P., & Siede, W. H. (2003) · Clinical Chemistry · doi:10.1373/49.1.41 · Cited by 19
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Kitzis, S. N., Kelley, H., Berg, E., Massaro, D. W., & Friedman, D. (1998) · Journal of Mathematical Psychology · doi:10.1006/jmps.1998.1215 · Cited by 19
Joshi, A. K., & Shen, L. (2006) · Cited by 19
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Lee, K. C., & Takefuji, Y. (1992) · International Journal of Electronics · doi:10.1080/00207219208925577 · Cited by 19
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Lehmann, C., & Blayo, F. (1991) · Springer eBooks · doi:10.1007/978-1-4615-3752-6_32 · Cited by 19
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Long, L. (1998) · VTechWorks (Virginia Tech) · Cited by 19
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Arsiwalla, X. D., Moulin-Frier, C., Herreros, I., Sánchez-Fibla, M., & Verschure, P. F. M. J. (2017) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/emhnp · Cited by 19
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Fusi, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.04946 · Cited by 19
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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