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

Analyzing Cache Side Channels Using Deep Neural Networks

Zhang, T., Zhang, Y., & Lee, R. B.-L. (2018) · Annual Computer Security Applications Conference · doi:10.1145/3274694.3274715 · Cited by 22

Thwarting Replication Attack Against Memristor-Based Neuromorphic Computing System

Yang, C., Liu, B., Li, H. H., Chen, Y., Barnell, M. D., Wu, Q., Wen, W., & Rajendran, J. (2019) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2019.2937817 · Cited by 22

MACHINE INTELLIGENCE. ESSAYS ON THE THEORY OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Шумский, С., & Shumskiy, S. (2019) · Publishing Center RIOR eBooks · doi:10.29039/02011-1 · Cited by 22

Deep Learning applied to the Identification of Fruit Fly in Intelligent Traps

Martins, V. A. M., Freitas, L. C., de Aguiar, M. S., Brisolara, L., & Ferreira, P. R. (2019) · Brazilian Symposium on Computing System Engineering (SBESC) · doi:10.1109/sbesc49506.2019.9046088 · Cited by 22

Toward Efficient Processing and Learning With Spikes: New Approaches for Multispike Learning

Yu, Q., Li, S., Tang, H., Wang, L., Dang, J., & Tan, K. C. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2984888 · Cited by 22

Online Multiview Deep Forest for Remote Sensing Image Classification via Data Fusion

Nie, X., Gao, R., Wang, R., & Xiang, D. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.3002848 · Cited by 22

A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems

Tama, B. A., & Lim, S. (2020) · Mathematics · doi:10.3390/math8101814 · Cited by 22

Artificial Intelligence for the Prediction of Exhaust Back Pressure Effect on the Performance of Diesel Engines

Fernoaga, V., Sandu, V., & Bălan, T. (2020) · Applied Sciences · doi:10.3390/app10207370 · Cited by 22

A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification

Yu, L., Yu, K., & Yu, K. (2021) · Financial Innovation · doi:10.1186/s40854-021-00249-x · Cited by 22

Integration of neural network and AP-NDEA model for performance evaluation of sustainable pharmaceutical supply chain

Moslemi, S., Mirzazadeh, A., Weber, G., & Sobhanallahi, M. A. (2021) · OPSEARCH · doi:10.1007/s12597-021-00561-1 · Cited by 22

Deep Learning-based query-count forecasting using farmers’ helpline data

Godara, S., & Toshniwal, D. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106875 · Cited by 22

A Comparative Study of LASSO and ANN Regressions for the Prediction of the Direct Tensile Behavior of UHPFRC

García, J. A., Ortega-Guzmán, J. J., Chaparro-Ruiz, D. A., & García-Castaño, E. (2022) · Advances in Civil Engineering Materials · doi:10.1520/acem20210101 · Cited by 22

Neural Networks and Deep Learning

Theodoridis, S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818803-3.00030-1 · Cited by 22

Machine Learning for Hybrid Machine Translation

Hunsicker, S., Chen, Y., & Federmann, C. (2012) · Cited by 22

The Initial Freezing Temperature of Foods at High Pressure

Guignon, B., Torrecilla, J. S., Otero, L., Ramos, A. M., García, A. D. M., & Sanz, P. D. (2008) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408390701347736 · Cited by 22

Time Series Prediction of Chimney Foundation Settlement by Neural Networks

Zhang, G., Xiang, X., & Tang, H. (2011) · International Journal of Geomechanics · doi:10.1061/(asce)gm.1943-5622.0000029 · Cited by 22

Automated classification system for Bone Age X-ray images

Seok, J., Hyun, B., Kasa-Vubu, J. Z., & Girard, A. (2012) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2012.6377701 · Cited by 22

An online learning network for biometric scores fusion

Kim, Y., Toh, K., Teoh, A. B. J., Eng, H., & Yau, W. (2012) · Neurocomputing · doi:10.1016/j.neucom.2011.12.048 · Cited by 22

Kernel Multilayer Perceptron

Rauber, T. W., & Berns, K. (2011) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2011.21 · Cited by 22

Intelligent email: aiding users with AI

Dredze, M. H., Wallach, H., Puller, D., Brooks, T., Carroll, J., Magarick, J., Blitzer, J. C., & Pereira, F. M. B. (2008) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 22

Self-organizing map for symbolic data

Yang, M., Hung, W., & Chen, D. (2012) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2012.04.006 · Cited by 22

An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

Xie, X., Qu, H., Liu, G., Zhang, M., & Kurths, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150329 · Cited by 22

Chinese Handwriting Recognition: An Algorithmic Perspective

Su, T. (2013) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-642-31812-2 · Cited by 22

Feature Learning and Deep Learning Architecture Survey

Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_10 · Cited by 22

Neutron spectrum unfolding using radial basis function neural networks

Alvar, A. A., Deevband, M. R., & Ashtiyani, M. (2017) · Applied Radiation and Isotopes · doi:10.1016/j.apradiso.2017.07.048 · Cited by 22

Microarray Data Classified by Artificial Neural Networks

Linder, R., Richards, T. A., & Wagner, M. (2007) · Methods in molecular biology · doi:10.1007/978-1-59745-304-2_22 · Cited by 22

Learning intersections and thresholds of halfspaces

Klivans, A. R., O’Donnell, R., & Servedio, R. A. (2002) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2002.1181894 · Cited by 22

Fuzzy perceptron neural networks for classifiers with numerical data and linguistic rules as inputs

Chang, J., & Chen, J. (2000) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.890331 · Cited by 22

Adaptive stochastic cellular automata: Theory

Lee, Y. C., Qian, S., Jones, R. D., Barnes, C. W., Flake, G. W., O'Rourke, M. K., Lee, K., Chen, H. H., Sun, G.-Z., Zhang, Y. Q., et al. (1990) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(90)90180-w · Cited by 22

Pattern Recognition from One Example by Chopping

Fleuret, F., & Blanchard, G. (2005) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 22

The common inverse-dynamics motor-command coordinates for complex and simple spikes

Kawato, M. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081760 · Cited by 22

A Machine Learning Approach to Anaphora Resolution Including Named Entity Recognition, PP Attachment Disambiguation, and Animacy Detection

Nøklestad, A. (2009) · NORA - Norwegian Open Research Archives · Cited by 22

AI-Powered Social BotsPreprint

Adams, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05143 · Cited by 22

A Quantum Model for Multilayer PerceptronPreprint

Shao, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.10561 · Cited by 22

Adaptive Precision CNN Accelerator Using Radix-X Parallel Connected Memristor CrossbarsPreprint

Lee, J., Eshraghian, J. K., Cho, K.-R., & Eshraghian, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09395 · Cited by 22

Inventing Intelligence: On the History of Complex Information Processing and Artificial Intelligence in the United States in the Mid-Twentieth Century

Penn, J. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.63087 · Cited by 22

Generalizing MLPs With Dropouts, Batch Normalization, and Skip ConnectionsPreprint

Kim, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.08186 · Cited by 22

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive ReviewPreprint

Golroudbari, A. A., & Sabour, M. H. (2023) · doi:10.22541/au.168664884.43899660/v1 · Cited by 22

Machine Learning Approaches for Predicting Progression to Alzheimer’s Disease in Patients with Mild Cognitive Impairment

Gelir, F., Akan, T., Alp, S., Gecili, E., Bhuiyan, M. S., Disbrow, E. A., Conrad, S. A., Vanchiere, J. A., Kevil, C. G., (ADNI), T. A. D. N. I., et al. (2024) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-024-00918-z · Cited by 21

Physics‐Guided Deep Learning Model for Daily Groundwater Table Maps Estimation Using Passive Surface‐Wave Dispersion

Teixeira, J. C., Bodet, L., Rivière, A., Hallier, A., Gesret, A., Dangeard, M., Dhemaied, A., & Boisson, J. (2025) · Water Resources Research · doi:10.1029/2024wr037706 · Cited by 21

Physical twinning for joint encoding-decoding optimization in computational optics: a review

Bian, L., Zhan, X., Yan, R., Chang, X., Huang, H., & Zhang, J. (2025) · Light Science & Applications · doi:10.1038/s41377-025-01810-4 · Cited by 21

Federated Learning Under Concept Drift: A Systematic Survey of Foundations, Innovations, and Future Research Directions

Mahdi, O. A., Pardede, E., Bevinakoppa, S., & Ali, N. (2025) · Electronics · doi:10.3390/electronics14224480 · Cited by 21

Smartphone-based colorimetric sensing with reference calibration and ensemble machine learning for enhanced detection of nitrite and ammonium ions

Quoc, T. N., Phan, M.-V., Dang, K., Gia, H. N. H., Thien, P. N. T., Hoang, V. T., Do, T.-L., Tran, T.-B. H., Do, M.-H., Nguyen, L.-K.-T., et al. (2026) · Analytica Chimica Acta · doi:10.1016/j.aca.2026.345097 · Cited by 21

Machine Learning in Next-Generation Polymer Composites: Recent Advances and Perspectives

Olhan, S., Antil, B., & Maimí, P. (2026) · Applied Composite Materials · doi:10.1007/s10443-025-10437-y · Cited by 21

Integrating Feedback and Predictive Control in a Bio-Inspired Model of Visual Pursuit Implemented on a Humanoid Robot

Vannucci, L., Falotico, E., Lecce, N. D., Dario, P., & Laschi, C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22979-9_26 · Cited by 21

Actuators and sensors fault diagnosis with dynamic, state-space neural networks

Luzar, M., Czajkowski, A., Witczak, M., & Korbicz, J. (2012) · International Conference on Methods and Models in Automation and Robotics · doi:10.1109/mmar.2012.6347889 · Cited by 21

Hybrid Stochastic-Adversarial On-line Learning

Lazaric, A., & Munos, R. (2009) · HAL · Cited by 21

Intelligent emotion and behavior based on topological consciousness and adaptive resonance theory in a companion robot

Chumkamon, S., Hayashi, E., & Koike, M. (2016) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2016.09.004 · Cited by 21

Deep learning schemes for parabolic nonlocal integro-differential equations

Castro, J. (2022) · Partial Differential Equations and Applications · doi:10.1007/s42985-022-00213-z · Cited by 21

How to Make a Class

Pasquinelli, M. (2021) · Qui Parle · doi:10.1215/10418385-8955836 · Cited by 21

Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms

Wahid, M. F., Tafreshi, R., Khan, Z., & Retnanto, A. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109265 · Cited by 21

The longitudinal relationship between BOLD signal variability changes and white matter maturation during early childhood

Wang, H., Ghaderi, A., Long, X., Reynolds, J. E., Lebel, C. A., & Protzner, A. B. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118448 · Cited by 21

Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods

Wodecka, B., Drewnowski, J., Białek, A., Łazuka, E., & Szulżyk-Cieplak, J. (2022) · Processes · doi:10.3390/pr10010085 · Cited by 21

Genome-Enabled Prediction Methods Based on Machine Learning

Reinoso-Peláez, E. L., Gianola, D., & González-Recio, Ó. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2205-6_7 · Cited by 21

Recurrent neural network-based prediction of critical heat flux in rod bundles with non-uniform axial power shape

Marcinkiewicz, K., Wieckhorst, O., Macián‐Juan, R., Rehm, M., & Pohl, P. (2022) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2022.111825 · Cited by 21

Lithology identification in carbonate thin section images of the Brazilian pre-salt reservoirs by the computational vision and deep learning

Faria, E. L., Coelho, J. M., Matos, T. F., Santos, B. C. C., Trevizan, W. A., Gonzalez, J. L., Bom, C. R., de Albuquerque, M. P., & de Albuquerque, M. P. (2022) · Computational Geosciences · doi:10.1007/s10596-022-10168-0 · Cited by 21

Machine learning for rock mechanics problems; an insight

Yu, H., Taleghani, A. D., Balushi, F. A., & Wang, H. (2022) · Frontiers in Mechanical Engineering · doi:10.3389/fmech.2022.1003170 · Cited by 21

Finite strain poro-hyperelasticity: an asymptotic multi-scale ALE-FSI approach supported by ANNs

Dehghani, H., & Zilian, A. (2023) · Computational Mechanics · doi:10.1007/s00466-022-02262-y · Cited by 21

Towards a new generation of artificial‐intelligence‐based infrared atmospheric sounding interferometer retrievals of surface temperature: Part I – Methodology

Boucher, E., Aires, F., & Pellet, V. (2023) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4447 · Cited by 21

A Holistic Approach on Airfare Price Prediction Using Machine Learning Techniques

Kalampokas, Τ., Tziridis, K., Kalampokas, N., Nikolaou, A., Vrochidou, Ε., & Papakostas, G. A. (2023) · IEEE Access · doi:10.1109/access.2023.3274669 · Cited by 21

Total Harmonics Distortion Prediction at the Point of Common Coupling of industrial load with the grid using Artificial Neural Network

Ugwuagbo, E., Balogun, A., Ray, B. R., Anwar, A., & Ugwuishiwu, C. H. (2023) · Energy and AI · doi:10.1016/j.egyai.2023.100281 · Cited by 21

Deep Learning for Natural Language Processing: A Survey

Arkhangelskaya, E. O., & Nikolenko, S. (2023) · Journal of Mathematical Sciences · doi:10.1007/s10958-023-06519-6 · Cited by 21

Solving classification tasks by a receptron based on nonlinear optical speckle fields

Paroli, B., Martini, G., Potenza, M. A. C., Siano, M., Mirigliano, M., & Milani, P. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.08.001 · Cited by 21

Photothermally Activated Artificial Neuromorphic Synapses

Blankenship, B. W., Li, R., Guo, R., Zhao, N., Shin, J., Yang, R., Ko, S. H., Wu, J., Rho, Y., & Grigoropoulos, C. P. (2023) · Nano Letters · doi:10.1021/acs.nanolett.3c02681 · Cited by 21

The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review

Chang, M. C., Kim, J. K., Park, D., Kim, J. H., Kim, C. R., & Choo, Y. J. (2023) · Healthcare · doi:10.3390/healthcare11192687 · Cited by 21

Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling

Tuninetti, V., Forcael, D., Valenzuela, M., Martínez, A., Ávila, A. I., Medina, C., Pincheira, G., Salas, A., Oñate, Á., & Duchêne, L. (2024) · Materials · doi:10.3390/ma17020317 · Cited by 21

DAI-Net: Dual Adaptive Interaction Network for Coordinated Medication Recommendation

Zou, X. G., He, X., Zheng, X., Zhang, W., Chen, J., & Tang, C. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3425833 · Cited by 21

Adapters Strike Back

Steitz, J.-M. O., & Roth, S. (2024) · 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/cvpr52733.2024.02213 · Cited by 21

Experimental investigation on the formation and failure of landslide dams considering the landslide mobility and river flow

Luo, H., Zhou, G. G. D., Lu, X., Cui, K. F. E., Zhao, Y., Xie, Y., Zhong, W., Zhou, J., & Pasuto, A. (2024) · Engineering Geology · doi:10.1016/j.enggeo.2024.107873 · Cited by 21

AI-enabled routing in next generation networks: A survey

Aktas, F., Shayea, I. A. M., Ergen, M., Saoud, B., Yahya, A. E., & Aldasheva, L. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.01.095 · Cited by 21

Training-efficient density quantum machine learning

Coyle, B., Raj, S., Mathur, N., Cherrat, E. A., Jain, N., Kazdaghli, S., & Kerenidis, I. (2025) · npj Quantum Information · doi:10.1038/s41534-025-01099-6 · Cited by 21

A Biological Gradient Descent for Prediction Through a Combination of STDP and Homeostatic Plasticity

Galtier, M. N., & Wainrib, G. (2013) · Neural Computation · doi:10.1162/neco_a_00512 · Cited by 21

Nonparametric budgeted stochastic gradient descent

Le, T., Nguyen, V., Nguyen, T. D., & Phung, D. (2016) · Own your potential (DEAKIN) · Cited by 21

Organic Memristor Based Elements for Bio-inspired Computing

Battistoni, S., Dimonte, A., & Erokhin, V. V. (2016) · Emergence, complexity and computation · doi:10.1007/978-3-319-33921-4_18 · Cited by 21

Real-time simulation of a cat-scale artificial cerebellum on PEZY-SC processors

Yamazaki, T., Igarashi, J., Makino, J., & Ebisuzaki, T. (2017) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342017710705 · Cited by 21

Risk Analysis of Reservoir Operations Considering Short-Term Flood Control and Long-Term Water Supply: A Case Study for the Da-Han Creek Basin in Taiwan

Cheng, W.-M., Huang, C., Hsu, N., & Wei, C. (2017) · Water · doi:10.3390/w9060424 · Cited by 21

Parameterization of typhoon-induced ocean cooling using temperature equation and machine learning algorithms: an example of typhoon Soulik (2013)

Wei, J., Jiang, G., & Liu, X. (2017) · Ocean Dynamics · doi:10.1007/s10236-017-1082-z · Cited by 21

Daily rainfall forecast model from satellite image using Convolution neural network

Boonyuen, K., Kaewprapha, P., & Srivihok, P. (2018) · doi:10.23919/incit.2018.8584886 · Cited by 21

Machine Learning Algorithm-Based Tool and Digital Framework for Substituting Daylight Simulations in Early-Stage Architectural Design Evaluation

Radziszewski, K., & Waczyńska, M. (2018) · Symposium on Simulation for Architecture and Urban Design (SimAUD) · doi:10.22360/simaud.2018.simaud.001 · Cited by 21

Network-Oriented Modeling and Its Conceptual Foundations

Jan Treur, A. (2016) · Understanding complex systems · doi:10.1007/978-3-319-45213-5_1 · Cited by 21

An Automated Vehicle Fuel Economy Benefits Evaluation Framework Using Real-World Travel and Traffic Data

Zhu, L., Gonder, J. D., Bjärkvik, E., Pourabdollah, M., & Lindenberg, B. (2019) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2019.2919537 · Cited by 21

Sensitivity and Robustness of Neural Networks for Defect-Depth Estimation in CFRP Composites

Saeed, N., Zarkani, H. A., & Omar, M. (2019) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-019-0607-4 · Cited by 21

Brain Computation: A Computer Science Perspective

Maass, W., Papadimitriou, C. H., Vempala, S., & Legenstein, R. A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-319-91908-9_11 · Cited by 21

Growing field of materials informatics: databases and artificial intelligence

López‐Bezanilla, A., & Littlewood, P. B. (2020) · MRS Communications · doi:10.1557/mrc.2020.2 · Cited by 21

Artificial neural networks for cloud masking of Sentinel-2 ocean images with noise and sunglint

Kristollari, V., & Karathanassi, V. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1714776 · Cited by 21

MoveAE

Suguitan, M., Gómez, R., & Hoffman, G. (2020) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3319502.3374807 · Cited by 21

Cancer-Related Gene Signature Selection Based on Boosted Regression for Multilayer Perceptron

Seo, H., & Cho, D. (2020) · IEEE Access · doi:10.1109/access.2020.2985414 · Cited by 21

A primer on artificial intelligence for the paediatric cardiologist

Gearhart, A. S., Gaffar, S., & Chang, A. C. (2020) · Cardiology in the Young · doi:10.1017/s1047951120001493 · Cited by 21

Poroelastic model parameter identification using artificial neural networks: on the effects of heterogeneous porosity and solid matrix Poisson ratio

Dehghani, H., & Zilian, A. (2020) · Computational Mechanics · doi:10.1007/s00466-020-01868-4 · Cited by 21

Introduction to Artificial Intelligence

Chang, A. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-823337-5.00027-5 · Cited by 21

The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks

Gilson, M., Dahmen, D., Moreno‐Bote, R., Insabato, A., & Helias, M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008127 · Cited by 21

Neuronized Priors for Bayesian Sparse Linear Regression

Shin, M., & Liu, J. S. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1876710 · Cited by 21

Niobium Neuron: RSFQ Based Bio-Inspired Circuit

Feldhoff, F., & Toepfer, H. (2021) · IEEE Transactions on Applied Superconductivity · doi:10.1109/tasc.2021.3063212 · Cited by 21

A Survey of Algorithmic and Hardware Optimization Techniques for Vision Convolutional Neural Networks on FPGAs

Sateesan, A., Sinha, S., Smitha, K. G., & Vinod, A. P. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10458-1 · Cited by 21

A Partially Interpretable Adaptive Softmax Regression for Credit Scoring

Munkhdalai, L., Ryu, K. H., Namsrai, O.-E., & Theera‐Umpon, N. (2021) · Applied Sciences · doi:10.3390/app11073227 · Cited by 21

A Novel Multicategory Defect Detection Method Based on the Convolutional Neural Network Method for TFT-LCD Panels

Chang, Y.-C., Chang, K., Meng, H.-M., & Chiu, H.-C. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/6505372 · Cited by 21

A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering

Chávez, T., Roberts, E. J., Zwart, P. H., & Hexemer, A. (2022) · Journal of Applied Crystallography · doi:10.1107/s1600576722007105 · Cited by 21

A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir

Jiang, R., Ji, Z.-F., Mo, W., Wang, S., Zhang, M., Yin, W., Wang, Z., Lin, Y.-P., Wang, X., & Ashraf, U. (2022) · Energies · doi:10.3390/en15197016 · Cited by 21

Connectionism and Neural Networks

Barrow, H. G. (1996) · Elsevier eBooks · doi:10.1016/b978-012161964-0/50007-8 · Cited by 21

A Second-Order Perceptron Algorithm

Cesa‐Bianchi, N., Conconi, A., & Gentile, C. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45435-7_9 · Cited by 21

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)