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The perceptron: A probabilistic model for information storage and organization in the brain.
Abstract
If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the
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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
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Items 9,001–9,100 of 13,333
Artificial neural network with complex weight and its training
Shin, Y.-C., & Sridhar, R. (1992) · IEEE Symposium on Neuroinformatics and Neurocomputers · doi:10.1109/rnns.1992.268552 · Cited by 1
Advanced online learning for natural language processing
Crammer, K. (2008) · doi:10.3115/1564169.1564173 · Cited by 1
Oil Reservoir Properties Estimation by Fuzzy-Neural Networks
Ho, T. L., & Ehara, S. (2007) · QIR (Kyushu University Institutional Repository) (Kyushu University) · Cited by 1
An example of generating Internet-based course material
Fischer, I., Wiest, S., & Zell, A. (2000) · Cited by 1
Naoe, K., Sasaki, H., & Takefuji, Y. (2009) · International Conference of Soft Computing and Pattern Recognition (SoCPaR) · doi:10.1109/socpar.2009.109 · Cited by 1
Aksin, D. Y., Aras, S., & Göknar, İ. C. (2011) · DergiPark (Istanbul University) · Cited by 1
Teichert, J., & Malaka, R. (2000) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2000.861315 · Cited by 1
Neuronale Informationsverarbeitung für Fahrerassistenzsysteme
Handmann, U. (2000) · Cited by 1
Evolving an emotion-based neural control architecture
Ackovska, N., Bozinovski, S., & Božinovska, L. (2008) · IEEE SoutheastCon · doi:10.1109/secon.2008.4494328 · Cited by 1
Machine Learning in Web Mining
Fakultet, C., & Za, D. (2006) · Cited by 1
UNCERTAINTY IN NEURAL AND PHYSICAL PROCESSES
Jaeger, S. (2006) · Cited by 1
Approximate discriminative training of graphical models
Jancsary, J. (2012) · reposiTUm (TU Wien) · Cited by 1
Shape-texture features for the VHSR satellite images classification using the MLP neural net.
Mahi, H., & Kaouadji, M. (2014) · doi:10.12760/01-2014-2-04 · Cited by 1
Construction of Meanings in Living and Artificial Agents
Matematiky, F., & Informatiky, A. M. A. (2007) · Cited by 1
An Implementation of Deep Belief Networks Using Restricted Boltzmann Machines in Clojure
Sims, J. C. (2016) · doi:10.23860/thesis-sims-james-2016 · Cited by 1
Rainfall Estimation And Real-time ReservoirOperation With Neural Network And FuzzyInference
Kojiri, T., Ito, K., & Sakakima, S. (1970) · WIT transactions on information and communication technologies · doi:10.2495/ai940041 · Cited by 1
Hybrid intelligent systems for pattern recognition and signal processing
Youssif, R. S., & Purdy, C. (2004) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 1
Comparative Anatomy of the Cerebellum
Barlow, J. S. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511529771.003 · Cited by 1
A neural network approach to the prediction of violence
Stanners, R. F., & Gordon, J. S. (1992) · Cited by 1
On the use of Convolutional Neural Networks for Pedestrian Detection
Masip, S., & López, A. M. (2015) · Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) · Cited by 1
Learning with Incidental Supervision
Klementiev, A. (2009) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 1
Hierarchical Adult Image Rating System
Kim, W., Lee, H., & Yoon, K. (2006) · Springer eBooks · doi:10.1007/978-3-540-37258-5_110 · Cited by 1
Goldner, D. (1998) · DESY (CERN, DESY, Fermilab, IHEP, and SLAC) · doi:10.17877/de290r-15862 · Cited by 1
Computational approaches to linguistic consensus
Gasser, L., & Wang, J. (2006) · Cited by 1
Investigations into the capabilities of the SDM and combining CMAC with PURR-PUSS.
Ryan, S. W. (1996) · UC Research Repository (University of Canterbury) · doi:10.26021/3234 · Cited by 1
Avaliação e Melhoria da Margem de Colapso de Tensão Utilizando Rede Neural Artificial
Gouvêa, M. M., & Terra, L. D. B. (2016) · Congresso Brasileiro de Redes Neurais (CBRN) · doi:10.21528/cbrn1999-030 · Cited by 1
A cerebellar long-term depression update
Linden, D. J. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081942 · Cited by 1
Cellular mechanisms of long-term depression: From consensus to open questions
Crépel, F. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081954 · Cited by 1
Nitric oxide is involved in cerebellar long-term depression
Okada, D. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081814 · Cited by 1
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Smith, A. M. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00082005 · Cited by 1
Which cerebellar cells contribute to extracellular cGMP?
Kiedrowski, L. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081772 · Cited by 1
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Kano, M. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081759 · Cited by 1
Constructing a theory of cerebellar function in limb movement control is premature
Jaeger, D. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081747 · Cited by 1
O’Mara, S. M. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081826 · Cited by 1
Control of complex biomechanical systems
Fain, M. (2013) · cIRcle (University of British Columbia) · doi:10.14288/1.0052179 · Cited by 1
Molecules involved in cerebellar long-term depression (LTD) and mutant mice defective in it
Hirano, T. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081711 · Cited by 1
Smolensky's proper treatment of connectionism: Having it both ways
Goel, V. (1990) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00079413 · Cited by 1
How can the cerebellum match “error signal” and “error correction”?
Dufossé, M. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081590 · Cited by 1
Hepp, K. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081693 · Cited by 1
Landslide stability analysis based on artificial neural networks
Kaya, T., Gelıslı, K., & Babacan, A. E. (2012) · doi:10.1190/ist092012-001.135 · Cited by 1
Sensorimotor learning in structures “upstream” from the cerebellum
van Donkelaar, P. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081905 · Cited by 1
Plasticity of cerebro-cerebellar interactions in patients with cerebellar dysfunction
Wessel, K. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081930 · Cited by 1
Deep Neural Network Approach for Single Channel Speech Enhancement Processing
Li, D. (2016) · uO Research (University of Ottawa) · doi:10.20381/ruor-5532 · Cited by 1
Hardware-Accelerated neural network
Piñol, L. E. (2016) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · Cited by 1
Concept of a Robust & Training-free Probabilistic System for Real-time Intention Analysis in Teams
Giersich, M. (2009) · Ros Dok Rostocker Dokumentenserver (Universitätsbibliothek Rostock) · doi:10.18453/rosdok_id00000585 · Cited by 1
Intrinsically Evolvable Artificial Neural Networks
Merchant, S. G. (2007) · Cited by 1
Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139924801.013 · Cited by 1
General Systems Theory and Biosystems: An Introduction
Balis, G. U. (1978) · Elsevier eBooks · doi:10.1016/b978-0-409-95009-0.50012-7 · Cited by 1
The Quest for the Intelligent Machine
Durkin, J. (2002) · Intelligent Systems · doi:10.1201/9781420040814.ch1a · Cited by 1
Individual Based Spatial Models With Evolved Fish Behaviour
Huse, G., & Giske, J. (1998) · NORA - Norwegian Open Research Archives · doi:10.17895/ices.pub.26807005 · Cited by 1
The Principles of Neural Networks
Denz, C. (1998) · Vieweg+Teubner eBooks · doi:10.1007/978-3-663-12272-2_2 · Cited by 1
Touch, M. (2016) · DukeSpace (Duke University) · Cited by 1
Functions of Multi-Layered Neural Networks and their Fast Learning Algorithm
Asakawa, K., Watanabe, N., Kawamura, A., Masuoka, R., Tanahashi, J.-I., & Yamada, H. (1990) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss1987.110.3_141 · Cited by 1
A system design approach to neuromorphic classifiers
Ramakrishnan, S. (2013) · SMARTech Repository (Georgia Institute of Technology) · Cited by 1
Wakuya, H., & Shida, K. (1996) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss1987.116.9_1051 · Cited by 1
Minimal supervision for language learning: bootstrapping global patterns from local knowledge
Roth, D., & Connor, M. (2011) · Cited by 1
Cone beam filtering using artificial neural networks
Munley, M. T., Floyd, C. E., Tourassi, G. D., & Coleman, R. E. (2002) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.1991.259307 · Cited by 1
A Multiclass Kernel Perceptron Algorithm
Xu, J., & Zhang, X. (2006) · International Conference on Neural Networks and Brain · doi:10.1109/icnnb.2005.1614728 · Cited by 1
Improving information retrieval using a morphological neural network model
Dankel, D. D., & Roberson, C. (2008) · Cited by 1
Novel Applications of Image-Processing Techniques to Particle Physics
Merz, G. W. (2015) · The Knowledge Bank (The Ohio State University) · Cited by 1
Learning to Rank: Online Learning, Statistical Theory and Applications.
Chaudhuri, S. (2016) · Deep Blue (University of Michigan) · Cited by 1
Carpenter, G. A., Gjaja, M. N., Gopal, S., Markuzon, N., & Woodcock, C. E. (1996) · OpenBU (Boston University) · Cited by 1
Machine learning based compact photonic structure design for strong light confinementPreprint
Turduev, M., Latifoğlu, Ç., Giden, İ. H., & Hanay, Y. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.00260 · Cited by 1
A Rate of Convergence for Learning Theory with Consensus
Gregory, J. (2015) · VTechWorks (Virginia Tech) · Cited by 1
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Casals, S. G. (2014) · theses.fr (ABES) · Cited by 1
Did you know? Modelling vision: computational science for understanding human visual perception
Mrowka, R., Freytag, A., & Reuter, S. (2017) · Acta Physiologica · doi:10.1111/apha.12875 · Cited by 1
Hierarchical self-organizing learning systems for embodied intelligence
Starzyk, J. A., & Liu, Y. (2009) · Cited by 1
Intelligent Speed Adaptation in Curves for Autonomous Vehicles
Partouche, D. (2006) · HAL · Cited by 1
Large deviations for the dynamics of heterogeneous neural networks
Cabana, T. (2016) · theses.fr (ABES) · doi:10.70675/4947ebb7z5fd1z4bafz99ffzbf22d361d6fc · Cited by 1
HISTORY AND DEVELOPMENT OF NEURAL NETWORKS
Harston, C. T., & Maren, A. J. (1990) · Elsevier eBooks · doi:10.1016/b978-0-12-546090-3.50006-1 · Cited by 1
Translation: the Intertranslatability of Languages; Translation and Language Teaching
Malmkjær, K. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0032 · Cited by 1
Bowden, R. J. J., Zhu, J., & Cho, J. S. (2006) · doi:10.13140/rg.2.2.28234.08649 · Cited by 1
Dea, J. (2017) · Deep Blue (University of Michigan) · Cited by 1
Enaliev, A. Z. (2016) · doi:10.20868/upm.thesis.43029 · Cited by 1
Towards co-evolution of fitness predictors and Deep Neural NetworksPreprint
Funika, W., & Koperek, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.00119 · Cited by 1
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Bourreau, L. (2017) · theses.fr (ABES) · doi:10.70675/28d78aa6zf361z4e6czbcbez96485f86b159 · Cited by 1
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Vega, M. A. Á. (2018) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2018.tde-06032018-161351 · Cited by 1
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Kovács, G. (2018) · doi:10.14232/phd.4108 · Cited by 1
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Stenberg, C. E. (2017) · NORA - Norwegian Open Research Archives · Cited by 1
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Jain, S. K. (2008) · Cited by 1
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Blanchette, G. (2018) · Otago University Research Archive (University of Otago) · Cited by 1
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Lan, X., Guo, X., & Barner, K. E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.04356 · Cited by 1
Strategy for construction of polymerized volume data sets
Aragonda, P. (2006) · OakTrust (Texas A&M University Libraries) · Cited by 1
Aung, A. M. (2018) · Digital WPI · Cited by 1
Shapiro, D. (2017) · uO Research (University of Ottawa) · doi:10.20381/ruor-20552 · Cited by 1
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Qi, F. H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/535724 · Cited by 1
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Metadata
- Authors
- Rosenblatt, F.
- Corresponding author
- Rosenblatt, F.
- Venue
- Psychological Review
- Published
- 1958
- Volume
- 65
- Issue
- 6
- Pages
- 386-408
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2040870580
- Semantic Scholar
- 5d11aad09f65431b5d3cb1d85328743c9e53ba96
- Cited by
- 12,280
- References
- 15
- Citations by year
- 2026: 659 · 2025: 1019 · 2024: 969 · 2023: 933 · 2022: 945 · 2021: 965 · 2020: 898 · 2019: 826 · 2018: 494 · 2017: 411 · 2016: 318 · 2015: 313 · 2014: 282 · 2013: 332 · 2012: 255
- FWCI
- 14.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- perceptron, probabilistic models, information storage, memory, perceptual identification, generalization, memory retrieval
- MeSH terms
- Brain, Humans, Perception, Models, Statistical, Information Storage and Retrieval, Neural Networks, Computer
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