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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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
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 9,096
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084827 · Cited by 5,273
“Rosenblatt [2], [3] proposed a single-layer perceptron model by adding learning ability to the MP model.”
BackgroundDeep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Prediction, Learning, and Games
Cesa‐Bianchi, N., & Lugosi, G. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546921 · Cited by 3,338
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
1D convolutional neural networks and applications: A survey
Serkan Kiranyaz, B., Avcı, O., Abdeljaber, O., İnce, T., Gabbouj, M., & Inman, D. J. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107398 · Cited by 3,185
Supervised Sequence Labelling with Recurrent Neural Networks
Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120
Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115
Machine Learning for Fluid Mechanics
Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010719-060214 · Cited by 3,063
Solving Multiclass Learning Problems via Error-Correcting Output Codes
Dietterich, T. G., & Bakiri, G. (1995) · Journal of Artificial Intelligence Research · doi:10.1613/jair.105 · Cited by 2,739
Recent advances and applications of machine learning in solid-state materials science
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,786
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., & Haussler, D. H. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.10.906 · Cited by 2,457
Towards spike-based machine intelligence with neuromorphic computing
Roy, K., Jaiswal, A., & Panda, P. (2019) · Nature · doi:10.1038/s41586-019-1677-2 · Cited by 2,383
Catastrophic forgetting in connectionist networks
French, R. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01294-2 · Cited by 2,311
Rajaraman, A., & Ullman, J. D. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9781139058452 · Cited by 2,307
Simplifying Graph Convolutional NetworksPreprint
Wu, F., Zhang, T., Junior, A. H. D. S., Fifty, C., Yu, T., & Weinberger, K. Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07153 · Cited by 2,307
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
Stochastic Variational InferencePreprint
Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. W. (2012) · arXiv (Cornell University) · doi:10.5555/2567709.2502622 · Cited by 1,991
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and GaugesPreprint
Bronstein, M. M., Bruna, J., Cohen, T., & Veličković, P. (2021) · arXiv (Cornell University) · doi:10.4230/oasics.dx.2026.1 · Cited by 2,094
Advances in Large-Margin Classifiers
(2000) · The MIT Press eBooks · doi:10.7551/mitpress/1113.001.0001 · Cited by 1,910
Review of Deep Learning Algorithms and Architectures
Shrestha, A., & Mahmood, A. (2019) · IEEE Access · doi:10.1109/access.2019.2912200 · Cited by 1,883
“In 1957, Frank Rosenblatt created the perceptron, the first prototype of what we now know as a neural network [1].”
BackgroundDiscriminative training methods for hidden Markov models
Collins, M. (2002) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1118693.1118694 · Cited by 1,881
Neural networks for control systems—A survey
Hunt, K. J., Sbarbaro, D. G., Żbikowski, R., & Gawthrop, P. J. (1992) · Automatica · doi:10.1016/0005-1098(92)90053-i · Cited by 1,874
A Survey of the Usages of Deep Learning for Natural Language Processing
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836
How does the brain solve visual object recognition?
DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.010 · Cited by 1,790
Continual Learning Through Synaptic Intelligence
Zenke, F., Poole, B., & Ganguli, S. (2017) · arXiv · doi:10.48550/arxiv.1703.04200 · Cited by 1,784
Trends in extreme learning machines: A review
Huang, G. M., Huang, G.-B., Song, S., & You, K. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.10.001 · Cited by 1,759
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S. K. E., Sidike, P., Nasrin, M. S., Hasan, M., Essen, B. C. V., Awwal, A. A. S., & Asari, V. K. (2019) · Electronics · doi:10.3390/electronics8030292 · Cited by 1,722
Online Passive-Aggressive Algorithms
Crammer, K., Dekel, O., Keshet, J., Shalev‐Shwartz, S., & Singer, Y. (2003) · Cited by 1,682
Awad, M., & Khanna, R. (2015) · Apress eBooks · doi:10.1007/978-1-4302-5990-9 · Cited by 1,625
GAUSSIAN PROCESSES FOR MACHINE LEARNING
Seeger, M. (2004) · International Journal of Neural Systems · doi:10.1142/s0129065704001899 · Cited by 1,597
Deep Learning Applications in Medical Image Analysis
Ker, J., Wang, L., Rao, J. P., & Lim, C. C. T. (2017) · IEEE Access · doi:10.1109/access.2017.2788044 · Cited by 1,574
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557
Deep learning-based electroencephalography analysis: a systematic review
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532
Pouyanfar, S., Sadiq, S. B., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., Shyu, M., Chen, S., & Iyengar, S. S. (2018) · ACM Computing Surveys · doi:10.1145/3234150 · Cited by 1,510
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477
“Note that, in numerous scientific applications, it has been most common so far to use linear models [151], favoring interpretability often at the expense of predictivity (see [63] and [117]).”
BackgroundDeep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473
Some studies in machine learning using the game of checkers
Samuel, A. L. (2000) · IBM Journal of Research and Development · doi:10.1147/rd.441.0206 · Cited by 1,439
An introduction to quantum machine learning
Schuld, M., Sinayskiy, I., & Petruccione, F. (2015) · Contemporary Physics · doi:10.1080/00107514.2014.964942 · Cited by 1,381
The History Began from AlexNet: A Comprehensive Survey on Deep Learning ApproachesPreprint
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S. K. E., Sidike, P., Nasrin, M. S., Essen, B. C. V., Awwal, A. A. S., & Asari, V. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.01164 · Cited by 1,380
Online Learning and Online Convex Optimization
Shalev‐Shwartz, S. (2012) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000018 · Cited by 1,364
Connectionist Speech Recognition
Bourlard, H. A., & Morgan, N. H. (1994) · Springer eBooks · doi:10.1007/978-1-4615-3210-1 · Cited by 1,336
Materials discovery and design using machine learning
Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276
Growing cell structures—A self-organizing network for unsupervised and supervised learning
Fritzke, B. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90091-4 · Cited by 1,268
Progress in supervised neural networks
Hush, D. R., & Horne, B. G. (1993) · IEEE Signal Processing Magazine · doi:10.1109/79.180705 · Cited by 1,254
Guo, M.-H., Lu, C.-Z., Liu, Z.-N., Cheng, M., & Hu, S. (2023) · Computational Visual Media · doi:10.1007/s41095-023-0364-2 · Cited by 1,235
An introduction to neural computing
Kohonen, T. (1988) · Neural Networks · doi:10.1016/0893-6080(88)90020-2 · Cited by 1,212
Artificial intelligence in cardiology
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202
The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays
Li, Z., Askim, J. R., & Suslick, K. S. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00226 · Cited by 1,200
sdm: a reproducible and extensible R platform for species distribution modelling
Naimi, B., & Araújo, M. B. (2016) · Ecography · doi:10.1111/ecog.01881 · Cited by 1,193
Recent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
“Originally inspired by biological models of computation and cognition in the human brain [23, 24], one of DL’s major strengths is its potential to extract higher-level features from the raw input data.”
BackgroundOnline Learning and Online Convex Optimization
Shalev‐Shwartz, S. (2011) · now publishers, Inc. eBooks · doi:10.1561/9781601985477 · Cited by 1,171
Organic electronics for neuromorphic computing
van de Burgt, Y., Melianas, A., Keene, S. T., Malliaras, G. G., & Salleo, A. (2018) · Nature Electronics · doi:10.1038/s41928-018-0103-3 · Cited by 1,146
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122
Connectionist Speech Recognition: A Hybrid Approach
Bourlard, H. A., & Morgan, N. H. (1993) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,115
Deep Learning: A Primer for Radiologists
Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., Kadoury, S., & Tang, A. (2017) · RadioGraphics · doi:10.1148/rg.2017170077 · Cited by 1,115
A review on time series forecasting techniques for building energy consumption
Deb, C., Zhang, F., Yang, J., Lee, S. E., & Shah, K. W. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.02.085 · Cited by 1,094
Kriegeskorte, N. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035447 · Cited by 1,093
State of the art and practice in AI in education
Holmes, W., & Tuomi, I. (2022) · European Journal of Education · doi:10.1111/ejed.12533 · Cited by 1,093
Large margin classification using the perceptron algorithm
Freund, Y., & Schapire, R. E. (1998) · Conference on Learning Theory · doi:10.1145/279943.279985 · Cited by 1,091
Training Spiking Neural Networks Using Lessons From Deep Learning
Eshraghian, J. K., Ward, M., Neftci, E., Wang, X., Lenz, G., Dwivedi, G., Bennamoun, M., Jeong, D. S., & Lu, W. D. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3308088 · Cited by 1,106
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,079
Experimental demonstration of associative memory with memristive neural networks
Pershin, Y. V., & Di Ventra, M. (2010) · Neural Networks · doi:10.1016/j.neunet.2010.05.001 · Cited by 1,041
Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
Cossarizza, A., Chang, H., Radbruch, A., Acs, A., Adam, D., Adam‐Klages, S., Agace, W. W., Aghaeepour, N., Akdiş, M., Allez, M., et al. (2019) · European Journal of Immunology · doi:10.1002/eji.201970107 · Cited by 1,007
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012
Fast Binary Feature Selection with Conditional Mutual Information
Fleuret, F. (2004) · doi:10.5555/1005332.1044711 · Cited by 986
Neural networks and their applications
Bishop, C. M. (1994) · Review of Scientific Instruments · doi:10.1063/1.1144830 · Cited by 981
Deep Reinforcement Learning: A Survey
Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., Dai, B., & Miao, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3207346 · Cited by 972
“The perceptron [68] is the earliest neural network prototype known as a single-layer neural network (no hidden layers).”
BackgroundData Mining: Concepts and Techniques (2nd edition)
Han, J., & Kamber, M. (2006) · Cited by 968
Machine Learning Approaches for Clinical Psychology and Psychiatry
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966
An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels
Huang, G.-B. (2014) · Cognitive Computation · doi:10.1007/s12559-014-9255-2 · Cited by 965
3-D Deep Learning Approach for Remote Sensing Image Classification
Hamida, A. B., Benoît, A., Lambert, P., & Amar, C. B. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2818945 · Cited by 950
State-dependent computations: spatiotemporal processing in cortical networks
Buonomano, D. V., & Maass, W. (2009) · Nature reviews. Neuroscience · doi:10.1038/nrn2558 · Cited by 950
Neural Networks and Deep Learning
Theodoridis, S. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801522-3.00018-5 · Cited by 942
Deep Learning for Audio Signal Processing
Purwins, H., Li, B., Virtanen, T. I., Schlüter, J., Chang, S.-Y., & Sainath, T. N. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2908700 · Cited by 929
Econometrics and Machine Learning
Arthur Charpentier, A., Flachaire, E., & Ly, A. (2019) · Economie et Statistique / Economics and Statistics · doi:10.24187/ecostat.2018.505d.1970 · Cited by 928
Elements of Artificial Neural Networks
Mehrotra, K. G., Mohan, C. K., & Ranka, S. (1996) · The MIT Press eBooks · doi:10.7551/mitpress/2687.001.0001 · Cited by 921
Naqa, I. E., & Murphy, M. J. (2015) · Springer eBooks · doi:10.1007/978-3-319-18305-3_1 · Cited by 913
Online learning: A comprehensive survey
Hoi, S. C. H., Sahoo, D., Lu, J., & Zhao, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.112 · Cited by 913
Face classification using electronic synapses
Yao, P., Wu, H., Gao, B., Eryilmaz, S. B., Huang, X., Zhang, W., Zhang, Q., Deng, N., Shi, L., Wong, H.-S. P., et al. (2017) · Nature Communications · doi:10.1038/ncomms15199 · Cited by 909
Neuromorphic nanoelectronic materials
Sangwan, V. K., & Hersam, M. C. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0647-z · Cited by 907
Artificial neural networks as a tool in ecological modelling, an introduction
Lek, S., & Guégan, J. (1999) · Ecological Modelling · doi:10.1016/s0304-3800(99)00092-7 · Cited by 901
Raschka, S., Patterson, J. M., & Nolet, C. (2020) · Information · doi:10.3390/info11040193 · Cited by 928
On the use of deep learning for computational imaging
Barbastathis, G., Özcan, A., & Situ, G. (2019) · Optica · doi:10.1364/optica.6.000921 · Cited by 896
Review of Image Classification Algorithms Based on Convolutional Neural Networks
Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., & Miao, Y. (2021) · Remote Sensing · doi:10.3390/rs13224712 · Cited by 893
The functional architecture of the ventral temporal cortex and its role in categorization
Kalanit Grill-spector, A., & Weiner, K. S. (2014) · Nature reviews. Neuroscience · doi:10.1038/nrn3747 · Cited by 891
Machine Learning for Survival Analysis
Wang, P., Li, Y., & Reddy, C. K. A. (2019) · ACM Computing Surveys · doi:10.1145/3214306 · Cited by 858
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Learning and memorization of classifications.
Shepard, R. N., Hovland, C. I., & Jenkins, H. M. (1961) · The Psychological Monographs · doi:10.1037/h0093825 · Cited by 844
Foundations of neural networks, fuzzy systems, and knowledge engineering
Kasabov, N. K. (1997) · Choice Reviews Online · doi:10.5860/choice.35-0330 · Cited by 821
A Comprehensive Survey of Loss Functions in Machine Learning
Wang, Q., Ma, Y., Zhao, K., & Tian, Y. (2020) · Annals of Data Science · doi:10.1007/s40745-020-00253-5 · Cited by 818
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.11.018 · Cited by 809
From the neuron doctrine to neural networks
Yuste, R. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn3962 · Cited by 802
SUSTAIN: A Network Model of Category Learning.
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004) · Psychological Review · doi:10.1037/0033-295x.111.2.309 · Cited by 789
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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