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 12,801–12,900 of 13,333
Hu, X., Shi, K., Wang, Z., Wang, J., Guo, P., Song, W., Ban, H., & Ren, B. (2026) · Journal of Thermal Science and Engineering Applications · doi:10.1115/1.4071789
Καράτσης, Ά. (2026) · Dione (University of Piraeus)
Research on the generalization of a blood donor recruitment framework based on machine learning
Ye, X., Zhang, Z. M., Jiao, Y., Wang, M., Cai, L., Li, Z., Fang, P., Yan, H., Zhou, C., Ma, Y., et al. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03525-x
An Ensemble Method for Variable-Grain Political Ideology Classification on Social Media Texts
Kovacs, E.-R., Sgârdea, F. M., Ştefănescu, A., Pitulice, I. C., & Cotfas, L. (2026) · IEEE Access · doi:10.1109/access.2026.3684000
AI-Based Dementia Prediction From Low-Dose Brain CT in Mild Cognitive Impairments
Park, S., Ahn, J., Na, D. G., Kwak, K., Kim, J. P., & study, O. B. O. T. K.-R. (2026) · Dementia and Neurocognitive Disorders · doi:10.12779/dnd.2026.25.2.91
Pioneers & Pathfinders: 10 Years of Frontiers in Medicine
(2026) · Frontiers research topics · doi:10.3389/978-2-8325-7221-4
Chakraborty, P., Giles, S. A., & Cornelius, J. (2025) · IEEE International Integrated Reliability Workshop final report · doi:10.1109/iirw66154.2025.11481700
Belli, F., & Türkmen, A. R. (2026) · Bagcilar Medical Bulletin · doi:10.4274/bmb.galenos.2026.15807
(2022) · Emerald Group Publishing Limited eBooks · doi:10.1561/978-1-63828-053-820251008
Tracking of entanglement evolution in non-Markovian environments: a machine learning approach
Chen, P., Yoon, S., Shi, Y., & Yu, T. (2026) · Physica Scripta · doi:10.1088/1402-4896/ae6732
Kamzan, M., Roshanian, J., Teshnehlab, M., Ghanifar, M., & Nikkhah, A. A. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114522
Rendón, M. F. (2025) · JOURNAL DE OBJETOS Y OBJETIVOS MATEMÁTICOS · doi:10.66735/joom.v7i12.4
Zhu, K., Xu, W., Chen, H., Liu, N., & Zhang, L. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3689581
Wang, Z., Wang, Y., Yu, T., Yi, Y., Xue, K., & Xu, W. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1802259
AI Definitions and Key Milestones
Crespo, R. F. (2026) · Springer eBooks · doi:10.1007/978-3-032-18462-7_3
A Functional Bus Stop Typology: Explainable Machine Learning on Transit Data to Inform Policymaking
Azhdari, M., Oke, J. B., & Gonzales, E. J. (2026) · Data Science for Transportation · doi:10.1007/s42421-026-00155-7
Gupta, V., Katre, R. R., Sirdesai, N. N., Panchal, S., & Agarwal, A. K. (2026) · Journal of the Geological Society of India · doi:10.17491/jgsi/2026/174401
Hanson, S. J., & Hanson, C. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11560-3
Asraoui, L. A. L., Chafik, H., Baddou, A., & Abdelkader, B. (2026) · Springer eBooks · doi:10.1007/978-3-032-16597-8_4
Moreira, G. D. C., Costa, J. F. C. L., & Deutsch, C. V. (2026) · Natural Resources Research · doi:10.1007/s11053-026-10690-3
Deep Reinforcement Learning and the IID Assumption
Özgan, F., Engelbrecht, H. A., & Schiele, G. (2026) · International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML) · doi:10.1109/aaiml67890.2026.11498102
From Perceptron to Feedforward Neural Networks: Foundations and Early Models
Ghojogh, B., & Ghodsi, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-10738-1_2
Social learning strategies for matters of tastePreprint
Herzog, S., Barkoczi, D., & Analytis, P. P. (2018) · OSF Preprints (OSF Preprints)
Martin-Saquet, J., Dorise, A., Villain, E., Tergolina, V. B., Sanchez, S., & Panzoli, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-23604-3_15
Bartlett, A., Liem, C. C. S., & Panichella, A. (2026) · Research Square · doi:10.21203/rs.3.rs-9426270/v1
Von Neumann NetworksPreprint
Chandra, S. S. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.05780
Choi, H., Luu, H. M., & Park, S. (2026) · Magnetic Resonance in Medicine · doi:10.1002/mrm.70411
Generatiivisen tekoälyn mahdollisuudet matematiikan ylioppilaskokeeseen valmistautumisen tukena
Kuoppala, E. (2026) · Tampere University Institutional Repository (Tampere University)
Hybrid Adaptive Resonance Theory Model for Online Noisy Data Classification
Shi, M., Yin, J., Wang, H., Lim, C. P., Cao, J., & Zheng, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7394-3_4
William, B. A., Njimongou, F. S., Nzeuga, A. R., Ambassa, A. B., Béssémè, F. M., & Ngounouno, I. (2026) · Ore Geology Reviews · doi:10.1016/j.oregeorev.2026.107318
Machine learning exploration of YBCO thin film growth parameters under small data constraint
Wang, X., Zhang, J., Li, C., Ren, J., Wang, Y., Zhang, Y., Xiao, J., Yang, L., Jiang, X., & Yuan, J. (2026) · Acta Physica Sinica · doi:10.7498/aps.75.20260159
BCM rules for physical neural networks: mechanism, realization, and prospects
Huang, J., Huang, A., Mei, W. A. N. G., Xiao, Z., & Di, Z. (2026) · Acta Physica Sinica · doi:10.7498/aps.75.20260397
Fang, L., Song, X., Yin, W., Huang, Y., Huang, J., Fang, Y., & Su, H. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01108-7
Physics-Modeled Neural NetworksPreprint
Felipe-Sosa, R., del Rey, A. M., & Ceballos, M. F. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.08176
TiF: A Multi‐Scale Data Fusion and Fourier Encoding Framework for Financial Risk Prediction
Zhang, H. S., & Zhou, P. (2026) · Journal of Forecasting · doi:10.1002/for.70172
Zúñiga, J. M. M., & Ávila, N. E. P. (2026) · Estudios y Perspectivas Revista Científica y Académica · doi:10.61384/r.c.a..v6i2.1850
Le, N. A., Nguyen, H. Q., Thanh, P. N., Nguyen, H. A., Le, D. A. T., Tran, B., Mai, S. T., & Anh, D. T. (2026) · Hydrological Sciences Journal · doi:10.1080/02626667.2026.2671902
Inductive Biases in Field-level Cosmological Inference from Galaxy Catalogs
Baldwin, J. O., Genel, S., & Villaescusa-Navarro, F. (2026) · The Astrophysical Journal · doi:10.3847/1538-4357/aea400
Complex systems vs. complex adaptive systems: why the difference matters
Shutters, S. T., & Dhein, K. (2026) · Frontiers in Complex Systems · doi:10.3389/fcpxs.2026.1808634
Song, R., Jin, W., Zheng, H., He, S., Li, H., Zhong, J., Xian, H., Hu, Y., & Zhang, M. (2026) · Eurasian Journal of Medicine and Oncology · doi:10.36922/ejmo025360375
Deep learning-based object detection approaches for landslide identification
Geem, Z. W., Bekdaş, G., Ni̇gdeli, S. M., Aydın, Y., Işıkdağ, Ü., & Kim, T.-H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41481-7.00003-1
Baier, D., Rese, A., & Randazzo, D. (2026) · Springer eBooks · doi:10.1007/978-3-658-49371-4_13
III. Künstliche Intelligenz in der radiologischen Diagnostik: Rechtliche Aspekte
Dokmanac, S., & Günther, J. (2026) · Verlag Karl Alber eBooks · doi:10.5771/9783495988251-77
Wagner, R., Caspers, J., Eickhoff, S. B., Langner, R., Steinborn, M. B., Dokmanac, S., & Günther, J. (2026) · Verlag Karl Alber eBooks · doi:10.5771/9783495988251-1
Constantin, S. G. (2026) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/1000193274
Deep Learning Approaches for Accelerated MRI Reconstruction: A Review
Ranjana, K., & Dass, S. (2026) · International Conference on Smart Multidomain Integrated Learning Environments (ICSMILE) · doi:10.1109/icsmile69273.2026.11519289
Data and Knowledge Dual-Driven Artificial Intelligence
Wang, H., Wang, M., Liu, L., & Wu, J. (2026) · Springer eBooks · doi:10.1007/978-981-95-5585-7_4
Online Reinforcement Learning Torque Controller for Electrical Motor Drives
Moreira, W. K., LAHM, B. H., Silva, P. S., Vieira, R. P., & Rech, C. (2026) · Conference proceedings/Conference proceedings - IEEE Applied Power Electronics Conference and Exposition · doi:10.1109/apec51134.2026.11517130
SMiLE: safe machine learning via embedded overapproximation
Francobaldi, M. (2026) · AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna) · doi:10.48676/unibo/amsdottorato/12471
The Thermodynamic Costs of Simple Linear Regression
D’Ambrosia, S. H., Daniels, S., DeWeese, M. R., & Sahai, A. (2026) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2026.3732971
Khalifa, A., Rebaï, I., BenSaid, F., & Kacem, Y. H. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-23286-1_30
Aliyev, K., Artun, E., & Kulga, B. (2026) · Unconventional Resources · doi:10.1016/j.uncres.2026.100432
Leskinen, N. (2026) · Aaltodoc (Aalto University)
A review on additive manufacturing techniques and process parameter optimization methods
Gade, S., Praharaj, P., Hiwase, V., Gurav, P., Mahajan, A., Yadav, S., Satpute, J. B., Campli, S., Hakizimana, E., & Notla, S. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08873-x
A step-by-step semiotic understanding of LLMs and chatbots through interdisciplinary dialogue
Thibault, M., & Allois, D. (2025) · Digital Age in Semiotics & Communication · doi:10.33919/dasc.25.8.2
Erciyes, K. (2026) · Texts in computer science · doi:10.1007/978-3-032-05294-0_16
AI-Driven Approaches in Construction and Demolition Waste Management: A Systematic Review
Sönmez, N., & Tokdemir, O. B. (2026) · Lecture notes in civil engineering · doi:10.1007/978-981-95-8489-5_21
Humanism and Wisdom in the AI Era
Söilen, K. S. (2026) · Springer eBooks · doi:10.1007/978-3-032-23069-0_10
Tirumala, K. M. P., & Sekhar, M. C. (2026) · International Conference on Knowledge Engineering and Communication Systems (ICKECS) · doi:10.1109/ickecs70176.2026.11527999
Shibue, S., Song, Y., & Takemura, K. (2026) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2026.118039
Nagy, M., Végh, L., & Takáč, O. (2026) · Eruditio-Educatio · doi:10.36007/eruedu.2026.1.056-066
Cavalleri, E., Alavinezhad, M., Mesiti, M., & Malchiodi, D. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774905.3794655
The Industrial Revolution of the Intelligence Age
Faraboschi, P., Kalinowski, C., Young, C., Frachtenberg, E., & Ho, R. (2026) · Computer · doi:10.1109/mc.2026.3678508
Faal, R., Patro, E. R., Ala‐aho, P., & Haghighi, A. T. (2026) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2026.103591
Utvärdering av stora språkmodeller för generering av Python-docstrings
Röblom, O. (2026) · Doria (University of Helsinki)
Ferstler, Y. (2026) · Archipel (Université du Québec à Montréal)
LSTM-Driven DGA values Forecasting for Predictive Maintenance of Power Transformers
Alguraishi, A., Altaisan, M. A., Almutairi, R., & Rob, R. (2024) · Saudi Arabia Smart Grid Conference (SASG) · doi:10.1109/sasg64132.2024.11535897
Assembling and Training the Perceptron: A Personal Account
Jensen, G. O., & Dunning, D. E. (2026) · IEEE Annals of the History of Computing · doi:10.1109/mahc.2026.3686038
Dynamic Short Convolutions Improve TransformersPreprint
Sieberling, O., Runwal, B., Panda, R., & Kim, Y. J. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2606.03825
Classification of real and falsified narratives in a repeated-measures text corpus
Wei, R. (2020) · cIRcle (University of British Columbia)
Wang, Z., (24122499), P. P., & Holan, S. H. (2026) · Handbook of statistics · doi:10.1016/bs.host.2026.05.005
Beyond scaling: Non-monotonic effects of depth and width in neural networks
Christakis, N., Fung, D., & Drikakis, D. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134166
Jaimes, C. A. M., & Quesada, S. D. C. (2026) · Universidad Industrial de Santander
Ten lectures on turbulence: 2. AI and turbulence
Wang, X., Ke, C., & Lee, C. (2026) · Transport Phenomena · doi:10.1515/tp-2026-0035
Machine Learning and Isogeometric Analysis
Dixit, P. M., & Gautam, S. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33389-7.00017-6
Yapay Zekâ, Medyada Temsil ve Anlama Dair Sorunlar
Bilge, A. T., & Devran, Y. (2026) · AJIT-e Academic Journal of Information Technology · doi:10.5824/ajite.2026.02.007.x
The Generative Mind: Internalizing Noise in Brains and Machines
Ryu, J. (2026) · Journal of Sciences and Innovation for Sustainable Peace · doi:10.23184/jsisp.vol.4.no.1.06
Akada, T. (2026) · Institutional Repositories DataBase (IRDB) · doi:10.18926/70603
Red fescue (Festuca rubra L.) variety recognition using subset division and neural networks
Slebioda, L., & Zawieja, B. E. (2026) · Theory in Biosciences · doi:10.1007/s12064-026-00480-z
Li, K., Wang, X., & Zhang, X. (2026) · International Conference on Geology, Mapping and Remote Sensing (ICGMRS) · doi:10.1109/icgmrs70230.2026.11542248
Directional Linear Separability of Neural Representations: Geometry and TransformationsPreprint
Wei, Y., Qi, X., Yang, S., & Shen, F. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2606.08721
Ergün, D. (2026) · İNİF E - Dergi · doi:10.47107/inifedergi.1737953
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
Mena, H., Blaskovits, J. T., Lin, K., & Andrienko, D. (2026) · Advanced Materials · doi:10.1002/adma.202523667
Yang, Y., & Xiao, J. (2026) · Advanced topics in science and technology in China · doi:10.1007/978-981-95-8037-8_2
Jet quenching identification via supervised learning in simulated heavy-ion collisions
da Silva, L. L., & Munhoz, M. G. (2026) · Physical Review C · doi:10.1103/nhl8-kknn
Diffractive Arithmetic Processing by Conditional Optical Generative Models
Wang, F., Sun, Y., Zhang, Z., Yang, C., Chen, N., Wang, S., Han, J., Li, Y., Qu, G., Wang, S., et al. (2026) · Laser & Photonics Review · doi:10.1002/lpor.202502983
Nanomaterials and nanodevices for physical reservoir computing
Jeong, S., Kang, B., Lee, J., & Oh, H. (2026) · Current Applied Physics · doi:10.1016/j.cap.2026.06.005
A Classification-based Matheuristic Approach for the Covering Tour Problem with Path UpgradesPreprint
Amitrano, D., Baldomero‐Naranjo, M., Boccia, M., Mancuso, A., & Rodríguez-Chía, A. M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6915661
4DO-DETR for otitis media detection
Zhao, X., Zhang, H., Liu, D., & Lei, G. (2026) · Scientific Reports · doi:10.1038/s41598-026-44468-7
Quach, C., Xu, Y., Safikou, E., & Putnam, S. A. (2026) · Machine Learning from Challenging Data · doi:10.1117/12.3096569
Su, S. (2026) · Research Square · doi:10.21203/rs.3.rs-9976268/v1
Janiš, T. (2026) · Brno University of Technology Digital Library (Brno University of Technology)
SoTA: An Interactive Art Exhibition for Public AI Engagement
Choi, J., Hwang, I., Park, S., Cho, H., Bae, H., & Kang, Y. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812889
Xu, H., Xue, H., Yuan, H., & Huang, Z. (2026) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2026.2682550
Hypergraph-Based Semantic Models in Cognitive and Robotic Systems
Hajdu, C. (2026) · Széchenyi István Egyetem · doi:10.15477/sze.mmtdi.2026.008
The Mathematics of AI Winters: The mathematical Taxonomy of Paradigm Fragility in AI WinterPreprint
Alonso, M. N. I., & Aznar, D. P. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2606.12610
Zhang, Y., Li, P., Yang, X., Li, B., Yi, Z., Fan, L., Luo, J., & Tang, J. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123109
Sztuczna inteligencja – nowe rodzaje ryzyka
Jajuga, K., & Pietrzyk, R. (2026) · Wroclaw University of Economics and Business eBooks · doi:10.15611/2026.46.3.03
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