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 10,101–10,200 of 13,333
Nadchodzi era sztucznej inteligencji w ochronie wzroku (i nie tylko...)
Pniewski, J. (2018) · ReIn UW (University of Warsaw)
Learning to compute inner consensus - A noble approach to modeling agreement between Capsules.Preprint
Faria, G. S. (2019) · arXiv (Cornell University)
Patching - A Framework for Adapting Immutable Classifiers to Evolving Domains
Kauschke, S. (2019) · TUbilio (Technical University of Darmstadt) · doi:10.26083/tuprints-00009089
Designing convolutional neural network architectures based on dynamical system concepts
Ferreira, M. D. (2019) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.55.2019.tde-26042019-082539
Identifying the Most Explainable ClassifierPreprint
Mullins, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.08595
Mehrzadfar, H. (2019) · Bilkent University Institutional Repository (Bilkent University)
Sharma, S. (2019) · Digital Commons - DU (University of Denver)
Farhangi, M. M. (2019) · ThinkIR: The University of Louisville's Institutional Repository (University of Louisville) · doi:10.18297/etd/3301
Misak, C. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.050
Towards Synthetic Dataset Generation for Semantic Segmentation Networks
Khan, S. (2019) · UWSpace (University of Waterloo)
Automatic semblance velocity analysis using Convolutional Neural Networks
Park, M. J. (2019) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-jnjq-df49
Analytic Aesthetics and Philosophy of Art
Davies, S. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.027
Feminist Philosophy and Real Politics
Finlayson, L. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.026
Ebbs, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.005
Becker, K., & Thomson, I. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.002
Iwamoto, R., & Yukawa, M. (2019) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2019.8902958
Sartre and Merleau-Ponty on Freedom
Carman, T. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.031
Guerrero, L., Kalmanson, L., & Mattice, S. A. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.055
Aesthetics, Psychoanalysis, and the Avant-Garde
Lee, J. S. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.044
John Rawls’sPolitical Liberalism
Schoelandt, C. V., & Gaus, G. F. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.024
KRATKOROČNA PROGNOZA REŽIMA DISTRIBUTIVNE MREŽE
Jerković, N. (2019) · Zbornik radova Fakulteta tehničkih nauka u Novom Sadu · doi:10.24867/05be20jerkovic
Jewish Philosophy and the Shoah
Katz, C. E. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.056
Logic in the Second Half of the Twentieth Century
Burgess, J. P. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.013
Authenticity and Social Critique
Guignon, C. B., & Aho, K. A. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.033
Classification in e-procurement
Roberts, P. J., Mitchell, R., Ruiz, V. F., & Bishop, J. M. (2012) · IEEE International Conference on Cybernetic Intelligent Systems (CIS) · doi:10.1109/cis.2013.6782151
Gelsleichter, Y. A., dos Anjos, L. H. C., Costa, E. M., Valente, G., Debiasi, P., Antunes, M. A. H., & Marcondes, R. A. T. (2019) · Preprints.org · doi:10.20944/preprints201911.0053.v1
Naturalism from the Mid-Twentieth Century to the Present
Macarthur, D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.016
Varieties of Externalism, Linguistic and Mental
Goldberg, S. C. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.007
Cerbone, D. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.051
The Concept of Autonomy in the History of the Frankfurt School
O’Connor, B. M. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.037
Contemporary Responses to Radical Skepticism
Pritchard, D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.011
Leo Strauss: Political Philosophy as First Philosophy
Zuckert, C., & Zuckert, M. P. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.039
The Computational Advantages of Intrinsic Plasticity in Neural Networks
Shaw, N. P. (2019) · UWSpace (University of Waterloo)
Becker, K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.012
Phenomenology and Ordinary Language Philosophy
Mulhall, S. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.048
The relationship between actions and significance of email
Alrashed, T. (2019) · DSpace@MIT (Massachusetts Institute of Technology)
Decision Making guided by Emotion A computational architecturePreprint
Béroule, D., & Gisquet-Verrier, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.09948
Portfolio Optimisation under Rough Stochastic Volatility via Machine Learning
Wang, K. (2019) · NORA - Norwegian Open Research Archives
Shrestha, A. (2019) · University of Bridgeport ScholarWorks (University of Bridgeport)
Conrads, P., Feaster, T. D., & Roehl, E. A. (2007) · Proceedings of the Water Environment Federation · doi:10.2175/193864707787780738
Shao, F., & Liu, H. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1423/1/012059
Subtractive Perceptrons for Learning Images: A Preliminary Report
Tizhoosh, H. R., Kalra, S., Lifshitz, S., & Babaie, M. (2019) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2019.8936079
Large-scale Kernel Methods and Applications to Lifelong Robot LearningPreprint
Camoriano, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.05629
Ruiz, J. Z. (2019) · Repositorio Institucional de la Universidad de Málaga (University of Málaga)
Training Neural Networks Through the Integration of Evolution and Gradient Descent
Morse, G. (2019) · Journal of International Crisis and Risk Communication Research
Dpto. de Geología y Obras Civiles, F. D. I., Barreto, W., Dpto. de Hidráulica, D. D. I. C., Torres, J., Dpto. de Geología y Obras Civiles, F. D. I., Torres, R., Dpto. de Geología y Obras Civiles, F. D. I., González, L., Dpto. de Geología y Obras Civiles, F. D. I., Picón, R., et al. (2019) · Revista Tecnica De La Facultad De Ingenieria Universidad Del Zulia · doi:10.22209/rt.ve2019a01
Laguerre-Gauss Preprocessing: Line Profiles as Image Features for Aerial Images ClassificationPreprint
Murillo-González, A., Pabón, J. D. O., Paniagua, J. G., & Montoya, O. L. Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.06729
Data classification using various learning algorithms
Baba, U. A. (2017)
Molina-Cobo, C., Forradellas, M. T. S., & Vidal-Suñé, A. (2019) · Revista Internacional de Organizaciones · doi:10.17345/rio23.193-221
Patton, R. M., Gao, S. B., Paulissen, S., Haas, N. Q., Jewell, B. C., Zhang, X., & Graf, P. A. (2020) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2020-01-0739
Hassanlou, L., Meshgini, S., Alizadeh, E., & Farzamnia, A. (2019) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke48569.2019.8965200
Wu, J. (2019) · Deep Blue (University of Michigan)
Usage of different neural networks in identification of plant types
Барцев, С. И., Ivanova, Y., & Saltykov, M. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/734/1/012097
End-to-end Optical Chinese Character Recognition Based on Deep Learning
Li, B. (2019) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1145/3377713.3377809
Kowalik, M., Kowalczyk, M., Michalik, J. M., Giebułtowski, M., Zalecki, R., Niewolski, J., & Tokarz, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03452
Online Passive-Aggressive Total-Error-Rate MinimizationPreprint
Jang, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.01771
Design and Application of Process Object Intelligent Model
Yin, F., Li, Q., Ye, S., Ding, J., Sun, J., & Cai, J. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8996196
Análisis de funcionamiento de una red neuronal implementada sobre una tarjeta SoC Zynq
Contreras, A. M., Reyes, J. I. D., González, A. A. M., & Varela, D. T. (2018) · Research in Computing Science · doi:10.13053/rcs-147-5-1
Caracterização autoral a partir de textos utilizando redes neurais artificiais
Dias, R. F. S. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.100.2020.tde-24012020-202805
Representation learning of spatio-temporal features from video
da Costa, G. D. B. P. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.55.2020.tde-03022020-093918
A New Clustering neural network for Chinese word segmentationPreprint
Zhao, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07458
Using neural networks to identify psychosis from fMRI data
Vahermaa, V. (2020)
Swarm Filter - A Simple Deep Learning Component Inspired by Swarm Concept
Nguyen, H.-T., & Nguyen, L.-M. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00221
Optymalizacja treningu i wnioskowania sieci neuronowych
Blok, M., & Pietrołaj, M. (2019) · Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne · doi:10.15199/59.2019.12.6
Application of Computational Intelligence to Energy Systems
Felice, M. D. (2011) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4068383
Arbeit in einer künftigen digitalen Gesellschaftsordnung
Frey, H., & Beste, D. (2020) · Technik im Fokus · doi:10.1007/978-3-662-59531-2_2
Design Patterns based on Deep Learning analyzing Distributed Data
Burkhardt, D., Agyei-Kena, N., Frey, P., Kurrle, S., & Lasi, H. (2020) · doi:10.30844/wi_2020_a5-burkhardt
Sound event detection with weakly labelled data
Kong, Q. (2020) · Surrey Research Insight Open Access (The University of Surrey) · doi:10.15126/thesis.00853328
Otero, A. G. L., Potiens, A. J., & Marumo, J. T. (2020) · Brazilian Journal of Radiation Sciences · doi:10.15392/bjrs.v8i1.1132
Manzini, N. (2020) · theses.fr (ABES) · doi:10.70675/3fda1d22z93bbz421fza98bza9c690d1f270
Contrôle tolérant au vieillissement dans des systèmes pile à combustible PEMFC
Mezzi, R. (2019) · theses.fr (ABES) · doi:10.70675/55694cdcz08e9z4e8az9c79zcebb993e8af9
Cerè, G. (2019) · ORCA Online Research @Cardiff (Cardiff University)
The Loyalty of Travel Agency Clients. An Empirical Study
Michalska-Dudek, I. (2018) · Barometr Regionalny Analizy i Prognozy · doi:10.56583/br.351
Bayesianism in Cognitive Science
Bermúdez, J. L. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108339216.009
Deep Learning for Handwritten Digits Recognition Using MATLAB Toolbox
Chen, J. (2019) · UVic’s Research and Learning Repository (University of Victoria)
Optimizing Convolutional Neural Network Parameters for Better Image ClassificationPreprint
Dhingra, M., Rawat, S. S., & Fiaidhi, J. (2020) · doi:10.36227/techrxiv.12089358.v1
Foundations of deep convolutional models through kernel methods
Bietti, A. (2019) · theses.fr (ABES) · doi:10.70675/fa4f83e6z7dc4z4eaaz87b7zbb3b0ee76a79
Statistical models on manifolds for anomaly detection in medical images
Tilquin, F. (2019) · theses.fr (ABES) · doi:10.70675/c53be4bcz78d4z4c6aza980za89fa912ecff
Câmara, D. (2015) · Elsevier eBooks · doi:10.1016/b978-1-78548-021-8.50003-x
Sikka, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.07114
Termografia em um contexto de manutenção industrial eficiente
Dinardi, P. T., da Costa, K. S., & Torné, I. G. (2020) · Editora Poisson eBooks · doi:10.36229/978-65-86127-20-1.cap.05
Armstrong, J. A., Osborne, C. M. J., & Fletcher, L. (2020) · Astronomy & Geophysics · doi:10.1093/astrogeo/ataa044
Prediction of Short-Term Heavy Fog Based on Neural Network Method
时, 玮. (2020) · Software Engineering and Applications · doi:10.12677/sea.2020.93021
EVALUATION OF THE RATIONALITY OF ENTERPRISE POTENTIAL MANAGEMENT
Shchebel, A. (2020) · ΛΌГOΣ МИСТЕЦТВО НАУКОВОЇ ДУМКИ · doi:10.36074/2663-4139.10.01
Mokhtari, M. (2020) · Summit (Simon Fraser University)
Real-Time Instance and Semantic Segmentation Using Deep Learning
Kolhatkar, D. (2020) · uO Research (University of Ottawa) · doi:10.20381/ruor-24844
Spicer, J. (2026) · Warwick Research Archive Portal (University of Warwick) · doi:10.82444/warw.33802813
Schettini, G. M. D. L. P., & Neto, P. S. G. D. M. (2020) · Revista de Engenharia e Pesquisa Aplicada · doi:10.25286/repa.v5i2.1339
Structure-guided registration in learning based image analysis
Lee, M. C. H. (2019) · Spiral (Imperial College London) · doi:10.25560/79296
The exploratory status of postconnectionist models
Milojević, M., & Subotić, V. (2020) · Theoria Beograd · doi:10.2298/theo2002135m
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