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 4,701–4,800 of 13,333
Neuro-evolution of spiking neural networks on SpiNNaker neuromorphic hardware
Vandesompele, A., Walter, F., & Röhrbein, F. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7850250 · Cited by 9
Neuromorphic Computing based on Resistive RAM
Chen, Z., Wu, H., Gao, B., Yao, P., Li, X., & Qian, H. (2017) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3060403.3066873 · Cited by 9
RG-inspired machine learning for lattice field theory
Foreman, S., Giedt, J., Meurice, Y., & Unmuth-Yockey, J. (2018) · EPJ Web of Conferences · doi:10.1051/epjconf/201817511025 · Cited by 9
Neural Network-Based Analog-to-Digital Converters
Tankimanova, A., & James, A. P. (2018) · InTech eBooks · doi:10.5772/intechopen.73038 · Cited by 9
Bioinformatics and Translation Elongation
Xia, X. (2018) · Springer eBooks · doi:10.1007/978-3-032-18842-7_11 · Cited by 9
A Holistic Open Pit Mine Slope Stability Index Using Artificial Neural Networks
Fakir, M., & Ferentinou, M. (2017) · Cited by 9
Citizen Data and Trust in Official Statistics
Ruppert, E. S., Grommé, F., Ustek‐Spilda, F., & Cakici, B. (2019) · Economie et Statistique / Economics and Statistics · doi:10.24187/ecostat.2018.505d.1971 · Cited by 9
Method of Selecting an Optimal Activation Function in Perceptron for Recognition of Simple Objects
Latypova, R., & Tumakov, D. N. (2018) · IEEE East-West Design & Test Symposium (EWDTS) · doi:10.1109/ewdts.2018.8524867 · Cited by 9
Picturing, signifying, and attending
Huebner, B. (2018) · Belgrade Philosophical Annual · doi:10.5937/bpa1831007h · Cited by 9
Yoshida, Y., Karakida, R., Okada, M., & Амари, Ш.-И. (2019) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8121/ab0669 · Cited by 9
Liu, J., Li, J., Wang, T., & He, R. (2019) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice.2019.00016 · Cited by 9
A survey on hyper basis function neural networks
Zhou, Y., Mu, T., Pang, Z., & Zheng, C. (2019) · Systems Science & Control Engineering · doi:10.1080/21642583.2019.1699474 · Cited by 9
Online Learning Using Multiple Times Weight Updating
Singh, S., & Sharma, A. (2020) · Applied Artificial Intelligence · doi:10.1080/08839514.2020.1730623 · Cited by 9
Kanchan, M., & Maniyeri, R. (2020) · Fluid Dynamics Research · doi:10.1088/1873-7005/aba9b8 · Cited by 9
In Depth Analysis, Applications and Future Issues of Artificial Neural Network
Soni, B., Mathur, P., & Bora, A. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-52067-0_7 · Cited by 9
Leszczyński, Z., & Jasiński, T. (2020) · Sustainability · doi:10.3390/su12208353 · Cited by 9
Kang, B., & Lee, K. (2020) · Geofluids · doi:10.1155/2020/8892556 · Cited by 9
Whitman, J., Maske, H., Kingravi, H. A., & Chowdhary, G. V. (2021) · IEEE Control Systems · doi:10.1109/mcs.2020.3032801 · Cited by 9
A new spin on optimal portfolios and ecological equilibria
Garnier-Brun, J., Benzaquen, M., Ciliberti, S., & Bouchaud, J. (2021) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ac21d9 · Cited by 9
DigiNet: Prediction of Assamese handwritten digits using convolutional neural network
Dutta, P., & Muppalaneni, N. B. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6451 · Cited by 9
Gordienko, Y. G., Kostiukevych, K., Gordienko, N., Rokovyi, O., Alienin, O., & Stirenko, S. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-80475-6_1 · Cited by 9
Boulas, K. S., Dounias, G. D., & Papadopoulos, C. T. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01828-6 · Cited by 9
Deep Learning and Computational Chemistry
James, T., & Hristozov, D. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_5 · Cited by 9
Bio-inspired computing by nonlinear network dynamics—a brief introduction
Neves, F. S., & Timme, M. (2021) · Journal of Physics Complexity · doi:10.1088/2632-072x/ac3ad4 · Cited by 9
Chen, Z., Fang, Z., Sheng, V. S., Edwards, A., & Zhang, K. (2021) · IEEE International Conference on Big Knowledge (ICBK) · doi:10.1109/ickg52313.2021.00031 · Cited by 9
Parasitic-Aware Modelling for Neural Networks Implemented with Memristor Crossbar Array
Cao, T., Liu, C., Gao, Y., & Goh, W. L. (2021) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc51149.2021.00025 · Cited by 9
崔学刚, Xuegang, C., 方创琳, 李君, Liu, H., 张蔷, Chuanglin, F., Jun, L., Haimeng, L., & Qiang, Z. (2019) · 地理科学进展 · doi:10.18306/dlkxjz.2019.01.010 · Cited by 9
Zhang, X., Zhang, T., Li, L., & Zhang, J. (2022) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1177/09544100221097586 · Cited by 9
Deep Convolution Neural Networks for Image Classification
Kulkarni, A. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130603 · Cited by 9
Spiking Neural Networks Diagnosis of ADHD subtypes through EEG Signals Evaluation
Pedrollo, G. R., Franco, A. R., Bagesteiro, L. B., & Balbinot, A. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871223 · Cited by 9
Stirling engine optimization using artificial neural networks algorithm
Taki, O., Rhazi, K. S., & Mejdoub, Y. (2023) · ITM Web of Conferences · doi:10.1051/itmconf/20235202010 · Cited by 9
Learning With Incremental Instances and Features
Gu, S., Qian, Y., & Hou, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3236479 · Cited by 9
Thomakos, D. D., & Xidonas, P. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100284 · Cited by 9
Morelli, M. C., Hauth, J. M., Guardone, A., Huan, X., & Zhou, B. Y. (2023) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-023-03610-z · Cited by 9
Online Active Continual Learning for Robotic Lifelong Object Recognition
Nie, X., Deng, Z., He, M., Fan, M., & Tang, Z. S. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3308900 · Cited by 9
Lötsch, J., & Ultsch, A. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3040054 · Cited by 9
Chen, B., Jin, J., Liu, H., Yang, Z., Zhu, H., Wang, Y., Lin, J., Wang, S., & Chen, S. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1289669 · Cited by 9
GENEL VERİ KORUMA İLKELERİNİN YAPAY ZEKÂ KARŞISINDA UYGULANABİLİRLİĞİ SORUNU
BİLGİÇ, E. T. (2024) · Türkiye Adalet Akademisi Dergisi · doi:10.54049/taad.1418236 · Cited by 9
Algorithms and Representations for Personalised Information Access
Cöster, R. (2005) · Cited by 9
Entropy-Guided Feature Generation for Structured Learning of Portuguese Dependency Parsing
Fernandes, E. R., & Milidiú, R. L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28885-2_17 · Cited by 9
A Primal–Dual Smooth Perceptron–von Neumann Algorithm
Soheili, N., & Peña, J. (2013) · Fields Institute communications · doi:10.1007/978-3-319-00200-2_17 · Cited by 9
Comparison of Machine Learning Classifiers for Recognition of Online and Offline Handwritten Digits
Omidiora, E. O., Adeyanju, I., & Fenwa, O. D. (2013) · Journals & Books Hosting (International Knowledge Sharing Platform) · Cited by 9
Artificial Neural Networks in archaeology
Deravignone, L., & Jánica, G. M. (2006) · CNR Solar (Scientific Open-access Literature Archive and Repository) (Consiglio Nazionale delle Ricerche) · Cited by 9
Rojas–Galeano, S., Hsieh, E., Agranoff, D., Krishna, S., & Fernández-Reyes, D. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001806 · Cited by 9
Hans J. Bremermann: A pioneer in mathematical biology
Anderson, R. W., & Conrad, M. (1995) · Biosystems · doi:10.1016/0303-2647(94)01442-a · Cited by 9
Indices de formes et de textures. Application au classement de noyaux de cellules
Thibault, G., Fertil, B., Séqueira, J., & Mari, J.-L. (2010) · Ingénierie des systèmes d information · doi:10.3166/isi.15.2.73-96 · Cited by 9
Amico, F. D., Guattari, C., & Benedetto, A. (2010) · International Conference on Ground Penetrating Radar (GPR) · doi:10.1109/icgpr.2010.5550076 · Cited by 9
Huang, C., Hsu, N., Wei, C., & Lo, C.-W. (2015) · Advances in Meteorology · doi:10.1155/2015/472523 · Cited by 9
Shedding weights: More with less
Achler, T., Omar, C., & Amir, E. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634224 · Cited by 9
Estimation of Housing Prices by Fuzzy Regression and Artificial Neural Network
Ghodsi, R., Boostani, A., & Faghihi, F. (2010) · Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation · doi:10.1109/ams.2010.29 · Cited by 9
Individual Sequence Prediction Using Memory-Efficient Context Trees
Dekel, O., Shalev‐Shwartz, S., & Singer, Y. (2009) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2030460 · Cited by 9
Wersborg, I. S. G., Borgwardt, F., & Diepold, K. J. (2009) · IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO) · doi:10.1109/arso.2009.5587056 · Cited by 9
Diabetes Diagnosis with Maximum Covariance Weighted Resilience Back Propagation Procedure
Olatubosun, O., Olusoga, F., & Abayomi, F. (2015) · British Journal of Mathematics & Computer Science · doi:10.9734/bjmcs/2015/14871 · Cited by 9
Trivalent Chromium removal from Aqueous solution using Raw Natural Mixed Clay from BURKINA FASO
Samuel, P., Persson, I., Guel, B., Lundberg, D., & Faso, B. (2013) · Cited by 9
Motor learning and synaptic plasticity in the cerebellum
Thompson, R. F. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081887 · Cited by 9
Learning Future Classifiers without Additional Data
Kumagai, A., & Iwata, T. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10208 · Cited by 9
Recognising Gait Patterns of People in Risk of Falling with a Multi-layer Perceptron
Hoffmann, R., Lauterbach, C., Techmer, A., Conradt, J., & Steinhage, A. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-39904-1_8 · Cited by 9
Naturalistic Approaches to Creativity
Stokes, D., & Paul, E. S. (2016) · Wiley eBooks · doi:10.1002/9781118661666.ch22 · Cited by 9
Knowledge Discovery in Biomedical Data: Theory and Methods
Holmes, J. H. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-401678-1.00007-5 · Cited by 9
Dutta, S., Madan, S., Parikh, H., & Sundar, D. (2016) · BMC Genomics · doi:10.1186/s12864-016-3323-9 · Cited by 9
Zagajewski, B. (2010) · Cited by 9
The History of Writing as a History of Linguistics
Daniels, P. T. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0003 · Cited by 9
A Review of Deep Learning Architectures and Their Application
Kamarudin, J. A. M., Abdullah, A., & Sallehuddin, R. S. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-6502-6_7 · Cited by 9
Estimating a person's age from walking over a sensor floor
Hoffmann, R., Lauterbach, C., Conradt, J., & Steinhage, A. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.11.003 · Cited by 9
Deep Learning Technique-Based Steering of Autonomous Car
Yang, Y., Wu, Z., Xu, Q., & Yan, F. (2018) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026818500062 · Cited by 9
Badarloo, B., & Jafari, F. (2018) · Buildings · doi:10.3390/buildings8090121 · Cited by 9
Overview of Side Channel Cipher Analysis Based on Deep Learning
Song, S., Chen, K., & Zhang, Y. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1213/2/022013 · Cited by 9
Fathi-Kazerooni, S., Kaymak, Y., & Rojas‐Cessa, R. (2019) · IEEE International Conference on Communications workshops · doi:10.1109/iccw.2019.8756709 · Cited by 9
Mamann, Â. T. W. D., da Silva, J. A. G., Binelo, M. O., Scremin, O. B., Kraisig, A. R., Carvalho, I. R., Pereira, L. M., Berlezi, J. D., & Argenta, C. V. (2019) · Journal of Agricultural Studies · doi:10.5296/jas.v7i3.15153 · Cited by 9
Jwa, Y.-J., Guglielmo, G. D., Carloni, L. P., & Karagiorgi, G. S. (2019) · New York Scientific Data Summit (NYSDS) · doi:10.1109/nysds.2019.8909784 · Cited by 9
McCarthy, L., & Miralpeix, I. (2019) · Languages · doi:10.3390/languages5010001 · Cited by 9
On active learning methods for manifold data
Li, H., Castillo, E. D., & Runger, G. C. (2020) · Test · doi:10.1007/s11749-019-00694-y · Cited by 9
Four-Types of IIT-Induced Group Integrity of Plecoglossus altivelis
Niizato, T., Sakamoto, K., Mototake, Y.-I., Murakami, H., Tomaru, T., Hoshika, T., & Fukushima, T. (2020) · Entropy · doi:10.3390/e22070726 · Cited by 9
DOA Estimation of Underwater Acoustic Signals Based on PCA-kNN Algorithm
Liu, Y., Chen, H., & Wang, B. (2020) · International Conference on Computer Information and Big Data Applications (CIBDA) · doi:10.1109/cibda50819.2020.00115 · Cited by 9
Training Multi-Layer Perceptron Using Harris Hawks Optimization
Eker, E., Kayri, M., Ekinci, S., & İzci, D. (2020) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora49412.2020.9152874 · Cited by 9
A Unified Framework for Sparse Online Learning
Zhao, P., Wang, D., Wu, P., & Hoi, S. C. H. (2020) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3361559 · Cited by 9
Incremental predictive clustering trees for online semi-supervised multi-target regression
Osojnik, A., Panov, P., & Džeroski, S. (2020) · Machine Learning · doi:10.1007/s10994-020-05918-z · Cited by 9
Prediction of concrete compressive strength through artificial neural networks
Neira, P., Bennun, L., Pradena, M., & Gómez, J. A. F. (2020) · Journal of the Croatian Association of Civil Engineers · doi:10.14256/jce.2438.2018 · Cited by 9
Bearing Defect Classification Algorithm Based on Autoencoder Neural Network
Lu, M., & Mou, Y. (2020) · Advances in Civil Engineering · doi:10.1155/2020/6680315 · Cited by 9
Case Study: Reconstruction of Runoff Series of Hydrological Stations in the Nakdong River, Korea
Kwak, J., Lee, J., Jung, J., & Kim, H. S. (2020) · Water · doi:10.3390/w12123461 · Cited by 9
Good speciation and endogenous business cycles in a constraint satisfaction macroeconomic model
Sharma, D., Bouchaud, J., Tarzia, M., & Zamponi, F. (2021) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ac014a · Cited by 9
Artificial Intelligence in Clinical Psychology
Tuena, C., Chiappini, M., Repetto, C., & Riva, G. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818697-8.00001-7 · Cited by 9
Learning the Dynamics of Time Delay Systems with Trainable Delays.
Ji, X. A., Molnár, T. G., Avedisov, S. S., & Orosz, G. (2021) · Cited by 9
Li, C., & Wang, C. (2021) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2021/v11i130252 · Cited by 9
Multi-Stage Optimization of Induction Machines Using Methods for Model and Parameter Selection
Nell, M. M., Kubin, A., & Hameyer, K. (2021) · Energies · doi:10.3390/en14175537 · Cited by 9
Increasing the Performance of an IDS using ANN model on the realistic cyber dataset CSE-CIC-IDS2018
Ayachi, Y., Mellah, Y., Berrich, J., & Bouchentouf, T. (2020) · International Symposium on Advanced Electrical and Communication Technologies (ISAECT) · doi:10.1109/isaect50560.2020.9523662 · Cited by 9
Long anisotropic absolute refractory periods with rapid rise times to reliable responsiveness
Sardi, S., Vardi, R., Tugendhaft, Y., Sheinin, A., Goldental, A., & Kanter, I. (2022) · Physical review. E · doi:10.1103/physreve.105.014401 · Cited by 9
Denoised Internal Models: A Brain-inspired Autoencoder Against Adversarial Attacks
Liu, K.-Y., Li, X., Lai, Y.-R., Su, H., Wang, J., Guo, C., Xie, H. B., Guan, J., & Zhou, Y. (2022) · Machine Intelligence Research · doi:10.1007/s11633-022-1375-7 · Cited by 9
Intelligent Dendritic Neural Model for Classification Problems
Xu, W., Jia, D., Zhong, Z., Li, C., & Xu, Z. (2021) · Symmetry · doi:10.3390/sym14010011 · Cited by 9
Kostiukevych, K., Stirenko, S., Gordienko, N., Rokovyi, O., Alienin, O., & Gordienko, Y. G. (2021) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs53288.2021.9660880 · Cited by 9
Identification of Dynamic Systems
(2007) · Springer eBooks · doi:10.1007/1-84628-259-4_7 · Cited by 9
Determining satellite infall times using machine learning
Barmentloo, S., & Cautun, M. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad222 · Cited by 9
Cost-Sensitive Double Updating Online Learning and Its Application to Online Anomaly Detection
Zhao, P., & Hoi, S. C. H. (2013) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972832.23 · Cited by 9
The Hierarchical Accumulation of Knowledge in the Distributed Adaptive Control Architecture
Marcos, E., Ringwald, M., Duff, A., Sánchez-Fibla, M., & Verschure, P. F. M. J. (2013) · Springer eBooks · doi:10.1007/978-3-642-39875-9_10 · Cited by 9
On the Analysis and Control of a Linear Synchronous Servomotor with a Flexible Load
Hirvonen, M. (2006) · Cited by 9
A Brief history of clinical decision support
Greenes, R. A. (2007) · Elsevier eBooks · doi:10.1016/b978-012369377-8/50003-9 · Cited by 9
"New lamps for old!" (generalized multiple-valued neurons)
Moraga, C., & Heider, R. (2003) · IEEE International Symposium on Multiple-Valued Logic (ISMVL) · doi:10.1109/ismvl.1999.779692 · Cited by 9
Blighe, M., & O'Connor, N. E. (2008) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1386352.1386382 · Cited by 9
Heine, J., Land, W. H., & Egan, K. M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-37 · Cited by 9
Just the fax—differentiating voice and fax phone lines using call billing data
Kaplan, H. Y., Strauss, M. J., & Szegedy, M. (1999) · Cited by 9
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