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 9,101–9,200 of 13,333
Squeezing and Accelerating Neural Networks on Resource Constrained Hardware for Real Time Inference
Presutto, M. (2019) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 1
Sanya, R., Maiga, G., & Mwebaze, E. (2019) · Preprints.org · doi:10.20944/preprints201910.0319.v1 · Cited by 1
On Bioelectric AlgorithmsPreprint
Gilbert, S. L., Maguire, J., & Newport, C. (2018) · arXiv (Cornell University) · doi:10.4230/lipics.disc.2019.19 · Cited by 1
Uncertainty Measures and Transfer Learning in Active Learning for Text Classification
Oftedal, T. O. S. (2019) · NORA - Norwegian Open Research Archives · Cited by 1
Etmann, C., Schmidt, M., Behrmann, J., Boskamp, T., Hauberg‐Lotte, L., Peter, A., Casadonte, R., Kriegsmann, J., & Maaß, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.05459 · Cited by 1
Some statistical learning problems with incomplete data
Baudry, M. (2020) · theses.fr (ABES) · doi:10.70675/d0e96b44zdafbz449bzb676zfa79bf6ba227 · Cited by 1
Latent Variable Machine Learning Algorithms: Applications in a Nuclear Physics Experiment
Solli, R. (2019) · NORA - Norwegian Open Research Archives · Cited by 1
The Reflectron: Exploiting geometry for learning generalized linear models.Preprint
Boffi, N. M., & Slotine, J.-J. E. (2020) · arXiv (Cornell University) · Cited by 1
It may be time to perfect the neuron of artificial neural networkPreprint
Liu, G. (2020) · doi:10.36227/techrxiv.12477266.v4 · Cited by 1
Space of Reasons and Mathematical ModelPreprint
Richter, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02489 · Cited by 1
Depth-Adaptive Neural Networks from the Optimal Control viewpointPreprint
Aghili, J., & Mula, O. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02428 · Cited by 1
Machine learning classification of entrepreneurs in British historical census data
Montebruno, P., Bennett, R. J., Smith, H. S., & van Lieshout, C. (2020) · Munich Personal RePEc Archive (Ludwig Maximilian University of Munich) · doi:10.17863/cam.48279 · Cited by 1
Deep learning architectures applied to wind time series multi-step forecasting
Font, J. M. (2020) · doi:10.5821/dissertation-2117-328183 · Cited by 1
Data Anonymization using Machine Learning and Natural Language Processing
Helbrink, J., & Åkesson, S. (2020) · Lund University Publications Student Papers (Lund University) · Cited by 1
A fully integrated neural computing system
Rocha, P. V. (1992) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 1
Wang, K., Aanjaneya, M., & Bekris, K. E. (2020) · arXiv (Cornell University) · Cited by 1
Linear Separation via OptimismPreprint
Hanashiro, R., & Abernethy, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.08797 · Cited by 1
Da Navalha de Occam a um método de categorização de textos simples, eficiente e robusto
Silva, R. M. (2017) · doi:10.47749/t/unicamp.2017.980306 · Cited by 1
Rethinking supervised learning: insights from biological learning and from calling it by its namePreprint
Hernández-García, Á. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.02526 · Cited by 1
On Training of Artificial Neural NetworksPreprint
Malakooti, B. B., & Wang, J. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2921637 · Cited by 1
Sample-Optimal PAC Learning of Halfspaces with Malicious NoisePreprint
Shen, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06247 · Cited by 1
Rutherford, J. W. (2019) · ResearchWorks at the University of Washington (University of Washington) · Cited by 1
Supervised Learning in Spiking Neural Networks
Buller, B. T. (2020) · Research Repository (Delft University of Technology) · Cited by 1
Kerr microcombs based on soliton crystals for high-speed, scalable optical neural networksPreprint
Moss, D. (2020) · doi:10.36227/techrxiv.13193051.v1 · Cited by 1
Artificial Intelligence Narratives: An Objective Perspective on Current DevelopmentsPreprint
Klarmann, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.11961 · Cited by 1
A Survey on Semi-parametric Machine Learning Technique for Time Series Forecasting.Preprint
Ahmad, K. M., He, G., Yu, W., Xu, X., Kumar, J., & Saleem, M. A. (2021) · arXiv (Cornell University) · Cited by 1
Automated Tuning of Gate-defined Quantum Dots
Darulová, J. (2020) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000473107 · Cited by 1
Alejo, L. (2021) · TUbilio (Technical University of Darmstadt) · doi:10.26083/tuprints-00013463 · Cited by 1
SAT-based Method to Improve Neural Distinguisher and Applications to SIMON.Preprint
Hou, Z., Ren, J., & Chen, S. (2021) · IACR Cryptology ePrint Archive · Cited by 1
Scalable Data Analytics and Machine Learning on the Cloud
Salama, A. (2021) · TUbilio (Technical University of Darmstadt) · doi:10.26083/tuprints-00017625 · Cited by 1
Fruit Detection Using CenterNet
Zhao, K. (2021) · Tuwhera (Auckland University of Technology) · Cited by 1
Editorial: Probabilistic Perspectives on Brain (Dys)function
Parr, T., Marković, D., Ramstead, M. J. D., Smith, R. S., Hesp, C., & Friston, K. J. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.710179 · Cited by 1
Exponential Lower Bounds for Threshold Circuits of Sub-Linear Depth and EnergyPreprint
Uchizawa, K., & Abe, H. (2021) · arXiv (Cornell University) · doi:10.4230/lipics.mfcs.2023.85 · Cited by 1
Generating Treatment Plans for Prostate VMAT using Deep Learning
Peer, M. (2021) · reposiTUm (TU Wien) · doi:10.34726/hss.2021.83760 · Cited by 1
Controlling Recurrent Neural Networks by Diagonal ConceptorsPreprint
de Jong, J. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07968 · Cited by 1
Montelongo-Gonzalez, E. E. (2020) · doi:10.24275/uama.6732.7811 · Cited by 1
Dabane, G., Perrinet, L., & Daucé, E. (2021) · doi:10.36227/techrxiv.16550391.v1 · Cited by 1
Issues in Semantic Network Development and Utilization
Evans, S. (1997) · Computers and medicine · doi:10.1007/978-1-4612-1900-2_6 · Cited by 1
Quantum Error Correction using Quantum Convolutional Neural Network
Mishu, N. D. R., Mary, M. M., Ridwan, A. E. M., Meem, F. I., & Rahman, M. M. (2021) · BRAC University Institutional Repository (BRAC University) · doi:10.13140/rg.2.2.22596.76162 · Cited by 1
Real-time active control of a pneumatically actuated one-legged hopping robot.
Naik, K. (2006) · doi:10.82465/6198 · Cited by 1
Designing off-sample performance metrics.Preprint
Holland, M. J. (2021) · arXiv (Cornell University) · Cited by 1
Uniform Brackets, Containers, and Combinatorial Macbeath RegionsPreprint
Dutta, K., Ghosh, A., & Moran, S. (2021) · arXiv (Cornell University) · doi:10.4230/lipics.itcs.2022.59 · Cited by 1
Um estudo introdutório dos aspectos psicossociais da artificialização das inteligências
Nunes, L. J. D. D. L. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.47.2021.tde-21122021-120511 · Cited by 1
Nonparametric Models and Bayesian Networks. Applications to Anomaly Detection
González, D. A. (2021) · doi:10.20868/upm.thesis.69348 · Cited by 1
COVID-19 forecasting with deep learning: a distressing surveyPreprint
Gutierrez, L., de Medrano, R., & Aznarte, J. L. (2021) · doi:10.36227/techrxiv.16855189 · Cited by 1
How Violence Affects ProtestsPreprint
STEINERT-THRELKELD, Z., Joo, J., & Chan, A. (2019) · doi:10.33774/apsa-2019-bv6zd · Cited by 1
DEEP GENERATIVE MODELS FOR RESERVOIR DATA: AN APPLICATION IN SMART WELLS
Gurwicz, A. (2020) · doi:10.17771/pucrio.acad.48317 · Cited by 1
Comite de maquinas em predição de series temporais
Puma-Villanueva, W. J. (2006) · doi:10.47749/t/unicamp.2006.445702 · Cited by 1
Fast Quaternion Product Units for Learning Disentangled Representations in SO(3)Preprint
Qin, S., Zhang, X., Xu, H., & Xu, Y. (2022) · doi:10.36227/techrxiv.17791574.v1 · Cited by 1
Predição de crises epilépticas utilizando técnicas de Deep Learning
de Godoy, R. V. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.18.2021.tde-31012022-114038 · Cited by 1
Schulz, M. (2003) · Springer tracts in modern physics · doi:10.1007/0-387-21713-4_5 · Cited by 1
(2019) · Cambridge University Press eBooks · doi:10.1017/9781108644181.014 · Cited by 1
Zemanek, H. (1967) · Springer eBooks · doi:10.1007/978-3-662-00505-7_12 · Cited by 1
(2010) · Elsevier eBooks · doi:10.1016/b978-0-12-375070-9.00021-8 · Cited by 1
Bouveyron, C., Celeux, G., Murphy, T. B., & Raftery, A. E. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108644181.002 · Cited by 1
Schmidhuber, J. (2017) · Springer eBooks · doi:10.1007/978-1-4899-7687-1_909 · Cited by 1
(1993) · Elsevier eBooks · doi:10.1016/b978-0-08-051433-8.50029-x · Cited by 1
A self-supervised learning approach for astronomical images
Martinazzo, A. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.45.2021.tde-11012022-203357 · Cited by 1
Using Scratch to Teach Undergraduate Students Skills on AIPreprint
Apriliana, L. L., Guede, J. L., Estévez, J., & Graña, M. (2020) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/x24m6 · Cited by 1
Essays on Predictive Analytics in E-Commerce
Urbanke, P. (2016) · doi:10.53846/goediss-5801 · Cited by 1
Learning in Artificial Neural Networks
Reilly, R. G. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_332 · Cited by 1
Minería sobre grandes cantidades de datos
Moreno-Montiel, B. (2009) · doi:10.24275/uami.9593tv15x · Cited by 1
Deep Learning Methods for Motivic Pattern Extraction and Classification
Alvarez, A. A. (2021) · doi:10.20868/upm.thesis.66848 · Cited by 1
APPLICATION OF DEEP LEARNING FOR CYBERSECURITYPreprint
KUMAR, A. (2022) · doi:10.22541/au.164625896.62581020/v1 · Cited by 1
Using Computational Intelligence for solving the Ornstein-Zernike equationPreprint
Bedolla-Montiel, E. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.05089 · Cited by 1
A STUDY ON NEURAL NETWORKS FOR POKER PLAYING AGENTS
COSTA, A. M. (2019) · doi:10.17771/pucrio.acad.48011 · Cited by 1
Building Multiclass Classifiers for Remote Homology Detection and Fold Recognition
Rangwala, H., & Karypis, G. (2006) · Defense Technical Information Center (DTIC) · doi:10.21236/ada446086 · Cited by 1
Li, P., & Zhao, X. (2012) · Oxford University Press eBooks · doi:10.1093/obo/9780199772810-0010 · Cited by 1
Computer Science & Information Technology (CS & IT)
(2019) · doi:10.5121/csit.2019.90100 · Cited by 1
PREDICTING TRENDS IN THE STOCK MARKET
de Melo, J. P. F. (2018) · doi:10.17771/pucrio.acad.34653 · Cited by 1
(2012) · Elsevier eBooks · doi:10.1016/b978-0-12-381479-1.00015-0 · Cited by 1
Modeling Multivariate Time Series in Economics: from Auto-Regressions to Recurrent Neural NetworksPreprint
Verstyuk, S. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3294796 · Cited by 1
Dynamically structured holographic memory : a hybrid of discrete and distributed representation
Rutledge-Taylor, M. F. (2011) · doi:10.22215/etd/2011-09665 · Cited by 1
Auer, P. L. (2016) · Springer eBooks · doi:10.1007/978-1-4899-7502-7_618-1 · Cited by 1
It may be time to perfect the neuron of artificial neural networkPreprint
Liu, G. (2020) · doi:10.36227/techrxiv.12477266.v3 · Cited by 1
Munro, P. (2017) · Springer eBooks · doi:10.1007/978-1-4899-7687-1_51 · Cited by 1
An epigenetic modeling approach for adaptive prognostics of engineered systems
Seale, M. A., García‐Reyero, N., Salter, R., & Ruvinsky, A. I. (2021) · doi:10.21079/11681/41282 · Cited by 1
Neuromorphic Computing Based on Organic Memristive Systems
Erokhin, V. V. (2017) · Springer eBooks · doi:10.1007/978-3-642-27737-5_703-1 · Cited by 1
Vigo, R. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.04.007 · Cited by 1
Computers in behavioral science
(2007) · Systems Research and Behavioral Science · doi:10.1002/bs.3830050205 · Cited by 1
Dabane, G., Perrinet, L., & Daucé, E. (2021) · doi:10.36227/techrxiv.16550391 · Cited by 1
COMPREHENSIVE BIBLIOGRAPHY OF MACHINE LEARNING
(1983) · Elsevier eBooks · doi:10.1016/b978-0-08-051054-5.50021-2 · Cited by 1
Observadores inteligentes de estado
Berci, C. D. (2008) · doi:10.47749/t/unicamp.2008.431634 · Cited by 1
Neural Networks in Agrophysics
Lamorski, K. (2011) · Encyclopedia of earth sciences series/Encyclopedia of earth sciences · doi:10.1007/978-90-481-3585-1_97 · Cited by 1
Learning the neuron functions within neural networks based on genetic programming
Barton, A. (2009) · doi:10.22215/etd/2009-09227 · Cited by 1
On the complexity of Rocchio's similarity‐based relevance feedback algorithm
Chen, Z., & Fu, B. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20612 · Cited by 1
Computers in behavioral science
(2007) · Systems Research and Behavioral Science · doi:10.1002/bs.3830060311 · Cited by 1
A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered DataPreprint
Mohammad, N. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.06716 · Cited by 1
Redes neurais convolucionais como ferramenta de interpolação de dados sísmicos
Salgado, M. F. (2022) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.14.2022.tde-07072022-101006 · Cited by 1
Burgos, J. E. (2022) · Springer eBooks · doi:10.1007/978-3-319-55065-7_775 · Cited by 1
Rough set-based neuro-fuzzy system.
Ang, K. K. (2008) · doi:10.32657/10356/2487 · Cited by 1
Valdunciel, A. P. (2022) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.71544 · Cited by 1
Lizcano, J. A. M. (2015) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.39251 · Cited by 1
Signal, Image Processing and Embedded Systems Trends
(2022) · doi:10.5121/csit.2022.1220 · Cited by 1
Application of machine learning to gas flaringPreprint
Lu, R. (2023) · Thesis Commons (OSF Preprints) · doi:10.31237/osf.io/g6yvq · Cited by 1
Nametala, C. A. L. (2023) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.18.2023.tde-16032023-161345 · Cited by 1
Framework modular para detecção e desvio de objetos em veículos aéreos não tripulados.
de Almeida, V. M. (2022) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.3.2022.tde-04042023-085420 · Cited by 1
The Concept of Forward-Forward Learning Applied to a Multi Output PerceptronPreprint
Karlsson, K. F. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.03189 · Cited by 1
Santana, L. H. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/4qsut · Cited by 1
Realization of Multi-Valued Logic Using Optical Quantum Computing
Choe, S. (2023) · PDXScholar (Portland State University) · doi:10.15760/etd.8192 · Cited by 1
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