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 2,601–2,700 of 13,333
Latif, I., Surana, M., & Banerjee, A. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.108871 · Cited by 27
Harmonics Forecasting of Renewable Energy System Using Hybrid Model Based on LSTM and ANFIS
Hadi, F. M. A., & Aly, H. H. (2024) · IEEE Access · doi:10.1109/access.2024.3386092 · Cited by 27
Guo, J., Wang, W., Guo, J., D’Ariano, A., Bosi, T., & Zhang, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123989 · Cited by 27
Syntax-Based Grammaticality Improvement using CCG and Guided Search
Zhang, Y., & Clark, S. C. (2011) · Cited by 27
Randomized Variable Elimination
Stracuzzi, D. J., & Utgoff, P. E. (2002) · doi:10.5555/1005332.1044704 · Cited by 27
Sierra, B., Lazkano, E., Jauregi, E., & Irigoien, I. (2009) · Decision Support Systems · doi:10.1016/j.dss.2009.07.010 · Cited by 27
Adaptive game level creation through rank-based interactive evolution
Liapis, A., Martínez, H. P., Togelius, J., & Yannakakis, G. N. (2013) · IEEE Conference on Computational Intelligence and Games (CIG) · doi:10.1109/cig.2013.6633651 · Cited by 27
Yaoyu, Z., Lin, T., Guangming, Z., Yi, Z., Zhen, L., Yuanyuan, L., Chen, J., Guide, Y., Zhou, L., & Zhang, S. (2014) · Analytical Methods · doi:10.1039/c3ay41976b · Cited by 27
Malicious web page detection based on on-line learning algorithm
Zhang, W., Ding, Y., Tang, Y., & Zhao, B. (2011) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2011.6016954 · Cited by 27
Radial Basis Function Neural Network
Matera, F. (1998) · Substance Use & Misuse · doi:10.3109/10826089809115868 · Cited by 27
STRUCTURALLY BIASED PHONOLOGY: COMPLEXITY IN LEARNING AND TYPOLOGY
Pater, J., & Moreton, E. (2012) · Cited by 27
Embedding Learning for Declarative Memories
Volker Tresp, C., Ma, Y., Baier, S., & Yang, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58068-5_13 · Cited by 27
Hawryluk, M. R., & Mrzygłód, B. (2017) · Eksploatacja i Niezawodnosc - Maintenance and Reliability · doi:10.17531/ein.2017.3.4 · Cited by 27
Artificial Intelligent Models for Breast Cancer Early Detection
Halim, E., Halim, P. P., & Hebrard, M. (2018) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech.2018.8528140 · Cited by 27
Achieving Neural Compatibility With Human Sensorimotor Control in Prosthetic and Therapeutic Devices
Lan, N., Niu, C. M., Hao, M., Chou, C.-H., & Dai, C. (2019) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2019.2930356 · Cited by 27
Combining lexical and context features for automatic ontology extension
Althubaiti, S., Kafkas, Ş., Abdelhakim, M., & Hoehndorf, R. (2020) · Journal of Biomedical Semantics · doi:10.1186/s13326-019-0218-0 · Cited by 27
McCandless, T. C., Dettling, S. M., & Haupt, S. E. (2020) · Energies · doi:10.3390/en13030689 · Cited by 27
Finding continuity and discontinuity in fish schools via integrated information theory
Niizato, T., Sakamoto, K., Mototake, Y.-I., Murakami, H., Tomaru, T., Hoshika, T., & Fukushima, T. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229573 · Cited by 27
Song, G., Cheng, J., & Grattan, K. T. V. (2020) · IEEE Access · doi:10.1109/access.2020.2978392 · Cited by 27
Zhang, J., Yuan, H., & Dong, H. (2020) · Springer eBooks · doi:10.1007/978-981-15-4095-0_1 · Cited by 27
Ahmadi, S., Beyhaghi, H., Blum, A. L., & Naggita, K. (2021) · ACM Conference on Economics and Computation (EC) · doi:10.1145/3465456.3467629 · Cited by 27
Online training on a budget of support vector machines using twin prototypes
Wang, Z., & Vučetić, S. (2010) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.10075 · Cited by 27
A survey of deep network techniques all classifiers can adopt
Ghods, A., & Cook, D. J. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00722-8 · Cited by 27
Determining Hall of Fame Status for Major League Baseball Using an Artificial Neural Network
Young, W. A., Holland, W. S., & Weckman, G. R. (2008) · Journal of Quantitative Analysis in Sports · doi:10.2202/1559-0410.1131 · Cited by 27
Models of visual categorization
Serre, T. (2016) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1385 · Cited by 27
Three Principles for Models of Category Learning
Kruschke, J. K. (1993) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/s0079-7421(08)60136-x · Cited by 27
Measurement of Learning and Mental Abilities
Gulliksen, H. O. (1961) · Psychometrika · doi:10.1007/bf02289688 · Cited by 27
Artificial fuzzy neural networks in civil engineering
Rajasekaran, S., Febin, M. F., & Ramasamy, J. (1996) · Computers & Structures · doi:10.1016/0045-7949(96)00044-2 · Cited by 27
Evolving intelligent agents: A 50 year quest
Jong, K. A. D. (2008) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2007.913370 · Cited by 27
Artificial Neural Networks in Control Applications
Vemuri, V. (1993) · Advances in computers · doi:10.1016/s0065-2458(08)60272-7 · Cited by 27
New Neural Network Methods for Forecasting Regional Employment: an Analysis of German Labour Markets
Patuelli, R., Reggiani, A., Nijkamp, P. J., & Blien, U. (2006) · Spatial Economic Analysis · doi:10.1080/17421770600661568 · Cited by 27
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
Khardon, R., Roth, D., & Servedio, R. A. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0059 · Cited by 27
Precise deep neural network computation on imprecise low-power analog hardwarePreprint
Binas, J., Neil, D., Indiveri, G., Liu, S., & Pfeiffer, M. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-132629 · Cited by 27
Macroeconomic Indicator Forecasting with Deep Neural NetworksPreprint
Hall, A. S., & Cook, T. R. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3046657 · Cited by 27
HyperST-Net: Hypernetworks for Spatio-Temporal ForecastingPreprint
Pan, Z., Liang, Y., Zhang, J., Yi, X., Yu, Y. H., & Zheng, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.10889 · Cited by 27
Parallel Scheduled SamplingPreprint
Duckworth, D., Neelakantan, A., Goodrich, B., Kaiser, Ł., & Samy Bengio, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.04331 · Cited by 27
Learning Halfspaces with Massart Noise Under Structured DistributionsPreprint
Diakonikolas, I., Kontonis, V., Tzamos, C., & Zarifis, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05632 · Cited by 27
Online Bayesian Passive-Aggressive LearningPreprint
ShiTianlin, & ZhuJun (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.3388 · Cited by 27
Categories, words and rules in language acquisition
Hochmann, J. (2010) · SDL · Cited by 27
Royer, A., Farges, O., Boulet, P., & Burot, D. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4117265 · Cited by 27
Algorithm, Human, or the Centaur: How to Enhance Clinical Care?Preprint
Dean, A., Orfanoudaki, A., Saghafian, S., Song, K., Chakkera, H. A., & Cook, C. B. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4302002 · Cited by 27
Quantum deep learning neural networks
Kamruzzaman, A., Alhwaiti, Y., Leider, A., & Tappert, C. C. (2020) · Lecture notes in networks and systems · doi:10.1007/978-3-030-12385-7_24 · Cited by 26
Duff, A., Rennó‐Costa, C., Marcos, E., Luvizotto, A., Giovannucci, A., Sánchez-Fibla, M., Bernardet, U., & Verschure, P. F. M. J. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-05181-4_2 · Cited by 26
Veisi, H. (2023) · Industrial and applied mathematics · doi:10.1007/978-981-19-6553-1_1 · Cited by 26
Idzik, C., Kramer, A., Hirt, G., & Lohmar, J. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02115-2 · Cited by 26
Synthetic biological neural networks: From current implementations to future perspectives
Vasle, A. H., & Moškon, M. (2024) · Biosystems · doi:10.1016/j.biosystems.2024.105164 · Cited by 26
Guo, H., Chen, W., Wang, Y. S., Ma, F., Sun, P., Yuan, T., & Xie, X. (2024) · Mechanics of Advanced Materials and Structures · doi:10.1080/15376494.2024.2330488 · Cited by 26
Salloum, S. A., Marzouqi, A. M. A., Gupta, B., Aburayya, A., Saidat, M. R. A., & Alfaisal, R. (2024) · Studies in big data · doi:10.1007/978-3-031-52280-2_1 · Cited by 26
Matray, V., Amlani, F., Feyel, F., & Néron, D. (2024) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2024.117243 · Cited by 26
Astorayme, M. A., Vázquez‐Rowe, I., & Kahhat, R. F. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.173843 · Cited by 26
Giesa, N., Sekutowicz, M., Rubarth, K., Spies, C., Balzer, F., Haufe, S., & Boie, S. D. (2024) · Communications Medicine · doi:10.1038/s43856-024-00681-x · Cited by 26
Deep learning in fluorescence imaging and analysis
Mao, J., & He, H. (2024) · Journal of intelligent medicine. · doi:10.1002/jim4.17 · Cited by 26
Machine Learning‐Powered Optimization of a CHO Cell Cultivation Process
Richter, J., Wang, Q., Lange, F., Thiel, P., Yilmaz, N., Solle, D., Zhuang, X., & Beutel, S. (2025) · Biotechnology and Bioengineering · doi:10.1002/bit.28943 · Cited by 26
Deep learning systems as complex networks
Testolin, A., Piccolini, M., & Suweis, S. S. (2019) · Journal of Complex Networks · doi:10.1093/comnet/cnz018 · Cited by 26
Heid, E., Fleck, M., Chatterjee, P., Schröder, C., & MacKerell, A. D. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b01289 · Cited by 26
Filippov, V. A., Bobylev, A. N., Busygin, A. N., Pisarev, A. D., & Udovichenko, S. Y. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04383-7 · Cited by 26
The growth of cognition: Free energy minimization and the embryogenesis of cortical computation
Wright, J. J., & Bourke, P. D. (2020) · Physics of Life Reviews · doi:10.1016/j.plrev.2020.05.004 · Cited by 27
Artificial Neural Networks for Aging Simulation of Electrolysis Stacks
Bähr, M., Gusak, A., Stypka, S., & Oberschachtsiek, B. (2020) · Chemie Ingenieur Technik · doi:10.1002/cite.202000089 · Cited by 26
Fruit Detection from Digital Images Using CenterNet
Zhao, K., & Yan, W. Q. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-72073-5_24 · Cited by 26
K-fold Validation Neural Network Approach for Predicting the One-Day Compressive Strength of UHPC
García, J. A. (2021) · Advances in Civil Engineering Materials · doi:10.1520/acem20200055 · Cited by 26
Deep ReLU neural networks in high-dimensional approximation
Dũng, Ð., & Nguyen, V. K. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.07.027 · Cited by 26
Artificial Intelligence in Liver Transplantation
Khorsandi, S. E., Hardgrave, H., Osborn, T. A., Klutts, G. N., Nigh, J., Spencer‐Cole, R., Kakos, C. D., Anastasiou, I., Mavros, M. N., & Giorgakis, E. (2021) · Transplantation Proceedings · doi:10.1016/j.transproceed.2021.09.045 · Cited by 26
A Novel Multi-Neural Ensemble Approach for Cancer Diagnosis
Gupta, S., Gupta, M. K., & Kumar, R. (2021) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.2018182 · Cited by 26
Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_2 · Cited by 26
TRSAv1: A new benchmark dataset for classifying user reviews on Turkish e-commerce websites
Aydoğan, M., & Kocaman, V. (2022) · Journal of Information Science · doi:10.1177/01655515221074328 · Cited by 26
Graham, C. A., Shamkhalichenar, H., Browning, V. E., Byrd, V. J., Liu, Y., Gutierrez‐Wing, M. T., Novelo, N. D., Choi, J., & Tiersch, T. R. (2022) · Aquaculture · doi:10.1016/j.aquaculture.2022.738039 · Cited by 26
Leijnen, S., & Fjodor, V. V. (2020) · Proceedings · doi:10.3390/proceedings47010009 · Cited by 26
A Survey on Transformers for Point Cloud Processing: An Updated Overview
Zeng, J., Wang, D., & Chen, P. (2022) · IEEE Access · doi:10.1109/access.2022.3198999 · Cited by 26
A Novel Method for Fashion Clothing Image Classification Based on Deep Learning
Shin, S.-Y., Jo, G., & Wang, G. (2023) · Journal of Information and Communication Technology · doi:10.32890/jict2023.22.1.6 · Cited by 26
Ouali, L., Kabiri, L., Namous, M., Hssaisoune, M., Abdelrahman, K. S., Fnais, M. S., Kabiri, H., Hafyani, M. E., Oubaassine, H., Arioua, A., et al. (2023) · Sustainability · doi:10.3390/su15053874 · Cited by 26
Çırak, C. R., & ÇALIK, H. (2023) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2023.101436 · Cited by 26
Calculations of fractional derivative option pricing models based on neural network
Song, L., Wang, Y., Tan, Y., & Duan, K. (2023) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2023.115462 · Cited by 26
García, J. A., Gómez, J. A. F., Khan, M. I., Abbas, Y. M., & Pacheco-Bustos, C. A. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.132992 · Cited by 26
Bisset, C., Venter, P. V. Z., & Coetzer, R. L. J. (2023) · International Journal of Sustainable Energy · doi:10.1080/14786451.2023.2244618 · Cited by 26
A water quality prediction method based on k-nearest-neighbor probability rough sets and PSO-LSTM
Huang, M., Hu, B. Q., Jiang, H., & Fang, B. W. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05024-2 · Cited by 26
Universal approximation theorem for vector- and hypercomplex-valued neural networks
Valle, M. E., Vital, W. L., & Vieira, G. H. A. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106632 · Cited by 26
A Review on Artificial Neural Networks for Structural Analysis
Saini, R. (2025) · Journal of Vibration Engineering & Technologies · doi:10.1007/s42417-024-01749-7 · Cited by 26
Nanoscale connections for brain-like circuits
Legenstein, R. A. (2015) · Nature · doi:10.1038/521037a · Cited by 26
An Incremental Probabilistic Model to Predict Bus Bunching in Real-Time
Moreira-Matias, L., da Gama, J. M. P., Mendes‐Moreira, J., & de Sousa, J. F. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12571-8_20 · Cited by 26
Keywords in the mental lexicon
Vitevitch, M. S., & Goldstein, R. (2014) · Journal of Memory and Language · doi:10.1016/j.jml.2014.03.005 · Cited by 26
Tsao, C., Liu, C.-Y., Cha, T., Wu, S.-T., Sun, G.-H., Yü, D., Chen, H.-I., Chang, S.-Y., Chen, S.-C., & Hsu, C. (2014) · Journal of the Chinese Medical Association · doi:10.1016/j.jcma.2014.06.014 · Cited by 26
Rapid automated detection of roots in minirhizotron images
Zeng, G., Birchfield, S. T., & Wells, C. E. (2008) · Machine Vision and Applications · doi:10.1007/s00138-008-0179-2 · Cited by 26
Extraction Method of Handwritten Digit Recognition Tested on the MNIST Database
Kessab, B. E., Daoui, C., Bouikhalene, B., Fakir, M., & Moro, K. M. (2013) · International Journal of Advanced Science and Technology · Cited by 26
Predicting golf ball trajectories from swing plane: An artificial neural networks approach
Bačić, B. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.07.014 · Cited by 26
Hájek, P., Olej, V., & Myšková, R. (2013) · Digitální knihovna Univerzity Pardubice (Univerzity Pardubice) · Cited by 26
Using Machine Learning to Improve Cylindrical Algebraic Decomposition
Huang, Z., England, M., Wilson, D. J., Bridge, J. P., Davenport, J. H., & Paulson, L. C. (2019) · Mathematics in Computer Science · doi:10.1007/s11786-019-00394-8 · Cited by 26
Advanced Neural Network Approach, Its Explanation with LIME for Credit Scoring Application
Munkhdalai, L., Wang, L., Park, H. W., & Ryu, K. H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-14802-7_35 · Cited by 26
Flux prediction using artificial neural network (ANN) for the upper part of glycolysis
Nagaraja, A. A., Fontaine, N., Delsaut, M., Charton, P., Damour, C., Offmann, B., Grondin-Pérez, B., & Cadet, F. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216178 · Cited by 26
A Primer on Deep Learning Architectures and Applications in Speech Processing
Ogunfunmi, T., Ramachandran, R. P., Togneri, R. B., Zhao, Y., & Xia, X. (2019) · Circuits Systems and Signal Processing · doi:10.1007/s00034-019-01157-3 · Cited by 26
A Hybrid Credit Scoring Model Using Neural Networks and Logistic Regression
Munkhdalai, L., Lee, J. Y., & Ryu, K. H. (2019) · Smart innovation, systems and technologies · doi:10.1007/978-981-13-9714-1_27 · Cited by 26
Wu, J. M., Tsai, M., Xiao, S.-H., & Liaw, Y. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01826-1 · Cited by 26
Abdoun, N., Assad, S. E., Hoang, T. M., Olivier Déforges, A., Assaf, R., & Khalil, M. A. (2020) · Entropy · doi:10.3390/e22091012 · Cited by 26
Dreher, D., Schmidt, M., Welch, C., Ourza, S., Zündorf, S., Maucher, J., Peters, S., Dreizler, A. M., Böhm, B., & Hanuschkin, A. (2020) · International Journal of Engine Research · doi:10.1177/1468087420974148 · Cited by 26
Lyngdoh, G. A., Li, H., Zaki, M. F. M., Krishnan, N. M. A., & Das, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-78368-1 · Cited by 26
Dissecting the Roles of Supervised and Unsupervised Learning in Perceptual Discrimination Judgments
Loewenstein, Y., Raviv, O., & Ahissar, M. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0757-20.2020 · Cited by 26
Sampling Kaczmarz-Motzkin method for linear feasibility problems: generalization and acceleration
Morshed, M. S., Islam, M. S., & Noor‐E‐Alam, M. (2021) · Mathematical Programming · doi:10.1007/s10107-021-01649-8 · Cited by 26
Marini, F. (2009) · Elsevier eBooks · doi:10.1016/b978-044452701-1.00128-9 · Cited by 26
A Comparative Evaluation of Ensemble Classifiers for Malicious Webpage Detection
Subaşı, A., Balfaqih, M., Balfagih, Z., & Alfawwaz, K. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.10.082 · Cited by 26
Lexcellent, C. (2019) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-030-21445-6_2 · Cited by 26
Application of Neural Networks in Petroleum Reservoir Lithology and Saturation Prediction
Cvetković, M., Velić, J., & Malvić, T. (2009) · Geologia Croatica · doi:10.4154/gc.2009.10 · Cited by 26
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