Cited by 13,333Neural Networks and ApplicationsMemory and Neural MechanismsNeural dynamics and brain function

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.

Rosenblatt, F.·1958·Psychological Review

Open this paper in CoChat — free account

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

Integrity signals

Registry

Crossref · 10.1037/h0042519

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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
The Organization of Behavior
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
Untitled work
(1952) · Medical Entomology and Zoology · Cited by 1,436
The Sensory Order.
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
The cell assembly: Mark II.
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
Why the Mind is in the Head?
McCulloch, W. S. (1950) · dialectica · doi:10.1111/j.1746-8361.1950.tb01019.x · Cited by 73

Flagged first, then by citations

Show all 15 references

1 of the 15 references couldn’t be matched to a known paper.

Sorted by citations

Items 1,801–1,900 of 13,333

Study of deep learning techniques for medical image analysis: A review
Singhal, A., Phogat, M., Kumar, D., Kumar, A., Dahiya, M., & Shrivastava, V. K. (2022) · Materials Today Proceedings · doi:10.1016/j.matpr.2022.01.071 · Cited by 45
GSPHI: A novel deep learning model for predicting phage-host interactions via multiple biological information
Pan, J., You, W., Lü, X., Wang, S., You, Z., & Sun, Y. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.06.014 · Cited by 45
A comparison between machine and deep learning models on high stationarity data
Santoro, D., Ciano, T., & Феррара, М. (2024) · Scientific Reports · doi:10.1038/s41598-024-70341-6 · Cited by 45
G-Twin: Graph neural network-based digital twin for real-time and high-fidelity structural health monitoring for offshore wind turbines
Jiang, C., & Chen, N.-Z. (2025) · Marine Structures · doi:10.1016/j.marstruc.2025.103813 · Cited by 45
Machine Learning and Artificial Intelligence Applications in Soil Science
Minasny, B., & McBratney, A. B. (2025) · European Journal of Soil Science · doi:10.1111/ejss.70093 · Cited by 45
A review of time series forecasting and spatio-temporal series forecasting in deep learning
Yu, Q., Yang, G., Wang, X., Shi, Y., Feng, Y., & Liu, A. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07632-w · Cited by 45
Machine learning in burn care and research: A systematic review of the literature
Liu, N. T., & Salinas, J. (2015) · Burns · doi:10.1016/j.burns.2015.07.001 · Cited by 45
Multiple-domain manifold for feature extraction in machinery fault diagnosis
Gan, M., Wang, C., & Zhu, C. (2015) · Measurement · doi:10.1016/j.measurement.2015.07.042 · Cited by 45
Image interpretation-guided supervised classification using nested segmentation
Egorov, A., Hansen, M. C., Roy, D. P., Kommareddy, A. R., & Potapov, P. V. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.04.022 · Cited by 45
Neuromorphic behavior in percolating nanoparticle films
Fostner, S., & Brown, S. A. (2015) · Physical Review E · doi:10.1103/physreve.92.052134 · Cited by 45
A new reliability-based data-driven approach for noisy experimental data with physical constraints
Ayensa-Jiménez, J., Doweidar, M. H., Sanz‐Herrera, J. A., & Doblaré, M. (2017) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2017.08.027 · Cited by 45
Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity
Öztoprak, H., Toycan, M., Kemal, Y., Arıkan, O., Doğutepe, E., & Karakaş, S. (2017) · Clinical Neurophysiology · doi:10.1016/j.clinph.2017.09.105 · Cited by 45
A review of sparsity-based clustering methods
Oktar, Y., & Türkan, M. (2018) · Signal Processing · doi:10.1016/j.sigpro.2018.02.010 · Cited by 45
Green Computing in Sensors-Enabled Internet of Things: Neuro Fuzzy Logic-Based Load Balancing
Kashyap, P. K., Kumar, S., Dohare, U., Kumar, V., & Kharel, R. (2019) · Electronics · doi:10.3390/electronics8040384 · Cited by 45
Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach
Dhanraj, J. A., & Sugumaran, V. (2019) · Structural durability & health monitoring · doi:10.32604/sdhm.2019.00287 · Cited by 45
Chemists: AI Is Here; Unite To Get the Benefits
Griffen, E., Dossetter, A. G., & Leach, A. G. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c00163 · Cited by 45
Multi-task deep learning of near infrared spectra for improved grain quality trait predictions
Assadzadeh, S., Walker, C. J., McDonald, L. S., Maharjan, P., & Panozzo, J. (2020) · Journal of Near Infrared Spectroscopy · doi:10.1177/0967033520939318 · Cited by 45
Precision medicine and artificial intelligence: overview and relevance to reproductive medicine
Hajirasouliha, I., & Elemento, O. (2020) · Fertility and Sterility · doi:10.1016/j.fertnstert.2020.09.156 · Cited by 45
Optimized ANN model for predicting rock mass quality ahead of tunnel face using measure-while-drilling data
Liu, J., Jiang, Y., Han, W., & Sakaguchi, O. (2021) · Bulletin of Engineering Geology and the Environment · doi:10.1007/s10064-020-02057-6 · Cited by 45
ChemPix: automated recognition of hand-drawn hydrocarbon structures using deep learning
Weir, H., Thompson, K. C., Woodward, A., Choi, B., Braun, A., & Martı́nez, T. J. (2021) · Chemical Science · doi:10.1039/d1sc02957f · Cited by 45
Hydrogen bonded frameworks: smart materials used smartly
Yusov, A., Dillon, A. M., & Ward, M. D. (2021) · Molecular Systems Design & Engineering · doi:10.1039/d1me00055a · Cited by 45
Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach
Araújo, A. L. D., da Silva, V. M., Kudo, M. S., de Souza, E. S. C., Saldivia‐Siracusa, C., Giraldo‐Roldán, D., Lopes, M. A., Vargas, P. A., Khurram, S. A., Pearson, A. T., et al. (2023) · Journal of Oral Pathology and Medicine · doi:10.1111/jop.13397 · Cited by 45
Functionality in neural nets
Valiant, L. G. (1988) · Conference on Learning Theory · doi:10.5555/93025.93038 · Cited by 45
Online Ranking by Projecting
Crammer, K., & Singer, Y. (2004) · Neural Computation · doi:10.1162/0899766052530848 · Cited by 45
Symbolic and Sub-Symbolic Representations in Computational Models of Human Cognition
Kelley, T. D. (2003) · Theory & Psychology · doi:10.1177/0959354303136005 · Cited by 45
Higher-Order Neural Networks in Petroleum Engineering
Kumoluyi, A. O., & Daltaban, T. S. (1994) · SPE Western Regional Meeting · doi:10.2118/27905-ms · Cited by 45
Constructive Neural Network Learning Algorithms for Multi-Category Real-Valued Pattern Classification
Parekh, R., Yang, J., & Honavar, V. (1997) · Iowa State University Digital Repository (Iowa State University) · Cited by 45
Learning with Fenchel-Young LossesPreprint
Blondel, M., Martins, A. F. T., & Niculae, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.02324 · Cited by 45
Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM)
Sing, T. F., Yang, J. J., & Yu, S. M. (2021) · The Journal of Real Estate Finance and Economics · doi:10.1007/s11146-021-09861-1 · Cited by 44
A Brief Review on Deep Learning Applications in Genomic Studies
Shen, X., Jiang, C., Wen, Y., Li, C., & Lu, Q. L. (2022) · Frontiers in Systems Biology · doi:10.3389/fsysb.2022.877717 · Cited by 44
A Pedestrian Detection Network Model Based on Improved YOLOv5
Li, M.-L., Sun, G., & Yu, J.-X. (2023) · Entropy · doi:10.3390/e25020381 · Cited by 44
Artificial intelligence in the neonatal intensive care unit: the time is now
Beam, K., Sharma, P. K., Levy, P. T., & Beam, A. L. (2023) · Journal of Perinatology · doi:10.1038/s41372-023-01719-z · Cited by 44
Artificial Intelligence for Sign Language Translation – A Design Science Research Study
Strobel, G., Schoormann, T., Banh, L., Möller, F., & TU Braunschweig/Fraunhofer ISST, G. (2023) · Communications of the Association for Information Systems · doi:10.17705/1cais.05303 · Cited by 44
A digital twin approach for maritime carbon intensity evaluation accounting for operational and environmental uncertainty
Vasilikis, N., Geertsma, R., & Coraddu, A. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.115927 · Cited by 44
Machine learning small molecule properties in drug discovery
Schapin, N., Majewski, M., Varela‐Rial, A., Arróniz, C., & Fabritiis, G. D. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100020 · Cited by 44
Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings
Rabi, M., Jweihan, Y. S., Abarkan, I., Ferreira, F. P. V., Shamass, R., Limbachiya, V., Tsavdaridis, K. D., & Santos, L. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.101749 · Cited by 44
Multi-Layer Perceptron-Based Classification with Application to Outlier Detection in Saudi Arabia Stock Returns
Rashedi, K. A., Ismail, M. T., Wadi, S. A., Serroukh, A., Alshammari, T. S., & Jaber, J. J. (2024) · Journal of risk and financial management · doi:10.3390/jrfm17020069 · Cited by 44
Application of artificial neural network to multiphase flow metering: A review
Bahrami, S., Alamdari, S., Farajmashaei, M., Behbahani, M., Jamshidi, S., & Bahrami, B. (2024) · Flow Measurement and Instrumentation · doi:10.1016/j.flowmeasinst.2024.102601 · Cited by 44
Engineering Systems Which Generate Emergent Functionalities
Gleizes, M.-P., Camps, V., Georgé, J., & Capera, D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85029-8_5 · Cited by 44
A Structural Impairment Detection System Using Competitive Arrays of Artificial Neural Networks
STORY, B. A., & Fry, G. T. (2013) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12040 · Cited by 44
Market orientation and performance: modelling a neural network
Silva, M. M. P., Moutinho, L., Coelho, A., & Marques, A. (2009) · European Journal of Marketing · doi:10.1108/03090560910935505 · Cited by 44
Artificial Intelligence for Lithology Identification through Real-Time Drilling Data
Ali, A. M. M. (2015) · Journal of Earth Science & Climatic Change · doi:10.4172/2157-7617.1000265 · Cited by 44
Hybrid Model for Forecasting of Changes in Land Use and Land Cover Using Satellite Techniques
Márquez, A., Pérez, E. G., & Rey, D. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2885612 · Cited by 44
Machine learning for predicting landslide risk of Rohingya refugee camp infrastructure
Ahmed, N., Firoze, A., & Rahman, R. M. (2020) · Journal of Information and Telecommunication · doi:10.1080/24751839.2019.1704114 · Cited by 44
Deep Learning–Based Prediction of Refractive Error Using Photorefraction Images Captured by a Smartphone: Model Development and Validation Study
Chun, J., Kim, Y., Shin, K. Y., Han, S. H., Oh, S. Y., Chung, T., Park, K., & Lim, D. H. (2020) · JMIR Medical Informatics · doi:10.2196/16225 · Cited by 44
Robotics, AI, and Humanity
von Braun, J., Archer, M. S., Reichberg, G. M., & Sorondo, M. S. (2021) · Springer eBooks · doi:10.1007/978-3-030-54173-6 · Cited by 44
Trip Based Modeling of Fuel Consumption in Modern Heavy-Duty Vehicles Using Artificial Intelligence
Katreddi, S., & Thiruvengadam, A. (2021) · Energies · doi:10.3390/en14248592 · Cited by 44
Machine Learning for Improving Surface-Layer-Flux Estimates
McCandless, T. C., Gagne, D. J., Kosović, B., Haupt, S. E., Yang, B., Becker, C., & Schreck, J. S. (2022) · Boundary-Layer Meteorology · doi:10.1007/s10546-022-00727-4 · Cited by 44
Feed‐Forward Deep Neural Network (FFDNN)‐Based Deep Features for Static Malware Detection
Singh, P., Borgohain, S. K., Sarkar, A. K., Kumar, J., & Sharma, L. D. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/9544481 · Cited by 44
Introduction to Fuzzy Systems, Neural Networks, and Genetic Algorithms
Takagi, H. (1997) · Springer eBooks · doi:10.1007/978-1-4615-6191-0_1 · Cited by 44
Evolutionary programming: an introduction and some current directions
Fogel, D. B. (1994) · Statistics and Computing · doi:10.1007/bf00175356 · Cited by 44
Overview of hybrid optical neural networks
Jutamulia, S., & Yu, F. T. S. (1996) · Optics & Laser Technology · doi:10.1016/0030-3992(95)00070-4 · Cited by 44
Simultaneous kinetic-spectrophotometric determination of maltol and ethyl maltol in food samples by using chemometrics
Ni, Y., Wang, Y., & Serge Kokot, A. (2008) · Food Chemistry · doi:10.1016/j.foodchem.2007.12.036 · Cited by 44
A brief history of applied cognitive psychology
Hoffman, R. R., & Deffenbacher, K. A. (1992) · Applied Cognitive Psychology · doi:10.1002/acp.2350060102 · Cited by 45
The Use of a Bayesian Neural Network Model for Classification Tasks
Holst, A. G. (1997) · Cited by 44
Neural Network Modeling of Developmental Effects in Discrimination Shifts
Sirois, S., & Shultz, T. R. (1998) · Journal of Experimental Child Psychology · doi:10.1006/jecp.1998.2474 · Cited by 44
"Pattern recognition" computers as models for form perception.
Uhr, L. (1963) · Psychological Bulletin · doi:10.1037/h0048029 · Cited by 44
Distribution-Independent PAC Learning of Halfspaces with Massart NoisePreprint
Diakonikolas, I., Gouleakis, T., & Tzamos, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.10075 · Cited by 44
Speech Perception within a Biologically Realistic Information-Theoretic Framework
Kluender, K. R., & Kiefte, M. (2006) · Elsevier eBooks · doi:10.1016/b978-012369374-7/50007-9 · Cited by 43
Efficient dendritic learning as an alternative to synaptic plasticity hypothesis
Hodassman, S., Vardi, R., Tugendhaft, Y., Goldental, A., & Kanter, I. (2022) · Scientific Reports · doi:10.1038/s41598-022-10466-8 · Cited by 43
Deep learning-based feature extraction for prediction and interpretation of sharp-wave ripples in the rodent hippocampus
Navas-Olivé, A., Amaducci, R., Jurado-Parras, M.-T., Sebastian, E. R., & de la Prida, L. M. (2022) · eLife · doi:10.7554/elife.77772 · Cited by 43
Leveraging physiology and artificial intelligence to deliver advancements in health care
Zhang, A., Wu, Z., Wu, E., Wu, M. A., Snyder, M. P., Zou, J., & Wu, J. C. (2023) · Physiological Reviews · doi:10.1152/physrev.00033.2022 · Cited by 44
Brain-inspired artificial intelligence research: A review
Wang, G., Bao, H., Liu, Q., Zhou, T., Wu, S., Huang, T., Yu, Z., Lu, C., Gong, Y., Zhang, Z., et al. (2024) · Science China Technological Sciences · doi:10.1007/s11431-024-2732-9 · Cited by 43
Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method
Xiang, X., Guo, S., Cui, Z., Wang, L., & Xu, C. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131867 · Cited by 43
Hybrid deep learning model for accurate cost and schedule estimation in construction projects using sequential and non-sequential data
Cheng, M., Vu, Q.-T., & Gosal, F. E. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105904 · Cited by 43
Nanomedicines Targeting Metabolic Pathways in the Tumor Microenvironment: Future Perspectives and the Role of AI
Fan, S., Wang, W., Che, W., Xu, Y., Jin, C., Dong, L., & Xia, Q. (2025) · Metabolites · doi:10.3390/metabo15030201 · Cited by 43
Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures
Papillon, M., Sanborn, S., Mathe, J., Cornelis, L., Bertics, A., Buracas, D., Lillemark, H. J., Shewmake, C., Dinç, F., Xavier Pennec, B., et al. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/adf375 · Cited by 44
Structured light meets machine intelligence
Zhang, Z., Kong, L., Zhang, L., Pan, X., Das, T., Wang, B., Liu, B., Wang, F., Nape, I. M., Shen, Y., et al. (2025) · eLight · doi:10.1186/s43593-025-00102-z · Cited by 43
Advanced Structured Prediction
Nowozin, S. (2014) · The MIT Press eBooks · doi:10.7551/mitpress/9969.001.0001 · Cited by 43
Estimation of Surface Solar Radiation with Artificial Neural Networks
Tymvios, F. S., Michaelides, S. C., & Skouteli, C. S. (2008) · Springer eBooks · doi:10.1007/978-3-540-77455-6_9 · Cited by 43
Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory
Cantwell, G. T., Crossley, M. J., & Ashby, F. G. (2015) · Psychonomic Bulletin & Review · doi:10.3758/s13423-015-0827-2 · Cited by 43
A computational paradigm for dynamic logic-gates in neuronal activity
Goldental, A. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00052 · Cited by 43
A computational developmental model for specificity and transfer in perceptual learning
Solgi, M., Liu, T., & Weng, J. (2013) · Journal of Vision · doi:10.1167/13.1.7 · Cited by 43
Review of optimization of power dispatch in renewable energy system
Gandhi, O., Gallegos, C. D. R., & Srinivasan, D. (2016) · IEEE Innovative Smart Grid Technologies-Asia · doi:10.1109/isgt-asia.2016.7796394 · Cited by 43
Driver identification in intelligent vehicle systems using machine learning algorithms
Li, Z., Zhang, K., Chen, B., Dong, Y., & Zhang, L. (2018) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2017.0254 · Cited by 43
Artificial Intelligence, Automation, and Social Welfare: Some Ethical and Historical Perspectives on Technological Overstatement and Hyperbole
Oravec, J. A. (2018) · Ethics and Social Welfare · doi:10.1080/17496535.2018.1512142 · Cited by 43
On Improving the accuracy with Auto-Encoder on Conjunctivitis
Li, W., Liu, X., Liu, J., Chen, P., Wan, S., & Cui, X. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105489 · Cited by 43
A machine-learning-enhanced hierarchical multiscale method for bridging from molecular dynamics to continua
Xiao, S., Hu, R., Li, Z., Attarian, S., Björk, K.-M., & Lendasse, A. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04480-7 · Cited by 43
The Unbearable Shallow Understanding of Deep Learning
Plebe, A., & Grasso, G. M. (2019) · Minds and Machines · doi:10.1007/s11023-019-09512-8 · Cited by 43
Design of Feedforward Neural Networks in the Classification of Hyperspectral Imagery Using Superstructural Optimization
Sildir, H., Aydın, E., & Kavzoğlu, T. (2020) · Remote Sensing · doi:10.3390/rs12060956 · Cited by 43
Memristive Devices for Neuromorphic Applications: Comparative Analysis
Erokhin, V. V. (2020) · BioNanoScience · doi:10.1007/s12668-020-00795-1 · Cited by 43
Deep learning for camera data acquisition, control, and image estimation
Brady, D. J., Fang, L., & Ma, Z. (2020) · Advances in Optics and Photonics · doi:10.1364/aop.398263 · Cited by 43
The Future of Computational Linguistics: On Beyond Alchemy
Church, K., & Liberman, M. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.625341 · Cited by 43
Structural mechanism-based intelligent capacity prediction methods for concrete-encased CFST columns
Zhou, X., Hou, C., Peng, J., Yao, G.-H., & Fang, Z. (2023) · Journal of Constructional Steel Research · doi:10.1016/j.jcsr.2022.107769 · Cited by 43
Computational Applications Of Noise Sensitivity
O’Donnell, R. (2018) · KiltHub Repository · doi:10.1184/r1/6604301 · Cited by 43
An Artificial Neural-network Based Predictive Model for Pre-evacuation Human Response in Domestic Building Fire
Lo, S., Liu, M., Zhang, P. H., & Yuen, R. K. K. (2008) · Fire Technology · doi:10.1007/s10694-008-0064-6 · Cited by 43
Stability of the fittest: organizing learning through retroaxonal signals
Harris, K. D. (2008) · Trends in Neurosciences · doi:10.1016/j.tins.2007.12.002 · Cited by 43
Autonomous Self-assembly in a Swarm-bot
Groß, R., Michael Bonani, C., Francesco Mondada, C., & Dorigo, M. (2005) · Springer eBooks · doi:10.1007/3-540-29344-2_47 · Cited by 43
Beyond Turing Machines.
Eberbach, E., & Wegner, P. W. (2003) · Cited by 43
Development of Concrete Bridge Rating Prototype Expert System with Machine Learning
Kushida, M., Miyamoto, A., & Kinoshita, K. (1997) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)0887-3801(1997)11:4(238 · Cited by 43
Philosophy of Linguistics
Scholz, B. C. (2005) · Wiley eBooks · doi:10.1002/0470018860.s00142 · Cited by 43
Online adaptation strategies for statistical machine translation in post-editing scenarios
Martínez-Gómez, P., Sanchis-Trilles, G., & Casacuberta, F. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.01.011 · Cited by 42
Review of Algorithms for Artificial Intelligence on Low Memory Devices
Radanliev, P., & Roure, D. D. (2021) · IEEE Access · doi:10.1109/access.2021.3101579 · Cited by 42
Artificial neural network based fatigue life assessment of friction stir welding AA2024-T351 aluminum alloy and multi-objective optimization of welding parameters
Nejad, R. M., Sina, N., Moghadam, D. G., Branco, R., Macek, W., & Berto, F. (2022) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2022.106840 · Cited by 42
A Quantum Generative Adversarial Network-based Intrusion Detection System
Rahman, M. A., Shahriar, H., Clincy, V. A., Hossain, M. F., & Rahman, M. (2023) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00280 · Cited by 42
Forecasting electricity production from various energy sources in Türkiye: A predictive analysis of time series, deep learning, and hybrid models
Gülay, E., Sen, M., & Akgun, O. B. (2023) · Energy · doi:10.1016/j.energy.2023.129566 · Cited by 42
Robust Training of Artificial Feedforward Neural Networks
El-Melegy, M., Essai, M. H., & Amer, A. A. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-01082-8_9 · Cited by 42
An Adaptive Gradient Method for Online AUC Maximization
Ding, Y., Zhao, P., Hoi, S. C. H., & Ong, Y.-S. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9577 · Cited by 42
A Survey: Neural Network-Based Deep Learning for Acoustic Event Detection
Xia, X., Togneri, R. B., Sohel, F. A., Zhao, Y., & Huang, D. D. (2019) · Circuits Systems and Signal Processing · doi:10.1007/s00034-019-01094-1 · Cited by 42
Digit-tracking as a new tactile interface for visual perception analysis
Lio, G., Fadda, R., Doneddu, G., Duhamel, J., & Sirigu, A. (2019) · Nature Communications · doi:10.1038/s41467-019-13285-0 · Cited by 42

Authors: Rosenblatt, F.
Corresponding author: Rosenblatt, F.
Venue: Psychological Review
Publisher: American Psychological Association (APA)
Published: 1958
Volume: 65
Issue: 6
Pages: 386-408
ISSN: 0033-295X (ISSN-L)
1939-1471
DOI: 10.1037/h0042519
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

The perceptron: A probabilistic model for information storage and organization in the brain.

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cites

Cited by

Related

Metadata

Record

About these checks