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

Pruning recurrent neural networks replicates adolescent changes in working memory and reinforcement learning

Averbeck, B. B. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2121331119 · Cited by 37

Tensor networks in machine learning

Sengupta, R., Adhikary, S., Oseledets, I. V., & Biamonte, J. (2022) · European Mathematical Society Magazine · doi:10.4171/mag/101 · Cited by 37

Orbital angular momentum optical communications enhanced by artificial intelligence

Li, B., Luan, H., Li, K., Chen, Q., Meng, W., Cheng, K., Gu, M., & Fang, X. (2022) · Journal of Optics · doi:10.1088/2040-8986/ac8108 · Cited by 37

Mathematical Aspects of Neural Networks

Hammer, B., & Villmann, T. (2003) · Cited by 37

An exploratory study to design a novel hand movement identification system

Khezri, M., & Jahed, M. (2009) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2009.02.001 · Cited by 37

Non-steady state approach to the vacuum nitriding for tools

Kula, P., Wołowiec‐Korecka, E., Pietrasik, R., Dybowski, K., & Januszewicz, B. (2012) · Vacuum · doi:10.1016/j.vacuum.2012.08.001 · Cited by 37

Online Heterogeneous Transfer Learning by Knowledge Transition

Wu, H., Yan, Y., Ye, Y., Min, H., Ng, M. K., & Wu, Q. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3309537 · Cited by 37

A New Associative Model with Dynamical Synapses

Vazquez, R. A., & Sossa, H. (2008) · Neural Processing Letters · doi:10.1007/s11063-008-9089-6 · Cited by 37

ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW

Ttayagarajan, T., Ponnavaikko, M., Shanmugam, J., Panda, R. C., & Rao, P. G. (1998) · Drying Technology · doi:10.1080/07373939808917449 · Cited by 37

The autonomous intelligent system

Fritz, W., Martínez, R. G., Blanqué, J. R., Adobbati, R., Rama, A., & Sarno, M. (1989) · Robotics and Autonomous Systems · doi:10.1016/0921-8890(89)90004-3 · Cited by 37

Fast cosmological parameter estimation using neural networksPreprint

Auld, T., Bridges, M., Hobson, M., & Gull, S. F. (2006) · Cited by 37

Objections to Computationalism: A Survey

Miłkowski, M. (2018) · Roczniki Filozoficzne · doi:10.18290/rf.2018.66.3-3 · Cited by 36

From pixels to insights: Machine learning and deep learning for bioimage analysis

Jan, M., Spangaro, A., Lenartowicz, M., & Ušaj, M. M. (2023) · BioEssays · doi:10.1002/bies.202300114 · Cited by 36

Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification

Özsarı, Ş., Kumru, E., Ekinci, F., Akata, İ., Güzel, M. S., Açıcı, K., Özcan, E. S., & Aşuroğlu, T. (2024) · Sensors · doi:10.3390/s24227189 · Cited by 36

Large-Scale Integrated Photonic Device Platform for Energy-Efficient AI/ML Accelerators

Tossoun, B. M., Xiao, X., Cheung, S. S., Yuan, Y., Peng, Y., Srinivasan, S., Giamougiannis, G., Huang, Z., Singaraju, P., London, Y., et al. (2025) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2025.3527904 · Cited by 36

Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python

Lemenkova, P. (2025) · Examples and Counterexamples · doi:10.1016/j.exco.2025.100180 · Cited by 36

Morpholexical and Discriminative Language Models for Turkish Automatic Speech Recognition

Sak, H., Saraçlar, M., & Güngör, T. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2012.2201477 · Cited by 36

Prediction of Lost Circulation Using Virtual Intelligence in One of Iranian Oilfields

Moazzeni, A., Nabaei, M., & Jegarluei, S. G. (2010) · SPE Nigeria Annual International Conference and Exhibition · doi:10.2118/136992-ms · Cited by 36

Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network

Jahani, A., Feghhi, J., Makhdoum, M., & Omid, M. (2015) · Journal of Environmental Planning and Management · doi:10.1080/09640568.2015.1005732 · Cited by 36

ReRAM‐Based Neuromorphic Computing

Indiveri, G., Linn, E., & Ambrogio, S. (2016) · Wiley eBooks · doi:10.1002/9783527680870.ch25 · Cited by 36

Mobility prediction in mobile ad hoc networks using neural learning machines

Ghouti, L. (2016) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2016.03.001 · Cited by 36

Application of soft computing techniques in coastal study – A review

Dwarakish, G. S., & Nithyapriya, B. (2016) · Journal of Ocean Engineering and Science · doi:10.1016/j.joes.2016.06.004 · Cited by 36

Recognition of protein allosteric states and residues: Machine learning approaches

Zhou, H.-Y., Dong, Z., & Tao, P. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25218 · Cited by 36

A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

Shah, H., Tairan, N. M., Garg, H., & Ghazali, R. (2018) · Symmetry · doi:10.3390/sym10070292 · Cited by 36

A study on the relationship between the rank of input data and the performance of random weight neural network

Cao, W., Hu, L., Gao, J., Wang, X., & Ming, Z. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04719-8 · Cited by 36

Artificial neural network models for radiowave propagation in tunnels

Seretis, A., Zhang, X., Zeng, K., & Sarris, C. D. (2020) · IET Microwaves Antennas & Propagation · doi:10.1049/iet-map.2019.0988 · Cited by 36

Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping

Min, D.-H., & Yoon, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-86137-x · Cited by 36

Application of Machine Learning in Industry 4.0

Gourisaria, M. K., Agrawal, R., Harshvardhan, G. M., Pandey, M., & Rautaray, S. S. (2021) · Studies in big data · doi:10.1007/978-981-33-6518-6_4 · Cited by 36

PyPEF—An Integrated Framework for Data-Driven Protein Engineering

Siedhoff, N. E., Illig, A.-M., Schwaneberg, U., & Davari, M. D. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00099 · Cited by 36

Artificial Neural Network Model for Strength Prediction of Ultra-High-Performance Concrete

García, J. A. (2021) · ACI Materials Journal · doi:10.14359/51732710 · Cited by 36

CNN-Bidirectional LSTM Based Optical Character Recognition of Sanskrit Manuscripts : A Comprehensive Systematic Literature Review

Kataria, B., & Jethva, H. B. (2019) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2064126 · Cited by 36

Quantitative Gaussian approximation of randomly initialized deep neural networks

Basteri, A., & Trevisan, D. (2024) · Machine Learning · doi:10.1007/s10994-024-06578-z · Cited by 36

Metabolite, protein, and tissue dysfunction associated with COVID-19 disease severity

Rahnavard, A., Mann, B. T., Giri, A., Chatterjee, R., & Crandall, K. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-16396-9 · Cited by 36

Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

Tian, Y., Zhang, X., He, C., Tan, K. C., & Jin, Y. (2023) · Chinese Journal of Electronics · doi:10.23919/cje.2022.00.100 · Cited by 36

Data-driven models for predicting community changes in freshwater ecosystems: A review

Lee, D., Lee, D., Cha, Y., Min, J.-H., & Park, Y. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102163 · Cited by 36

A bibliometric analysis on the application of deep learning in finance: status, development and future directions

Manogna, R. L., & Anand, A. (2023) · Kybernetes · doi:10.1108/k-04-2023-0637 · Cited by 36

SUPERIORITY OF ARTIFICIAL NEURAL NETWORKS OVER STATISTICAL METHODS IN PREDICTION OF THE OPTIMAL LENGTH OF ROCK BOLTS

Hasanzadehshooiili, H., Lakirouhani, A., & Medzvieckas, J. (2012) · Journal of Civil Engineering and Management · doi:10.3846/13923730.2012.724029 · Cited by 36

Bidirectional associative memories

Acevedo, E., Yáñez-Márquéz, C., & Acevedo, A. (2013) · ACM Computing Surveys · doi:10.1145/2431211.2431217 · Cited by 36

Multisensor data fusion for high quality data analysis and processing in measurement and instrumentation

Huang, Y., Lan, Y., Hoffmann, W. C., & Lacey, R. E. (2007) · Journal of Bionic Engineering · doi:10.1016/s1672-6529(07)60013-4 · Cited by 36

Vertical migration in adult Atlantic cod (Gadus morhua)

Strand, E., & Huse, G. (2007) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/f07-135 · Cited by 36

Identification of properties important to protein aggregation using feature selection

Fang, Y., Gao, S., Tai, D., Middaugh, C. R., & Fang, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-314 · Cited by 36

Learning Halfspaces with Malicious Noise

Klivans, A. R., Long, P. M., & Servedio, R. A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02927-1_51 · Cited by 36

Central Nervous System and Computation

Guidolin, D., Albertin, G., Guescini, M., Fuxé, K., & Agnati, L. F. (2011) · The Quarterly Review of Biology · doi:10.1086/662456 · Cited by 36

Automated assessment of ESOL free text examinations

Briscoe, T., Medlock, B., & Andersen, Ø. E. (2021) · doi:10.48456/tr-790 · Cited by 36

Does the Cerebral Cortex Exploit High-Dimensional, Non-linear Dynamics for Information Processing?

Singer, W., & Lazăr, A. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00099 · Cited by 36

XCS with Computed Prediction for the Learning of Boolean Functions

Lanzi, P. L., Loiacono, D., Wilson, S. W., & Goldberg, D. E. (2005) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2005.1554736 · Cited by 36

Dysmetria of thought: Correlations and conundrums in the relationship between the cerebellum, learning, and cognitive processing

Schmahmann, J. D. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081851 · Cited by 36

Agnostic Learning of Geometric Patterns

Goldman, S. A., Kwek, S. S., & Scott, S. (2001) · Journal of Computer and System Sciences · doi:10.1006/jcss.2000.1723 · Cited by 36

Multi Layer Neural Networks as Replacement for Pooling OperationsPreprint

Fuhl, W., & Kasneci, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06969 · Cited by 36

Deep Learning Activation Functions: Fixed-Shape, Parametric, Adaptive, Stochastic, Miscellaneous, Non-Standard, EnsemblePreprint

Hammad, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2407.11090 · Cited by 36

An Equivalence between the Lasso and Support Vector Machines

Jaggi, M. (2013) · arXiv · doi:10.48550/arxiv.1303.1152 · Cited by 35

Using Mahalanobis–Taguchi system, logistic regression, and neural network method to evaluate purchasing audit quality

Lee, Y., Hsiao, Y.-C., Peng, C.-F., Tsai, S., Wu, C., & Chen, Q. (2014) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/0954405414539934 · Cited by 35

Learning, fast and slow

Meister, M. (2022) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2022.102555 · Cited by 36

Explainable machine learning aided optimization of masonry infilled reinforced concrete frames

Latif, I., Banerjee, A., & Surana, M. (2022) · Structures · doi:10.1016/j.istruc.2022.08.115 · Cited by 35

Bayesian Dual-Input-Channel LSTM-Based Prognostics: Toward Uncertainty Quantification Under Varying Future Operations

Xiahou, T., Wang, F., Liu, Y., & Zhang, Q. (2023) · IEEE Transactions on Reliability · doi:10.1109/tr.2023.3277332 · Cited by 35

ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels

Zhao, Y., Zheng, G., Mukherjee, S., McCann, R., & Awadallah, A. H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25620 · Cited by 35

Coarse-Graining with Equivariant Neural Networks: A Path Toward Accurate and Data-Efficient Models

Loose, T. D., Sahrmann, P. G., Qu, T. S., & Voth, G. A. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c05928 · Cited by 35

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm

Asteris, P. G., Gandomi, A. H., Armaghani, D. J., Kokoris, S. I., Papandreadi, A. T., Roumelioti, A., Papanikolaou, S., Tsoukalas, M. Z., Triantafyllidis, L., Koutras, E. I., et al. (2024) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2024.02.037 · Cited by 35

An explainable data-driven decision support framework for strategic customer development

Onari, M. A., Rezaee, M. J., Saberi, M., & Nobile, M. S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111761 · Cited by 35

Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study

Liu, J., Khan, M. K. H., Guo, W., Dong, F., Ge, W., Zhang, C., Gong, P., Patterson, T. A., & Hong, H. (2024) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2024.2377593 · Cited by 35

GPSFormer: A Global Perception and Local Structure Fitting-Based Transformer for Point Cloud Understanding

Wang, C., Wu, M., Lam, S.-K., Ning, X., Yu, S., Wang, R., Li, W., & Srikanthan, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73242-3_5 · Cited by 35

Anomaly detection in Smart-manufacturing era: A review

Elía, I., & Pagola, M. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109578 · Cited by 36

Understanding Machine Learning Principles: Learning, Inference, Generalization, and Computational Learning Theory

Du, K.-L., Zhang, R., Jiang, B., Zeng, J., & Lu, J. (2025) · Mathematics · doi:10.3390/math13030451 · Cited by 35

Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow Efficiency

Sanpablo, A. I. P., Quinzaños-Fresnedo, J., Gutiérrez-Martínez, J., Rodríguez, I. G. L., & Roldán-Valadéz, E. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056370620250403030638 · Cited by 36

Tree Revision Learning for Dependency Parsing

Attardi, G., & Ciaramita, M. (2007) · CINECA IRIS Institutial research information system (University of Pisa) · Cited by 35

Perspectives on Modeling in Cognitive Science

Shiffrin, R. M. (2010) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2010.01092.x · Cited by 35

Disturbance encountered landing system design based on sliding mode control with evolutionary computation and cerebellar model articulation controller

Juang, J., & Yu, S.-T. (2015) · Applied Mathematical Modelling · doi:10.1016/j.apm.2015.04.005 · Cited by 35

Early neural network development history: the age of Camelot

Eberhart, R. C., & Dobbins, R. W. (1990) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.59207 · Cited by 35

Competence and psychological reality.

Pylyshyn, Z. W. (1972) · American Psychologist · doi:10.1037/h0033134 · Cited by 35

Collaborative topic regression for online recommender systems: an online and Bayesian approach

Liu, C., Jin, T., Hoi, S. C. H., Zhao, P., & Sun, J. (2017) · Machine Learning · doi:10.1007/s10994-016-5599-z · Cited by 35

Randomized Machine Learning Approaches: Recent Developments and Challenges

Gallicchio, C., Martín‐Guerrero, J. D., Micheli, A., & Soria‐Olivas, E. (2017) · CINECA IRIS Institutial research information system (University of Pisa) · Cited by 35

A machine learning approach for predicting methionine oxidation sites

Aledo, J. C., Cantón, F. R., & Veredas, F. J. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1848-9 · Cited by 35

Early Expression Detection via Online Multi-Instance Learning With Nonlinear Extension

Xie, L., Tao, D., & Wei, H. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2869891 · Cited by 35

Unsupervised Deep Learning for Induction Motor Bearings Monitoring

Cipollini, F., Oneto, L., Coraddu, A., & Savio, S. (2019) · Data-Enabled Discovery and Applications · doi:10.1007/s41688-018-0025-2 · Cited by 35

Time series classification with Echo Memory Networks

Ma, Q., Zhuang, W., Shen, L., & Cottrell, G. W. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.05.008 · Cited by 35

Population adaptation in efficient balanced networks

Gutierrez, G. J., & Denève, S. (2019) · eLife · doi:10.7554/elife.46926 · Cited by 35

Perceptrons from memristors

Silva, F., Sanz, M., Seixas, J., Solano, E., & Omar, Y. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.10.013 · Cited by 35

On quantum methods for machine learning problems part II: Quantum classification algorithms

Ablayev, F. M., Ablayev, M., Huang, J. Z., Khadiev, K. R., Salikhova, N., & Wu, D. (2019) · Big Data Mining and Analytics · doi:10.26599/bdma.2019.9020018 · Cited by 35

A unique multilayer perceptron model (ANN) for different oxide/EG nanofluid’s viscosity from the experimental study

Yadav, D., Dansena, P., Ghosh, S. K., & Singh, P. K. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.124030 · Cited by 35

Artificial Intelligence Techniques for Modeling Indoor Building Temperature under Tropical Climate Using Outdoor Environmental Monitoring

Tzuc, O. M., Livas-García, A., Torres, M. J., May, E. C., López-Manrique, L. M., & Bassam, A. (2020) · Journal of Energy Engineering · doi:10.1061/(asce)ey.1943-7897.0000649 · Cited by 35

Neural network based 3D tracking with a graphene transparent focal stack imaging system

Zhang, D., Xu, Z., Huang, Z., Gutierrez, A. R., Blocker, C. J., Liu, C., Lien, M.-B., Cheng, G., Liu, Z., Chun, I. Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22696-x · Cited by 35

Evaluation of supervised machine-learning methods for predicting appearance traits from DNA

Katsara, M., Branicki, W., Walsh, S., Kayser, M., & Nothnagel, M. (2021) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2021.102507 · Cited by 35

Neural network-supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating

Singh, J., & Singh, S. (2021) · Journal of King Saud University - Engineering Sciences · doi:10.1016/j.jksues.2021.12.005 · Cited by 35

Adaptive Fuzzy Neural Network PID Algorithm for BLDCM Speed Control System

Yin, H., Yi, W., Wu, J., Wang, K., & Guan, J. (2021) · Mathematics · doi:10.3390/math10010118 · Cited by 35

Your Labels are Selling You Out: Relation Leaks in Vertical Federated Learning

Qiu, P., Zhang, X., Ji, S., Du, T., Pu, Y., Zhou, J. W., & Wang, T. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3208630 · Cited by 35

An illustration of model agnostic explainability methods applied to environmental data

Wikle, C. K., Datta, A., Hari, B. V., Boone, E. L., Sahoo, I., Indulekha, K., Castruccio, S., Simmons, S. J., Burr, W. S., & Chang, W. (2022) · Environmetrics · doi:10.1002/env.2772 · Cited by 35

Data-Driven Reduced Order Modelling for Patient-Specific Hemodynamics of Coronary Artery Bypass Grafts with Physical and Geometrical Parameters

Siena, P., Girfoglio, M., Ballarin, F., & Rozza, G. (2023) · Journal of Scientific Computing · doi:10.1007/s10915-022-02082-5 · Cited by 35

Using machine learning to retrospectively predict self‐reported gambling problems in Quebec

Murch, W. S., Kairouz, S., Dauphinais, S., Picard, É., Costes, J., & French, M. A. (2023) · Addiction · doi:10.1111/add.16179 · Cited by 35

Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning Machine

Ni, N., & Dong, S. S. (2023) · Journal of Scientific Computing · doi:10.1007/s10915-023-02162-0 · Cited by 35

Lessons from natural flight for aviation: then, now and tomorrow

Harvey, C., de Croon, G., Taylor, G. K., & Bomphrey, R. J. (2023) · Journal of Experimental Biology · doi:10.1242/jeb.245409 · Cited by 35

Optimized Zero False Positives Perceptron Training for Malware Detection

Gavriluţ, D. T., Benchea, R., & Vatamanu, C. (2012) · International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) · doi:10.1109/synasc.2012.34 · Cited by 35

The Interactive Effects of Strategic Marketing Planning and Performance: A Neural Network Analysis

Phillips, P., Davies, F., & Moutinho, L. (2001) · Journal of Marketing Management · doi:10.1362/0267257012571438 · Cited by 35

Snow disturbances in secondary Norway spruce forests in Central Europe: Regression modeling and its implications for forest management

Hlásny, T., Křístek, Š., Holuša, J., Trombik, J., & Urbaňcová, N. (2011) · Forest Ecology and Management · doi:10.1016/j.foreco.2011.08.005 · Cited by 35

Rough set-based neuro-fuzzy system

Ang, K. K., & Quek, C. H. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.246758 · Cited by 35

LISREL and neural network modelling: two comparison studies

Davies, F., Goode, M. M. H., Mazanec, J. A., & Moutinho, L. (1999) · Journal of Retailing and Consumer Services · doi:10.1016/s0969-6989(98)00009-5 · Cited by 35

Study of distributed learning as a solution to category proliferation in Fuzzy ARTMAP based neural systems

Parrado-Hernández, E., Gómez‐Sánchez, E., & Dimitriadis, Y. A. (2003) · Neural Networks · doi:10.1016/s0893-6080(03)00009-1 · Cited by 35

Multi-class protein fold recognition using adaptive codes

Ie, E., Weston, J., Noble, W. S., & Leslie, C. S. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102393 · Cited by 35

Error Estimation and Model Selection

Scheffer, T. (1999) · Cited by 35

Electron Microscopic in Cellular and Molecular Biology

Plattner, H., & Zingsheim, H. P. (1983) · Springer eBooks · doi:10.1007/978-1-4613-3533-7_1 · Cited by 35

Growing layers of perceptrons: introducing the Extentron algorithm

Baffes, P., & Zelle, J. M. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1992.226956 · Cited by 35

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)