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 901–1,000 of 13,333
Wang, H., Mao, K., Yuan, Z., Shi, J., Cao, M., Qin, Z., Duan, S., & Tang, B. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112665 · Cited by 100
Segmentation and classification on chest radiography: a systematic survey
Agrawal, T., & Choudhary, P. (2022) · The Visual Computer · doi:10.1007/s00371-021-02352-7 · Cited by 100
Lipschitz Certificates for Layered Network Structures Driven by Averaged Activation Operators
Combettes, P. L., & Pesquet, J.-C. (2020) · SIAM Journal on Mathematics of Data Science · doi:10.1137/19m1272780 · Cited by 100
The decline of computers as a general purpose technology
Thompson, N., & Spanuth, S. (2021) · Communications of the ACM · doi:10.1145/3430936 · Cited by 100
Braun, G., Carderera, A., Combettes, C. W., Hassani, H., Karbasi, A., Mokhtari, A., & Pokutta, S. (2025) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611978568 · Cited by 100
Artificial intelligence for microscopy: what you should know
von Chamier, L., Laine, R. F., & Henriques, R. (2019) · Biochemical Society Transactions · doi:10.1042/bst20180391 · Cited by 99
Bourquin, J., Schmidli, H., van Hoogevest, P., & Leuenberger, H. (1997) · Pharmaceutical Development and Technology · doi:10.3109/10837459709022615 · Cited by 99
Khan, R., Rehman, N. U., Iqbal, S., Abdullaev, S. S., & Aldosari, H. M. (2023) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c01323 · Cited by 99
The Human Brain Project and neuromorphic computing.
Calimera, A., Macii, E., & Poncino, M. (2013) · Europe PMC (PubMed Central) · doi:10.11138/fneur/2013.28.3.191 · Cited by 99
Prompt Engineering in Healthcare
Patil, R., Heston, T. F., & Bhuse, V. (2024) · Electronics · doi:10.3390/electronics13152961 · Cited by 99
“[3] identifies how a system consisting of randomly connected units can learn to associate or generate responses to specific input stimuli.”
Neural Networks – State of Art, Brief History, Basic Models and Architecture
Macukow, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45378-1_1 · Cited by 99
Is there a role for statistics in artificial intelligence?
Friedrich, S., Antes, G., Behr, S., Binder, H., Brannath, W., Dumpert, F., Ickstadt, K., Kestler, H. A., Lederer, J., Leitgöb, H., et al. (2021) · Advances in Data Analysis and Classification · doi:10.1007/s11634-021-00455-6 · Cited by 99
Deep learning for financial forecasting: A review of recent trends
Giantsidi, S., & Tarantola, C. (2025) · International Review of Economics & Finance · doi:10.1016/j.iref.2025.104719 · Cited by 99
Predicting Outcomes After Liver Transplantation A Connectionist Approach
Doyle, H. R., Dvorchik, I., Mitchell, S., Marino, I. R., Ebert, F., McMichael, J., & Fung, J. J. (1994) · Annals of Surgery · doi:10.1097/00000658-199404000-00012 · Cited by 98
Cognitive Science: An Introduction to the Science of the Mind
Bermúdez, J. L. (2020) · Cited by 98
Mind, Body, World: Foundations of Cognitive Science
Dawson, M. R. W. (2013) · Athabasca University Press eBooks · doi:10.15215/aupress/9781927356173.01 · Cited by 98
Online Multiple Kernel Classification
Hoi, S. C. H., Jin, R., Zhao, P., & Yang, T. (2012) · Machine Learning · doi:10.1007/s10994-012-5319-2 · Cited by 98
Wojtecki, Ł., Iwaszenko, S., Apel, D. B., Bukowska, M., & Makówka, J. (2021) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2021.10.011 · Cited by 98
Hardware elementary perceptron based on polyaniline memristive devices
Demin, V. A., Erokhin, V. V., Emelyanov, A. V., Battistoni, S., Baldi, G., Iannotta, S., Kashkarov, P. K., & Kovalchuk, M. V. (2015) · Organic Electronics · doi:10.1016/j.orgel.2015.06.015 · Cited by 98
Molinari, F., Zeng, G., & Suri, J. S. (2010) · Journal of Ultrasound in Medicine · doi:10.7863/jum.2010.29.3.399 · Cited by 98
The Universal Approximation Property
Kratsios, A. (2021) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-020-09723-1 · Cited by 98
Neurosymbolic AI for Reasoning Over Knowledge Graphs: A Survey
DeLong, L. N., Mir, R. F., & Fleuriot, J. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3420218 · Cited by 98
Castellano-Méndez, M., González–Manteiga, W., Febrero–Bande, M., Prada-Sánchez, J. M., & Lozano-Calderón, R. (2004) · Journal of Hydrology · doi:10.1016/j.jhydrol.2004.03.011 · Cited by 97
Pattern recognition approaches in biomedical and clinical magnetic resonance spectroscopy: a review
El‐Deredy, W. (1997) · NMR in Biomedicine · doi:10.1002/(sici)1099-1492(199705)10:3 · Cited by 97
Unveiling phase transitions with machine learning
Canabarro, A., Fanchini, F. F., Malvezzi, A. L., Pereira, R. G., & Chaves, R. (2019) · Physical review. B./Physical review. B · doi:10.1103/physrevb.100.045129 · Cited by 97
A spiking network model for passage‐of‐time representation in the cerebellum
Yamazaki, T., & Tanaka, S. (2007) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2007.05837.x · Cited by 97
Vaddireddy, H., Rasheed, A., Staples, A., & San, O. (2020) · Physics of Fluids · doi:10.1063/1.5136351 · Cited by 97
Li, H., Wang, T., & Wu, G. (2021) · Structures · doi:10.1016/j.istruc.2021.09.008 · Cited by 97
Online Active Learning Methods for Fast Label-Efficient Spam Filtering.
Sculley, D. (2007) · Cited by 96
The Cerebellum and Adaptive Control
Barlow, J. S. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511529771 · Cited by 96
Artificial neural networks for machining processes surface roughness modeling
Pontes, F. J., Ferreira, J. R., Silva, M. B., de Paiva, A. P., & Balestrassi, P. P. (2009) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-009-2456-2 · Cited by 96
3-D mobile radio channel statistics
Clarke, R. H., & Khoo, W. L. (1997) · IEEE Transactions on Vehicular Technology · doi:10.1109/25.618205 · Cited by 96
Liew, B. X. W., Kovacs, F. M., Rügamer, D., & Royuela, A. (2022) · European Spine Journal · doi:10.1007/s00586-022-07188-w · Cited by 96
Li, C., Zhang, H., Ding, P., Yang, S., & Bai, Y. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113576 · Cited by 96
A Survey and Taxonomy of Loss Functions in Machine Learning
Ciampiconi, L., Elwood, A., Leonardi, M., Mohamed, A., & Rozza, A. (2026) · AI · doi:10.3390/ai7040128 · Cited by 100
Bhagat, S. K., Pilario, K. E., Babalola, O. E., Tiyasha, T., Yaqub, M., Onu, C. E., Pyrgaki, K., Falah, M. W., Jawad, A. H., Yaseen, D. A., et al. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.135522 · Cited by 95
Identifying Personal Stories in Millions of Weblog Entries
Gordon, A. S., & Swanson, R. (2009) · Cited by 95
Drew, P. J., & Monson, J. R. T. (2000) · Surgery · doi:10.1067/msy.2000.102173 · Cited by 95
A review of downscaling methods for remote sensing-based irrigation management: part I
Ha, W. S., Gowda, P. H., & Howell, T. A. (2012) · Irrigation Science · doi:10.1007/s00271-012-0331-7 · Cited by 95
Metasurface-enabled broadband multidimensional photodetectors
Jiang, H., Chen, Y., Guo, W., Zhang, Y., Zhou, R., Gu, M., Zhong, F., Ni, Z., Lü, J., Qiu, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52632-8 · Cited by 95
“The architecture of the neural network is formalized as follows 51 : Here, I i represents the photocurrent values detected from the three-port device under ultrabroadband, where I 1 , I 2 , I 3 (cid:1) (cid”
A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks
Rodríguez, E., Otero, B., Gutiérrez, N., & Canal, R. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3086296 · Cited by 95
Layer-Parallel Training of Deep Residual Neural Networks
Günther, S., Ruthotto, L., Schroder, J. B., Cyr, E. C., & Gauger, N. R. (2020) · SIAM Journal on Mathematics of Data Science · doi:10.1137/19m1247620 · Cited by 95
A neuroconstructivist model of past tense development and processing.
Westermann, G., & Ruh, N. (2012) · Psychological Review · doi:10.1037/a0028258 · Cited by 94
A pattern recognition approach to geophysical inversion using neural nets
Raiche, A. P. (1991) · Geophysical Journal International · doi:10.1111/j.1365-246x.1991.tb00801.x · Cited by 94
Online learning from click data for sponsored search
Ciaramita, M., Murdock, V., & Plachouras, V. (2008) · International World Wide Web Conference (WWW) · doi:10.1145/1367497.1367529 · Cited by 94
Statistical mechanics of complex neural systems and high dimensional data
Advani, M., Lahiri, S., & Ganguli, S. (2013) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2013/03/p03014 · Cited by 94
Sun, J., Fu, Y., Li, S., He, J., Xu, C., & Tan, L. (2018) · Journal of Sensors · doi:10.1155/2018/8580959 · Cited by 94
Generative Linguistics and Neural Networks at 60: Foundation, Friction, and Fusion
Pater, J. (2019) · Language · doi:10.1353/lan.2019.0009 · Cited by 94
Artificial intelligence for clinical decision support in neurology
Pedersen, M., Verspoor, K. M., Jenkinson, M., Law, M., Abbott, D. F., & Jackson, G. D. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa096 · Cited by 94
Singh, M., Pujar, G. V., Kumar, S. A., Bhagyalalitha, M., Akshatha, H. S., Abuhaija, B., Alsoud, A. R., Abualigah, L., Beeraka, N. M., & Gandomi, A. H. (2022) · Electronics · doi:10.3390/electronics11172634 · Cited by 94
Gautam, M. (2023) · Electricity · doi:10.3390/electricity4040020 · Cited by 94
Collins, M. (2004) · Text, speech and language technology · doi:10.1007/1-4020-2295-6_2 · Cited by 93
Multivariate statistical analysis methods in QSAR
Pirhadi, S., Shiri, F., & Ghasemi, J. B. (2015) · RSC Advances · doi:10.1039/c5ra10729f · Cited by 93
On the Hausdorff distance between the Heaviside step function and Verhulst logistic function
Kyurkchiev, N. V., & Markov, S. M. (2015) · Journal of Mathematical Chemistry · doi:10.1007/s10910-015-0552-0 · Cited by 93
Detection and application of influence rankings in small group meetings
Rienks, R., Zhang, D., Gática-Pérez, D., & Post, W. M. (2006) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1180995.1181047 · Cited by 93
Maiti, S., Tiwari, R. K., & Kümpel, H. (2007) · Geophysical Journal International · doi:10.1111/j.1365-246x.2007.03342.x · Cited by 93
Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy
Park, H., & Son, J. (2021) · Sensors · doi:10.3390/s21041186 · Cited by 93
Advances and Challenges of Single‐Pixel Imaging Based on Deep Learning
Song, K., Bian, Y., Wang, D., Li, R., Wu, K., Liu, H., Qin, C., Hu, J., & Xiao, L. (2024) · Laser & Photonics Review · doi:10.1002/lpor.202401397 · Cited by 93
The Forgetron: A Kernel-Based Perceptron on a Fixed Budget
Dekel, O., Shalev‐Shwartz, S., & Singer, Y. (2005) · Cited by 93
Azadeh, A., & Zarrin, M. (2016) · Safety Science · doi:10.1016/j.ssci.2016.06.001 · Cited by 93
Psychology in Cognitive Science: 1978-2038
Dedre Gentner, C. (2010) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2010.01103.x · Cited by 93
Electrical and dye coupling in an identified group of neurons in a coelenterate
Spencer, A. N., & Satterlie, R. A. (1980) · Journal of Neurobiology · doi:10.1002/neu.480110103 · Cited by 92
Cortical Connections and Parallel Processing: Structure and Function
Ballard, D. H. (1987) · The MIT Press eBooks · doi:10.7551/mitpress/7113.003.0027 · Cited by 92
A Deep Learning Perspective on the Origin of Facial Expressions
Breuer, R., & Kimmel, R. (2017) · arXiv · doi:10.48550/arxiv.1705.01842 · Cited by 92
The measurement of technical efficiency: a neural network approach
González, D. S., Delgado, F. J., & Castro, A. V. (2004) · Applied Economics · doi:10.1080/0003684042000217661 · Cited by 92
Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
Ji, Z., Guo, W., Wood, E. L., Liu, J., Sakkiah, S., Xu, X., Patterson, T. A., & Hong, H. (2022) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.1c00310 · Cited by 92
Artificial neural networks for photonic applications—from algorithms to implementation: tutorial
Freire, P. J., Manuylovich, E. S., Prilepsky, J. E., & Turitsyn, S. K. (2023) · Advances in Optics and Photonics · doi:10.1364/aop.484119 · Cited by 92
Artificial intelligence in food science and nutrition: A narrative review
Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., Ashida, K., et al. (2022) · Nutrition Reviews · doi:10.1093/nutrit/nuac033 · Cited by 91
[29] Artificial neural networks
Katz, W. T., Snell, J. W., & Merickel, M. (1992) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/0076-6879(92)10031-8 · Cited by 91
Bessa, M. A., & Pellegrino, S. (2018) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2018.01.035 · Cited by 91
Weakly supervised named entity transliteration and discovery from multilingual comparable corpora
Klementiev, A., & Roth, D. (2006) · International Conference on Computational Linguistics · doi:10.3115/1220175.1220278 · Cited by 91
Design of indoor positioning systems based on location fingerprinting technique
Krishnamurthy, P. V., & Kaemarungsi, K. (2005) · D-Scholarship@Pitt (University of Pittsburgh) · Cited by 91
Kuan, C. (2008) · Palgrave Macmillan UK eBooks · doi:10.1007/978-1-349-58802-2_63 · Cited by 91
An artificial neural network for SPECT image reconstruction
Floyd, C. E. (1991) · IEEE Transactions on Medical Imaging · doi:10.1109/42.97600 · Cited by 91
Mueller, A., Candrian, G., Grane, V. A., Kropotov, J. D., Ponomarev, V. A., & Baschera, G.-M. (2011) · Nonlinear Biomedical Physics · doi:10.1186/1753-4631-5-5 · Cited by 91
A Generalized Deep Learning Algorithm Based on NMF for Multi-View Clustering
Wang, D., Li, T., Deng, P., Liu, J., Huang, W. X., & Zhang, F. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3163584 · Cited by 91
“According to the working principle of neurons in deep learning network [13], [20], [34], [35] to build GDL, we first where L GDL is the number of training rounds required to obtain a desirable low-dimensional matrix W v .”
BackgroundCondition-Based Maintenance of Naval Propulsion Systems with supervised Data Analysis
Cipollini, F., Oneto, L., Coraddu, A., Murphy, A. J., & Anguita, D. (2017) · Ocean Engineering · doi:10.1016/j.oceaneng.2017.12.002 · Cited by 91
Introduction to convolutional neural network using Keras; an understanding from a statistician
Lee, H., & Song, J. (2019) · Communications for Statistical Applications and Methods · doi:10.29220/csam.2019.26.6.591 · Cited by 91
Stock market prediction with deep learning: The case of China
Liu, Q., Tao, Z., Tse, Y., & Wang, C. (2021) · Finance research letters · doi:10.1016/j.frl.2021.102209 · Cited by 91
Adriano Barra, A., Genovese, G., Sollich, P., & Tantari, D. (2018) · Physical review. E · doi:10.1103/physreve.97.022310 · Cited by 91
The language machine: Psycholinguistics in review
Altmann, G. T. M. (2001) · British Journal of Psychology · doi:10.1348/000712601162130 · Cited by 91
A deep neural network approach for learning intrinsic protein-RNA binding preferences
Ben-Bassat, I., Chor, B., & Orenstein, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty600 · Cited by 90
Cybersickness Analysis with EEG Using Deep Learning Algorithms
Jeong, D., Yoo, S., & Jang, Y. (2019) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr.2019.8798334 · Cited by 90
Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece
Moustris, K. P., Larissi, I. K., Nastos, P. T., & Paliatsos, A. G. (2011) · Water Resources Management · doi:10.1007/s11269-011-9790-5 · Cited by 90
Noise Tolerant Variants of the Perceptron Algorithm
Khardon, R., & Wachman, G. (2007) · doi:10.5555/1248659.1248667 · Cited by 90
Monitoring land use in cities using satellite imagery and deep learning
Banquet, A., Delbouve, P., Daams, M. N., & Veneri, P. (2022) · OECD regional development papers · doi:10.1787/dc8e85d5-en · Cited by 90
Park, M.-J., Lee, J., Cho, K., Park, J., Moon, J., Lee, S.-H., Kim, T., Oh, S., Choi, S.-W., Choi, Y., et al. (2022) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2022.3193354 · Cited by 90
“W ITH the advent of the world’s first artificial intelligence (AI) model perceptron [1] in 1960, the progress of compute performance growth has been traditionally consistent with AI model advancements.”
MethodFrom fundamentals to frontiers: a review of memristor mechanisms, modeling and emerging applications
Thakkar, P., Gosai, J., Gogoi, H. J., & Solanki, A. (2024) · Journal of Materials Chemistry C · doi:10.1039/d3tc03692h · Cited by 94
A synaptic learning rule for exploiting nonlinear dendritic computation
Bicknell, B. A., & Häusser, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.044 · Cited by 90
ARIMA vs LSTM on NASDAQ stock exchange data
Kobiela, D., Krefta, D., Król, W., & Weichbroth, P. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.445 · Cited by 90
Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review
Ferro, M. V., & Catania, P. (2023) · Horticulturae · doi:10.3390/horticulturae9030399 · Cited by 90
Astronomia ex machina: a history, primer and outlook on neural networks in astronomy
Smith, M. J., & Geach, J. E. (2023) · Royal Society Open Science · doi:10.1098/rsos.221454 · Cited by 91
Shi, H., Kowalczewski, A. C., Vu, D., Liu, X., Salekin, A., Yang, H., & Ma, Z. (2023) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2023.100276 · Cited by 90
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated PerceptronPreprint
Vaswani, S., Bach, F., & Schmidt, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.07288 · Cited by 90
Möckl, L., Roy, A. R., & Moerner, W. E. (2020) · Biomedical Optics Express · doi:10.1364/boe.386361 · Cited by 89
Nanotube devices based crossbar architecture: toward neuromorphic computing
Zhao, W. S., Agnus, G., Derycke, V., Filoramo, A., Bourgoin, J. ‐., & Gamrat, C. (2010) · Nanotechnology · doi:10.1088/0957-4484/21/17/175202 · Cited by 89
Catania, C. A., Bromberg, F., & Garino, C. G. G. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.08.068 · Cited by 89
Limitations of Learning via Embeddings in Euclidean Half-Spaces
Ben-David, S., Eiron, N., & Simon, H. U. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44581-1_25 · Cited by 89
Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging
Retson, T., Besser, A. H., Sall, S., Golden, D. I., & Hsiao, A. (2019) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000385 · Cited by 89
End-to-end deep representation learning for time series clustering: a comparative study
Lafabrègue, B., Weber, J., Gançarski, P., & Forestier, G. (2021) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-021-00796-y · Cited by 89
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