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

Show all 15 references

Sorted by citations

Items 5,501–5,600 of 13,333

A Novel Verification Method of Minimizing Verification Error in AND-Type Flash Arrays
Yu, J., Ryu, D., & Choi, W. Y. (2024) · IEEE Electron Device Letters · doi:10.1109/led.2024.3416185 · Cited by 6
Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice
Ophir, O., Shefi, O., & Lindenbaum, O. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09675-5 · Cited by 6
A Deep Learning Approach to Segment Coastal Marsh Tidal Creek Networks from High-Resolution Aerial Imagery
Dutt, R., Ortals, C. J., He, W., Curran, Z. C., Angelini, C., Canestrelli, A., & Jiang, Z. (2024) · Remote Sensing · doi:10.3390/rs16142659 · Cited by 6
Interplay of traditional methods and machine learning algorithms for tagging boosted objects
Bose, C., Chakraborty, A., Chowdhury, S., & Dutta, S. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01256-6 · Cited by 6
Individual route choice behavior in evacuation considering avoidance and phototropism: An experimental study
Shi, J., Ding, N., Wang, Y., & Zhang, E. (2024) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2024.130030 · Cited by 6
Transformers para previsão de desempenho acadêmico no ensino Fundamental e Médio
Rodrigues, L. S., dos Santos, M., Gomes, C. F. S., Noya, R. C., Goldschmidt, R. R., & de Oliveira, S. B. (2024) · Revista Brasileira de Informática na Educação · doi:10.5753/rbie.2024.3661 · Cited by 6
Visual speech recognition using compact hypercomplex neural networks
Panagos, I.-I., Sfikas, G., & Nikou, C. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2024.09.002 · Cited by 6
A comprehensive analysis of the artificial neural networks model for predicting monkeypox outbreaks
Alnaji, L. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37274 · Cited by 6
Digital Genome and Self-Regulating Distributed Software Applications with Associative Memory and Event-Driven History
Mikkilineni, R., Kelly, W. P., & Crawley, G. (2024) · Computers · doi:10.3390/computers13090220 · Cited by 6
Semantic Mapping of Landscape Morphologies: Tuning ML/DL Classification Approaches for Airborne LiDAR Data
Cappellazzo, M., Patrucco, G., Sammartano, G., Baldo, M., & Spano, A. T. (2024) · Remote Sensing · doi:10.3390/rs16193572 · Cited by 6
Interpretable logical-probabilistic approximation of neural networks
Vityaev, E. E., & Korolev, A. (2024) · Cognitive Systems Research · doi:10.1016/j.cogsys.2024.101301 · Cited by 6
How the technologies behind self‐driving cars, social networks, ChatGPT, and DALL‐E2 are changing structural biology
Bochtler, M. (2024) · BioEssays · doi:10.1002/bies.202400155 · Cited by 6
T-Smade: A Two-Stage Smart Detector for Evasive Spectre Attacks Under Various Workloads
Jiao, J., Wen, R., & Li, Y. (2024) · Electronics · doi:10.3390/electronics13204090 · Cited by 6
Application of machine learning method for energy reconstruction on space based high granularity calorimeter
Liao, C.-L., Quan, Z., Dong, Y., Xu, M., Zhang, C., Wang, J., Yang, X., Wu, Q., Sun, J., Liu, X., et al. (2024) · Experimental Astronomy · doi:10.1007/s10686-024-09957-5 · Cited by 6
Partially unitary learning
Belov, M. G., & Malyshkin, V. G. (2024) · Physical review. E · doi:10.1103/physreve.110.055306 · Cited by 6
Brain-inspired learning rules for spiking neural network-based control: a tutorial
Lee, C., Park, Y., Yoon, S., Lee, J., Cho, Y., & Park, C. (2024) · Biomedical Engineering Letters · doi:10.1007/s13534-024-00436-6 · Cited by 6
Tools for Artificial Intelligence
Liu, Z. “. (2025) · Springer eBooks · doi:10.1007/978-3-031-75953-6_2 · Cited by 6
Counterfactual synthetic minority oversampling technique: solving healthcare's imbalanced learning challenge
Almeida, G., & Bação, F. (2025) · Data Science and Management · doi:10.1016/j.dsm.2025.01.006 · Cited by 6
VireNet-SSD: object detection model for resource-constrained applications based on self-organized operational neural networks
Kamath, V., & Renuka, A. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-10986-0 · Cited by 6
Topology-Based Node-Level Membership Inference Attacks on Graph Neural Networks
Guan, F., Zhu, T., Zhou, W., & Yu, P. S. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3558855 · Cited by 6
Strategic implications of cognitive computing in IS: addressing AI fragmentation through knowledge similarity transformation
Tuczek, M., Degirmenci, K., Song, Y., Desouza, K. C., Breitner, M. H., & Watson, R. T. (2025) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2025.101908 · Cited by 6
An online fine-tuning method for modelling online SOH estimation under multi-stage constant current charging protocol
Li, H., Ding, R., Xi, Y., Zhang, S., & Zhang, X. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.117233 · Cited by 6
Stress, strain, or displacement? A novel machine learning based framework to predict mixed mode I/II fracture load and initiation angle
Mirzaei, A. M. (2025) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2025.111349 · Cited by 6
Sensation and Perception
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0005 · Cited by 6
Adaptation of RBM Learning for Intel MIC Architecture
Olas, T., Mleczko, W. K., Nowicki, R. K., Wyrzykowski, R., & Krzyżak, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19324-3_9 · Cited by 6
Dendro-plexing of Single Input Spikes via Multiple Synaptic Contacts Can Enhance Cortical Neuron Computation and Reduce Axonal Wiring
Beniaguev, D., Shapira, S., Segev, I., & London, M. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.0839-24.2026 · Cited by 6
Selection Versus Instruction: Use of Computer Models to Compare Brain Theories
Reeke, G. N. (1994) · International review of neurobiology · doi:10.1016/s0074-7742(08)60249-4 · Cited by 6
Classical Neural Networks
Kussul, E. M., Baidyk, T., & Wunsch, D. C. (2009) · Springer eBooks · doi:10.1007/978-3-642-02535-8_2 · Cited by 6
Acoustic Modelling Using Continuous Rational Kernels
Layton, M., & Gales, M. (2006) · Machine learning for signal processing ... · doi:10.1109/mlsp.2005.1532893 · Cited by 6
Survey on Kernel-Based Relation Extraction
Jung, H., Choi, S.-P., Lee, S., & Song, S.-K. (2012) · InTech eBooks · doi:10.5772/51005 · Cited by 6
Learning in Artificial Neural Networks
Braga, A. P. (2011) · Springer eBooks · doi:10.1007/978-0-85729-868-3_8 · Cited by 6
Performance Comparison of SAS-Multilayer Perceptron and Wavelet-Multilayer Perceptron Models in Terms of Daily Streamflow Prediction
Altunkaynak, A., & Nigussie, T. A. (2015) · Journal of Hydrologic Engineering · doi:10.1061/(asce)he.1943-5584.0001263 · Cited by 6
NeuroFURG: uma ferramenta de apoio ao ensino de Redes Neurais Artificiais
Madsen, C. A. B. C. W., & Adamatti, D. F. (2011) · Revista Brasileira de Informática na Educação · doi:10.5753/rbie.2011.19.02.14 · Cited by 6
Clustering high dimensional data
Kriegel, H., & Ntoutsi, E. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2641190.2641192 · Cited by 6
Learning topological image transforms using cellular simultaneous recurrent networks
Anderson, J. K., & Iftekharuddin, K. M. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6706723 · Cited by 6
Co-authorship proximity of A. M. Turing Award and John von Neumann Medal winners to the disciplinary boundaries of computer science
Fields, C. (2015) · Scientometrics · doi:10.1007/s11192-015-1575-9 · Cited by 6
Transparallel mind: classical computing with quantum power
van der Helm, P. A. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-015-9429-7 · Cited by 6
A New Dose Model for Assessment of Health Risk Due to Contaminants in Air
Rajkumar, T., Guesgen, H. W., Robinson, S. M., & Fisher, G. (2000) · Journal of the Air & Waste Management Association · doi:10.1080/10473289.2000.10463993 · Cited by 6
TeamSkill and the NBA
DeLong, C., Terveen, L., & Srivastava, J. (2013) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2492517.2492628 · Cited by 6
A Multiscale Scheme for Approximating the Quantron's Discriminating Function
Connolly, J.-F., & Labib, R. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2009.2022979 · Cited by 6
Document clustering using the LSI subspace signature model
Zhu, W. Z., & Allen, R. B. (2013) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.22623 · Cited by 6
Three Guidelines of Online Learning for Large-Scale Visual Recognition
Ushiku, Y., Hidaka, M., & Harada, T. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.457 · Cited by 6
Beyond Banditron: A Conservative and Efficient Reduction for Online Multiclass Prediction with Bandit Setting Model
Chen, G., Chen, G., Zhang, J., Chen, S., & Zhang, C. (2009) · Proceedings · doi:10.1109/icdm.2009.36 · Cited by 6
Surface Defect Isolation in Ceramic Tile Based on Texture Feature Analysis Using Radon Transform and FCM
Mansoory, M. S., Hojjat Tajik, N., & Pashna, M. (2009) · International Conference on Signal Processing Systems (ICSPS) · doi:10.1109/icsps.2009.66 · Cited by 6
Data Mining of Medical Datasets with Missing Attributes from Different Sources
Sajja, S. (2010) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 6
On-line Recognition of Handwritten Mathematical Symbols
Thoma, M., Kilgour, K., Stüker, S., & Waibel, A. (2014) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/1000048047 · Cited by 6
Neuron Clustering for Mitigating Catastrophic Forgetting in Supervised and Reinforcement Learning
Goodrich, B. (2015) · Cited by 6
Exponential mean-square H∞ filtering for arbitrarily switched neural networks with missing measurements
Che, Y., Shu, H., & Liu, Y. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.02.019 · Cited by 6
Dependency parsing with second-order feature maps and annotated semantic information
Ciaramita, M., & Attardi, G. (2007) · International Workshop/Conference on Parsing Technologies · doi:10.3115/1621410.1621427 · Cited by 6
Understanding Neural Networks
Wilamowski, B. M. (2018) · Intelligent Systems · doi:10.1201/9781315218427-5 · Cited by 6
Comparison of online adaptive learning algorithms for myoelectric hand control
Zhang, Y., Wang, Z., Zhang, Z. D., Fang, Y., & Liu, H. (2016) · Conference on Human System Interactions · doi:10.1109/hsi.2016.7529611 · Cited by 6
Neuromodeling and Natural Optimization of Nonlinear Devices and Circuits
Silva, P. H. D. F., Cruz, R. M. S., & D’Assunção, A. G. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-018-1.ch015 · Cited by 6
Classification of textures in satellite image with Gabor filters and a multi layer perceptron with back propagation algorithm obtaining high accuracy
Beluco, A., Engel, P. M., & Beluco, A. (2015) · International journal of energy and environment · doi:10.5935/2076-2909.20150001 · Cited by 6
Application of artificial neural network in medical geochemistry
Fajčíková, K., Stehlíková, B., Cvečková, V., & Rapant, S. (2017) · Environmental Geochemistry and Health · doi:10.1007/s10653-017-9944-3 · Cited by 6
Noam Chomsky's Contribution to Linguistics
Freidin, R. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0020 · Cited by 6
Speech Recognition Using Feed Forward Neural Network and Principle Component Analysis
Momo, N. S., Abdullah, & Uddin, J. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-67934-1_20 · Cited by 6
Statistical Methods for Scene and Event Classification
McFee, B. (2017) · Springer eBooks · doi:10.1007/978-3-319-63450-0_5 · Cited by 6
The Development of Intelligent Patient-Centric Systems for Health Care
Caronongan, A. P., Gorgui-Naguib, H., & Naguib, R. N. G. (2018) · Healthcare delivery in the information age · doi:10.1007/978-3-319-72287-0_22 · Cited by 6
Development of a Workshop About Artificial Intelligence for Schools
Estévez, J., Elizetxea, X., & Garate, G. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-94120-2_51 · Cited by 6
Intelligent Control with Neural Networks
Popović, D. Ž. (2000) · Elsevier eBooks · doi:10.1016/b978-012646490-0/50021-4 · Cited by 6
Secret Agents
Apprich, C. (2018) · Digital Culture & Society · doi:10.14361/dcs-2018-0104 · Cited by 6
Deception Detection and Analysis in Spoken Dialogues based on FastText
Hosomi, N., Sakti, S., Yoshino, K., & Nakamura, S. (2018) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659614 · Cited by 6
Self-planting: digging holes in rough landscapes
Sharma, D., Bouchaud, J., Tarzia, M., & Zamponi, F. (2019) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ab4800 · Cited by 6
Generic predictions of output probability based on complexities of inputs and outputs
Dingle, K., Valle-Pérez, G., & Louis, A. A. (2020) · Scientific Reports · doi:10.1038/s41598-020-61135-7 · Cited by 6
Exploiting Randomness in Deep Learning Algorithms
Langroudi, H. F., Merkel, C. E., Syed, H., & Kudithipudi, D. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852192 · Cited by 6
The Revival of Virtue Ethics
Baril, A., & Hazlett, A. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.019 · Cited by 6
Brain-Inspired Memristive Neural Networks for Unsupervised Learning
Ielmini, D., & Milo, V. (2019) · Springer eBooks · doi:10.1007/978-3-319-76375-0_17 · Cited by 6
Design of Convolutional Neural Network Based on Tree Fork Module
Yang, L., Zeng, S., Zhou, Y., Pan, B., Feng, Y., & Li, D. (2019) · International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) · doi:10.1109/dcabes48411.2019.00008 · Cited by 6
Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability DomainIncluded
Berenger, F., & Yamanishi, Y. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b01075 · Cited by 6
Theoretical and hypothetical pathways to real-time neuromorphic AGI/post-AGI ecosystems
Dambrot, S. M. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.02.122 · Cited by 7
Applying Convolutional Neural Networks For Image Detection
Karam, A.-F., Embaby, M., El-Kady, H., Abdel-Hafeez, S., Nabil, G., & Mohammed, A. (2019) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets48225.2019.9069750 · Cited by 6
Software-Level Accuracy Using Stochastic Computing With Charge-Trap-Flash Based Weight Matrix
Bhatt, V., Shrivastava, S., Chavan, T., & Ganguly, U. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206631 · Cited by 6
A New Approach for Navigation and Traffic Signs Indication Using Map Integrated Augmented Reality for Self-Driving Cars
Deore, H., Agrawal, A., Jaglan, V., Nagpal, P., & Sharma, M. M. (2020) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v21i3.1742 · Cited by 6
Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms
Leema, N., Nehemiah, K. H., R., E. C. V., & Kannan, A. (2020) · International Journal of Operations Research and Information Systems · doi:10.4018/ijoris.2020100104 · Cited by 6
Neural Radio in DS-UWB IoT Applications
Akhmedov, R. D. (2020) · IEEE Ukrainian Microwave Week (UkrMW) · doi:10.1109/ukrmw49653.2020.9252611 · Cited by 6
Organic Memristive Devices for Neuromorphic Applications
Battistoni, S. (2020) · BioNanoScience · doi:10.1007/s12668-020-00808-z · Cited by 6
An ANN experiment on the Indian economy: can the change in pollution generate an increase or decrease in GDP acceleration?
Mele, M., Nieddu, L., Abbafati, C., & Quarto, A. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-13182-4 · Cited by 6
Bioinspired Robotic Vision with Online Learning Capability and Rotation‐Invariant Properties
Berco, D., & Ang, D. S. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100025 · Cited by 6
A low-power embedded system for fire monitoring and detection using a multilayer perceptron
Papaioannou, A., Verikios, P., Kouzinopoulos, C., Ioannidis, D., & Tzovaras, D. K. (2021) · IEEE Sensors Applications Symposium (SAS) · doi:10.1109/sas51076.2021.9530090 · Cited by 6
Detecting and correcting automatic speech recognition errors with a new model
Arslan, R. S., Barışçı, N., Arıcı, N., & Koçer, S. (2021) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.3906/elk-2010-117 · Cited by 6
NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks
Siciliano, F., Bucarelli, M. S., Tolomei, G., & Silvestri, F. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892367 · Cited by 6
Semiautomatic Determination of the Geological Strength Index Using SfM and ANN Techniques
Zeni, M. A., Peroni, R. D. L., & dos Santos, F. A. G. (2021) · International Journal of Geomechanics · doi:10.1061/(asce)gm.1943-5622.0002203 · Cited by 6
Characterization of multiscale logic operations in the neural circuits
Woo, J., Choi, K., Kim, S. H., Han, K., & Choi, M. Y. (2021) · Frontiers in Bioscience-Landmark · doi:10.52586/4983 · Cited by 6
Invariant surface elastic properties in FCC metals and their correlation to bulk properties revealed by machine learning methods
Chen, X., Dingreville, R., Richeton, T., & Berbenni, S. (2022) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2022.104852 · Cited by 6
Electronic model of a dubois fuzzy integration neuron
S., J. L. P., M, A. G., C., F. C., & V., A. M. (2009) · Journal of Applied Research and Technology · doi:10.22201/icat.16656423.2009.7.01.511 · Cited by 6
Deep networks may capture biological behavior for shallow, but not deep, empirical characterizations
Neri, P. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.04.023 · Cited by 6
Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid‐19 Pandemic
Bhattacharjee, A., Vishwakarma, G. K., Gajare, N., & Singh, N. (2022) · Regional Science Policy & Practice · doi:10.1111/rsp3.12555 · Cited by 6
Deep Learning Framework Fuzzing Based on Model Mutation
Shen, X., Zhang, J., Wang, X., Yu, H., & Sun, G. (2021) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc53577.2021.00059 · Cited by 6
Intelligent Edge-Embedded Technologies for Digitising Industry
Vermesan, O., & Nava, M. D. (2022) · doi:10.13052/rp-9788770226103 · Cited by 7
Improvement of the subcooled boiling model using a new net vapor generation correlation inferred from artificial neural networks to predict the void fraction profiles in the vertical channel
Lee, T. B., & Jeong, Y. H. (2022) · Nuclear Engineering and Technology · doi:10.1016/j.net.2022.07.031 · Cited by 6
Application of Machine Learning to Environmental DNA Metabarcoding
Kimura, M., Yamanaka, H., & Nakashima, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3207173 · Cited by 6
Structured random receptive fields enable informative sensory encodings
Pandey, B., Pachitariu, M., Brunton, B. W., & Harris, K. D. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010484 · Cited by 6
Adaptive supervised learning on data streams in reproducing kernel Hilbert spaces with data sparsity constraint
Wang, H., Li, Q., & Liu, Y. (2022) · Stat · doi:10.1002/sta4.514 · Cited by 6
Neural Network-Augmented Locally Adaptive Linear Regression Model for Tabular Data
Munkhdalai, L., Munkhdalai, T., Pham, V.-H., Hong, J., Ryu, K. H., & Theera‐Umpon, N. (2022) · Sustainability · doi:10.3390/su142215273 · Cited by 6
Comparison of discriminant methods and deep learning analysis in plant taxonomy: a case study of Elatine
Łysko, A., Popiela, A. A., Forczmański, P., Attila Molnár, V., Lukács, B. A., Barta, Z., Maćków, W., & Wolski, G. J. (2022) · Scientific Reports · doi:10.1038/s41598-022-24660-1 · Cited by 6
Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing
Fu, H., Yang, Y., Liu, Y., Lin, J., Harrison, E., Mishra, V. K., & Kuo, C. ‐. J. (2022) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipaasc55919.2022.9980233 · Cited by 6
AI in Medicine: Some Pertinent History
Shortliffe, E. H., & Shah, N. H. (2022) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-09108-7_2 · Cited by 6
Cortical Columns Computing Systems: Microarchitecture Model, Functional Building Blocks, and Design Tools
Shen, J. P., & Nair, H. (2023) · Artificial intelligence · doi:10.5772/intechopen.110252 · Cited by 6
Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer
Mattoo, F. U. H. A., Nawaz, T. H., Saleem, M. M., Khan, U. S., & Hamza, A. (2023) · Micromachines · doi:10.3390/mi14040817 · Cited by 6
Please Report Your Compute
Sevilla, J., Ho, A., & Besiroglu, T. (2023) · Communications of the ACM · doi:10.1145/3563035 · Cited by 6

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