The perceptron: A probabilistic model for information storage and organization in the brain.

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

On Modeling Protein Superfamilies with Low Primary Sequence Conservation

Scott, S., Ji, H., Wen, P., Fomenko, D. E., & Gladyshev, V. N. (2003) · Lincoln (University of Nebraska) · Cited by 4

Multiple sensors and Artificial Neural Networks in a cognitive technical system for laser welding

Wersborg, I. S. G., Schorp, K., Bautze, T., & Diepold, K. J. (2009) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2009.5416755 · Cited by 4

A Novel Ensemble Approach Based on Balanced Perceptrons Applied to Microarray Datasets

Enes, K. B., Villela, S. M., & Neto, R. F. (2015) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2015.37 · Cited by 4

Differentiating models of associative learning: Reorientation, superconditioning, and the role of inhibition.

Dupuis, B., & Dawson, M. R. W. (2013) · Journal of Experimental Psychology Animal Behavior Processes · doi:10.1037/a0032174 · Cited by 4

Sensitivity Analysis on Neural Network Algorithm for Primary Superheater Spray Modeling

Mazalan, N. A., Malek, A. A., Wahid, M. B. A., & Mailah, M. (2016) · Heat Transfer Engineering · doi:10.1080/01457632.2016.1195134 · Cited by 4

System and Circuit Design for Biologically-Inspired Intelligent Learning

Temel, T. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-018-1 · Cited by 4

Online Prediction for Forex with an Optimized Experts Selection Model

Zhu, J., Yang, J. M., Xiao, J., Huang, C., Zhao, G., & Tang, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45814-4_30 · Cited by 4

The Brain in Silicon: History, and Skepticism

Plebe, A., & Grasso, G. M. (2016) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-47286-7_19 · Cited by 4

Have You Lost the Thread? Discovering Ongoing Conversations in Scattered Dialog Blocks

Zanzotto, F. M., & Ferrone, L. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2885501 · Cited by 4

European Linguistics since Saussure

Graffi, G. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0021 · Cited by 4

Deep Saliency: Prediction of Interestingness in Video with CNN

Chaabouni, S., Benois‐Pineau, J., Zemmari, A., & Amar, C. B. (2017) · Springer eBooks · doi:10.1007/978-3-319-57687-9_3 · Cited by 4

A comprehensive study on predicting river runoff

Tran, T. T. T., Giang, N. N. H., Duong, H. N., Nguyen, H. P. N., Hoài, T. V., & Nghi, V. V. (2017) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2017.8119467 · Cited by 4

ByteWise: A case study in neural network obfuscation identification

Jones, L., Christman, D., Bănescu, S., & Carlisle, M. C. (2018) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2018.8301720 · Cited by 4

Identifying the Responsible Group for Extreme Acts of Violence Through Pattern Recognition

Hashemi, M., & Hall, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91716-0_47 · Cited by 4

Frontiers of Computer Vision Technologies on Real Estate Property Photographs and Floorplans

Kiyota, Y. (2021) · New frontiers in regional science: Asian perspectives · doi:10.1007/978-981-15-8848-8_23 · Cited by 4

Beginner's guide to neural networks for the MNIST dataset using MATLAB

Kim, B., & Park, Y. (2018) · Korean Journal of Mathematics · doi:10.11568/kjm.2018.26.2.337 · Cited by 4

Applications of Deep Learning in Biomedicine

Wang, S., & Huang, T. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11507-7 · Cited by 4

Turing: The Great Unknown

de Sojo, Á. A., Lara, J. A., Lizcano, D., Martínez, M.-A., Pazos, J., & Esteban, F. D. D. L. P. (2019) · Foundations of Science · doi:10.1007/s10699-019-09596-6 · Cited by 4

Towards Automatic Construction of Multi-Network Models for Heterogeneous Multi-Task Learning

Garciarena, U., Mendiburu, A., & Santana, R. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3434748 · Cited by 4

[An Overview of the Application of Artificial Neural Networks in Lung Cancer Research].

Zhu, X., Chen, N., Liu, L., & Pu, Q. (2019) · Europe PMC (PubMed Central) · doi:10.3779/j.issn.1009-3419.2019.04.08 · Cited by 4

Study on Prediction of Similar Typhoons through Neural Network Optimization

Kim, Y.-J., Kim, T.-W., Yoon, J.-S., & Kim, I. (2019) · Journal of Ocean Engineering and Technology · doi:10.26748/ksoe.2019.065 · Cited by 4

Identification of Plasma Current Center by Neural Network Inference in EAST

Zhu, Z., Li, J., Xiao, B., Xu, X. Q., Yang, F., & Guo, Y. X. (2019) · IEEE Transactions on Plasma Science · doi:10.1109/tps.2019.2951367 · Cited by 4

Research and Application of Process Object Intelligent Learning Modeling

Li, Q., Yin, F., Su, Y., & Sun, J. (2019) · IEEE Sustainable Power and Energy Conference (iSPEC) · doi:10.1109/ispec48194.2019.8974889 · Cited by 4

Development of Traffic Speed Prediction Model Reflecting Spatio-temporal Impact based on Deep Neural Network

Dept. of Transportation Eng, U. O. S., Kim, Y.-C., Kim, J., Han, Y., Kim, J.-J., & Hwang, J. (2020) · The Journal of The Korea Institute of Intelligent Transport Systems · doi:10.12815/kits.2020.19.1.1 · Cited by 4

Maschinelles Lernen: Deep Learning mit neuronalen Netzen

Ernst, H., Schmidt, J., & Beneken, G. (2020) · Springer eBooks · doi:10.1007/978-3-658-30331-0_18 · Cited by 4

Prediction of Air Quality Indices by Neural Networks and Fuzzy Inference Systems – The Case of Pardubice Microregion

Hájek, P., & Olej, V. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-41013-0_31 · Cited by 4

Psychosocial risk management

Fernandes, A., Figueiredo, M., Avidos, L. M. M. A., Ribeiro, J., Vicente, D., Neves, J., & Vicente, H. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.09.069 · Cited by 4

Against neuroclassicism: On the perils of armchair neuroscience

Morgan, A. (2020) · Mind & Language · doi:10.1111/mila.12304 · Cited by 4

Networks of spike oscillators and their application for creating autonomous intelligent devices

Ванаг, В. К. (2021) · Physics-Uspekhi · doi:10.3367/ufne.2021.05.038978 · Cited by 4

Deep Learning

Nisbet, R. A., Miner, G. D., & Yale, K. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-416632-5.00019-0 · Cited by 4

Reduced Stopping Distance by Radar-Vision Fusion

(2007) · Springer eBooks · doi:10.1007/978-3-540-71325-8_3 · Cited by 4

Overview of Neural Network

., C. (2022) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-4851 · Cited by 4

Artificial neural network

Ghosh, A., Das, S. J., & Saha, B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15395-2.00008-9 · Cited by 4

Global and Regional Features

Krig, S. (2014) · Apress eBooks · doi:10.1007/978-1-4302-5930-5_3 · Cited by 4

Computing and Complexity — Networks, Nature and Virtual Worlds

Green, D. G., & Leishman, T. G. (2011) · Elsevier eBooks · doi:10.1016/b978-0-444-52076-0.50004-3 · Cited by 4

Mise en oeuvre de réseaux de neurones pour la modélisation de cinétiques réactionnelles en vue de la transposition batch/continu

Ammar, M. Y. (2007) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 4

Abordagem neurofuzzy para modelagem de sistemas dinamicos não lineares

Hell, M. (2008) · doi:10.47749/t/unicamp.2008.480536 · Cited by 4

Analysing the behaviour of neural networks

Breutel, S. (2004) · Cited by 4

Information processing using a model of associative memory

Nakano, K., & Nagumo, J. (1971) · International Joint Conference on Artificial Intelligence · Cited by 4

Learning Expert Knowledge and Theorem Proving

Kodratoff, Y. (1986) · Informatik-Fachberichte · doi:10.1007/978-3-642-71385-9_17 · Cited by 4

Arquitetura híbrida inteligente para navegação autônoma de robôs

Calvo, R. (2007) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2007.tde-15062007-161911 · Cited by 4

Integrated multi-model framework for adaptive multiple time-series analysis and modelling

Widiputra, H. (2011) · Tuwhera (Auckland University of Technology) · Cited by 4

Análise do conhecimento extraído de classificadores simbólicos utilizando medidas de avaliação e de interessabilidade.

Gomes, A. K. (2002) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2002.tde-04072002-144610 · Cited by 4

Learning Process in a Model of Associative Memory

Nakano, K. (1971) · Springer eBooks · doi:10.1007/978-1-4615-7566-5_15 · Cited by 4

Tool cutting force modeling and wear estimation of micro-end-milling operations

Bao, W.-Y. (1999) · doi:10.25148/etd.fi14050423 · Cited by 4

A Fuzzy Neuron with Binary Input and its Training Algorithm

Brouwer, R. K. (1999) · Neural Processing Letters · doi:10.1023/a:1018663627241 · Cited by 4

Sample compression, margins and generalization: Extensions to the set covering machine

Shah, M. (2006) · uO Research (University of Ottawa) · doi:10.20381/ruor-12920 · Cited by 4

Improving performance of genetic algorithms by using novel fitness functions

Cooper, J. (2006) · Loughborough University Research Repository (Loughborough University) · Cited by 4

Associative digital neural network based on code and graph theories

Tanaka, M., Takeya, K., Kanaya, M., & Chigusa, Y. (2003) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.1989.100812 · Cited by 4

The complexity of learning halfspaces using generalized linear methodsPreprint

Daniely, A., Linial, N., & Shalev‐Shwartz, S. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1211.0616 · Cited by 4

A walk in the statistical mechanical formulation of neural networksPreprint

Agliari, E., Adriano Barra, A., Galluzzi, A., Tantari, D., & Tavani, F. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.5300 · Cited by 4

La magnetoencefalografía: una nueva herramienta para el estudio de los procesos cognitivos básicos

Maestú, F., González-Marqués, J., Marty, G., Nadal, M., Cela‐Conde, C. J., & Ortiz, T. (2005) · Redalyc (Universidad Autónoma del Estado de México) · Cited by 4

Geometric learning algorithm for elementary perceptron and its convergence conditions

Miyoshi, S., Ikeda, K., & Nakayama, K. (1999) · Electronics and Communications in Japan (Part III Fundamental Electronic Science) · doi:10.1002/(sici)1520-6440(199909)82:9 · Cited by 4

Sequential inference with reliable observations: Learning to construct force-dynamic models

Fern, A., & Givan, R. L. (2006) · Artificial Intelligence · doi:10.1016/j.artint.2006.08.003 · Cited by 4

One cannot build theories of cerebellar function on shaky foundations: Induction properties of long-term depression have to be taken into account

Schutter, E. D. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081577 · Cited by 4

?The strange story of the Perceptron?

Forsyth, R. S. (1990) · Artificial Intelligence Review · doi:10.1007/bf00133190 · Cited by 4

Reply [to “Comment on ‘The use of artificial neural networks for the prediction of water quality parameters’ by H. R. Maier and G. C. Dandy”]

Maier, H. R., & Dandy, G. C. (1997) · Water Resources Research · doi:10.1029/97wr00970 · Cited by 4

On data classification by iterative linear partitioning

Anthony, M. (2004) · Discrete Applied Mathematics · doi:10.1016/j.dam.2004.04.003 · Cited by 4

Limitations of PET and lesion studies in defining the role of the human cerebellum in motor learning

Timmann, D., & Diener, H. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081899 · Cited by 4

MI-Winnow: A New Multiple-Instance Learning Algorithm

Cholleti, S. R., Goldman, S. A., & Rahmani, R. (2006) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2006.82 · Cited by 4

A PENALTY‐LOGIC SIMPLE‐TRANSITION MODEL FOR STRUCTURED SEQUENCES

Fern, A. (2009) · Computational Intelligence · doi:10.1111/j.1467-8640.2009.00346.x · Cited by 4

Maximum-entropy parameter estimation for the k-NN modified value-difference kernel

Hendrickx, I., & van den Bosch, A. P. J. (2005) · Research portal (Tilburg University) · Cited by 4

Online Classification for Complex Problems Using Simultaneous Projections

Amit, Y., Shalev‐Shwartz, S., & Singer, Y. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0008 · Cited by 4

Classification of Burst and Suppression in the Neonatal EEG

Löfhede, J. (2007) · Borås Academic Digital Archive (University of Borås) · Cited by 4

New support vector algorithms for multicategorical data applied to real-time object recognition

Borer, S. (2003) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-2826 · Cited by 4

Aplicação de redes neurais artificiais na análise de séries temporais econômico-financeiras

de Oliveira, M. A. (2007) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.12.2007.tde-31012008-112504 · Cited by 4

Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques

Ruifang Ge, A. (2010) · Texas ScholarWorks (Texas Digital Library) · Cited by 4

Learning and Deduction in Neural Networks and Logic

Komendantskaya, E. (2007) · Cited by 4

Neural Networks: New Tools For Data Analysis ?

Bioch, J. C., Verbeke, W., & van Dijk, M. W. (2005) · Workshop on Neural Network Applications and Tools (NNAT) · doi:10.1109/nnat.1993.586051 · Cited by 4

What behavioral benefit does stiffness control have? An elaboration of Smith's proposal

van Galen, G. P., Hendriks, A. W. C. J., & DeJong, W. P. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081917 · Cited by 4

Neural networks forecasting and classification-based techniques for novelty detection in time series

Oliveira, A. L. I. (2004) · Americanae (AECID Library) · Cited by 4

3-D modelling and classification in automated target recognition

Nutter, B. S. (1990) · ThinkTech (Texas Tech University) · Cited by 4

Adaptive Subgradient Methods for Online AUC MaximizationPreprint

Ding, Y., Zhao, P., Hoi, S. C. H., & Ong, Y.-S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.00351 · Cited by 4

Information extraction from multiple syntactic sources

Grishman, R., & Zhao, S. (2005) · VDM Verlag Dr. Müller eBooks · Cited by 4

Distributed ARTMAP

Carpenter, G. A., & Milenova, B. L. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1999.832688 · Cited by 4

Two separate pathways for cerebellar LTD: NO-dependent and NO-independent

Hartell, N. A. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081681 · Cited by 4

Kernel based online learning

Zhao, P. (2013) · doi:10.32657/10356/54684 · Cited by 4

The Capacity of Artificial Neural Networks Using the Delta Rule.

Prados, D. L. (1989) · doi:10.31390/gradschool_disstheses.4869 · Cited by 4

Fuzzy neural networks techniques and their applications

Ishibuchi, H., & Nii, M. (1998) · Neural network systems techniques and applications · doi:10.1016/s1874-5946(98)80014-x · Cited by 4

MARS

Meng, X., & Chen, Z. (2011) · IGI Global eBooks · doi:10.4018/978-1-59140-414-9.ch005 · Cited by 4

Domain Adaptation and Domain Generalization with Representation Learning

Ghifary, M. (2016) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.17014700.v1 · Cited by 4

Word sense disambiguation by a higher order connectionist net based on distributed representations

Xin, W., McTear, M., & Ojha, P. (2002) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.1993.320156 · Cited by 4

Estimation of classrooms occupancy using a multi-layer perceptronPreprint

Rodrigues, E. M. D. S., Pereira, L. D., Gaspar, A. R., Gomes, Á., & da Silva, M. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.02125 · Cited by 4

Neural Network Methods Applied to Character Recognition

Wilson, C. L., & Blue, J. L. (1992) · Social Science Computer Review · doi:10.1177/089443939201000204 · Cited by 4

Effectiveness of feature and classifier algorithms in character recognition systems

(1992) · doi:10.6028/nist.ir.4995 · Cited by 4

Kernel-based adaptive estimation: multidimensional and state-space approaches

Tobar, F. A. (2014) · Spiral (Imperial College London) · doi:10.25560/24575 · Cited by 4

Reduced-set models for improving the training and execution speed of kernel methods

Kingravi, H. A. (2014) · SMARTech Repository (Georgia Institute of Technology) · Cited by 4

Application of Nonlinear Identification to Long Range Weather Prediction

Toldalagi, P., & Mehra, R. К. (1982) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)63041-6 · Cited by 4

Machine learning paradigms for building energy performance simulations

Aijazi, A. (2017) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 4

Constructing high-dimensional neural network potentials for molecular dynamics

Stende, J.-A. (2017) · NORA - Norwegian Open Research Archives · Cited by 4

Deep Convolutional Neural Networks for Eigenvalue Problems in Mechanics

Finol, D. (2019) · Harvard Dataverse · doi:10.7910/dvn/k33g15 · Cited by 4

A Novel Hybrid Learning Algorithm For Artificial Neural Networks

Ghosh, R. (2003) · Griffith Research Online · doi:10.25904/1912/316 · Cited by 4

A century of excellence in measurements, standards, and technology 1901-2000

Lide, D. R. (2001) · doi:10.6028/nist.sp.958 · Cited by 4

Machine Learning and Options Pricing: a Comparison of Black-Scholes and a Deep Neural Network in Pricing and Hedging DAX 30 Index Options

Stark, L. (2017) · Aaltodoc (Aalto University) · Cited by 4

Artificial Neural Network for Forecasting One Day Ahead of Global Solar IrradiancePreprint

Ettayyebi, H., & Himdi, K. E. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3187061 · Cited by 4

Solution of Definite Integrals using Functional Link Artificial Neural NetworksPreprint

Changdar, S., & Bhattacharjee, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.09656 · Cited by 4

Human-like machine thinking: Language guided imaginationPreprint

Qi, F. H., & Wu, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07562 · Cited by 4

Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a MarginPreprint

Diakonikolas, I., Kane, D. M., & Manurangsi, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11335 · Cited by 4

Exploiting the Surrogate Gap in Online Multiclass ClassificationPreprint

van der Hoeven, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.12618 · Cited by 4

An Efficient Neural Network by a Classification Tree

Chabanon, C., Yves Lechevallier, B., & Millemann, S. (1992) · Physica-Verlag eBooks · doi:10.1007/978-3-662-26811-7_31 · Cited by 4

THE PERCEPTRON: A PROBABILISTIC MODEL FOR INFORMATION STORAGE AND ORGANIZATION

Brain, I. T., & Rosenblatt, F. F. (2013)

The perception: a probabilistic model for information storage and organization in the brain

Rosenblatt, F. F. (1988) · The MIT Press eBooks · Cited by 78

(1958) F. Rosenblatt, "The perceptron: a probabilistic model for information storage and organization in the brain," Psychological Review 65: 386-408

Rosenblatt, F. F. (1988) · The MIT Press eBooks · doi:10.7551/mitpress/4943.003.0010 · Cited by 13

Perception, memory and providing information

Upton, D. (2013) · doi:10.4324/9781315846972-11

Brain mechanisms of perception and memory from neuron to behavior

Ono, T., & Toyama, I. S. O. B. M. O. P. A. M. F. N. T. B. 1. (1993)