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

No more news from the cerebellum

Vincent, S. R. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081978 · Cited by 2

The role of the cerebellum in motor learning is limited

Hallett, M. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0008167x · Cited by 2

Cerebellar arm ataxia: Theories still have a lot to explain

Hore, J. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081723 · Cited by 2

How and what does the cerebellum learn?

Gilbert, P. F. C. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081644 · Cited by 2

Saccades and the adjustable pattern generator

Dean, P. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081589 · Cited by 2

Connectionism: Self-abuse is improper treatment

Oden, G. C. (1990) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00079437 · Cited by 2

A bridge between cerebellar long-term depression and discrete motor learning: Studies on gene knockout mice

Kano, M. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081966 · Cited by 2

Hierarchical clustering using K-Iterations Fast Learning Artificial Neural Networks (KFLANN)

Wong, L.-P. (2007) · doi:10.32657/10356/2530 · Cited by 2

Evolutionary discovery of learning rules for feedforward neural networks with step activation function

Radi, A., & Poli, R. (1999) · Cited by 2

Novel Machine Learning Methods for Computational Chemistry

Hansen, K. (2012) · doi:10.14279/depositonce-3309 · Cited by 2

Development and validation of two influenza assessments : exploring the impact of knowledge and social environment on health behaviors

Romine, W. L. (2011) · doi:10.32469/10355/14449 · Cited by 2

Das gustatorische System und antennales Lernen in der Honigbiene (Apis mellifera L.)

Haupt, S. S. (2005) · doi:10.14279/depositonce-1239 · Cited by 2

Stability and weight smoothing in cmac neural networks

Kraft, L. G., & Campagna, D. P. (1998) · Cited by 2

Models and reality

Searle, J. R. (1990) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00079401 · Cited by 2

A deep learning model for automatic image texture classification: Application to vision-based automatic aircraft landing

Lai, K. P. (2016) · School of Electrical Engineering & Computer Science; Science & Engineering Faculty · Cited by 2

Modulation channels in biomimic artificial neurons

Wells, R. B. (2003) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2002.1182912 · Cited by 2

Uma proposta para a obtenção da população sintética através de dados agregados para modelagem de geração de viagens por domicílio

Pianucci, M. N. (2016) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.18.2016.tde-24102016-154347 · Cited by 2

Application of neural networks to lesion detection in SPECT

Tourassi, G. D., Floyd, C. E., Munley, M. T., Bowsher, J. E., & Coleman, R. E. (2002) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.1991.259305 · Cited by 2

Comparative analysis of classification models for diagnosis Type 2 Diabetes

Almadni, D. (2021) · doi:10.32920/ryerson.14660673 · Cited by 2

Prognóstico de falhas baseado em redes neurais com estados de eco

Simeón, E. J. A. (2015) · doi:10.26512/2015.08.t.20510 · Cited by 2

Prediction and control in a just-in-time environment using neural networks

Wray, B., & Rakes, T. R. (1992) · VTechWorks (Virginia Tech) · Cited by 2

Le robot mobile Type 1

Lucidarme, P., & Simonin, O. (2006) · HAL · Cited by 2

Spectral techniques and soft computing

Moraga, C. (1998) · Approximation Theory and Its Applications · doi:10.1007/bf02856144 · Cited by 2

Exploiting discriminative language models for rerankin speech recognition hypotheses

Liu, C. W., Lin, S. H., & Chen, B. (2010) · Cited by 2

Predição de séries temporais por similaridade

Parmezan, A. R. S. (2016) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2016.tde-21112016-150659 · Cited by 2

Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?Preprint

Burkard, C., & Lagesse, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.05586 · Cited by 2

The Rescorla-Wagner Drift-Diffusion Model

Luzardo, A. (2018) · City Research Online (City University London) · doi:10.13140/rg.2.2.23320.08963 · Cited by 2

Prediction of Capillary Pressure and Relative Permeability Curves using Conventional Pore-scale Displacements and Artificial Neural Networks

Liu, S. (2017) · KU ScholarWorks (The University of Kansas) · Cited by 2

A neural network catalyzer for multi-dimensional similarity search.Preprint

Sablayrolles, A., Douze, M., Schmid, C., & Jeǵou, H. (2018) · arXiv (Cornell University) · Cited by 2

Theory and modeling of complex nonlinear delay dynamics applied to neuromorphic computing

Penkovsky, B. (2017) · theses.fr (ABES) · doi:10.70675/087b5637zca62z4ce7za9b1z4aedede2adf5 · Cited by 2

Bidirectional Learning in Recurrent Neural Networks Using Equilibrium Propagation

Khan, A. F. (2018) · UWSpace (University of Waterloo) · Cited by 2

Measure, Manifold, Learning, and Optimization: A Theory Of Neural NetworksPreprint

Li, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.12783 · Cited by 2

Using Capsule Networks for Image and Speech Recognition Problems

Xiong, Y. (2018) · Cited by 2

Monitoring of premature newborns by video and audio analyses

Cabon, S. (2019) · theses.fr (ABES) · doi:10.70675/42108a13zb4d8z4abbz86f0z65a256ca829a · Cited by 2

Online Machine Learning Algorithms over Data Streams

Benczúr, A. A., Kocsis, L., & Pálovics, R. (2018) · Springer eBooks · doi:10.1007/978-3-319-63962-8_329-1 · Cited by 2

A Multilingual Encoding Method for Text Classification and Dialect Identification Using Convolutional Neural NetworkPreprint

Helmy, A. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.07588 · Cited by 2

Special Section on Image Cytometry

Bengtsson, E. W., & Tárnok, A. (2019) · Cytometry Part A · doi:10.1002/cyto.a.23762 · Cited by 2

Design of Artificial Intelligence Agents for Games using Deep Reinforcement LearningPreprint

Roibu, A. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04127 · Cited by 2

Online Federated Learning over decentralized networks

Zhang, C. (2018) · doi:10.32657/10220/47928 · Cited by 2

Wireless Chip-Scale Communications for Neural Network Accelerators

Liñán, R. (2019) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · Cited by 2

Effects of artificial neural network speed-based inputs on heavy-duty vehicle emissions prediction

Hashemi, N. N. (2004) · doi:10.33915/etd.1490 · Cited by 2

Deep Grassmann Manifold Optimization for Computer Vision

Minnehan, B. (2019) · RIT Scholar Works (Rochester Institute of Technology) · Cited by 2

Quantifying and modelling non-local information processing of associative brain regionsPreprint

Balkenhol, J., Prada, J., Ehrenreich, H., Grohmann, J., von Kistowski, J., Wojcik, S. M., Blum, R., Kounev, S., & Dandekar, T. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.19.883124 · Cited by 2

Deep learning methods for radio fingerprinting

Rendon, B. C. (2019) · doi:10.17760/d20328700 · Cited by 2

The effect of depth context in the segmentation of the colon in MRI volumesPreprint

Benson, E., Rier, L., Millican, I., Pritchard, S., Costigan, C. M., Pound, M. P., Major, G., French, A. P., Gowland, P. A., Pridmore, T., et al. (2020) · medRxiv · doi:10.1101/2020.03.06.20027722 · Cited by 2

Time-Series Classification with Uni-Dimensional Convolutional Neural Networks: An Experimental Comparison with Long Short-Term Memory Networks

Kulam, S. (2019) · NORA - Norwegian Open Research Archives · Cited by 2

On-the-Fly Joint Feature Selection and ClassificationPreprint

Liyanage, Y. W., Zois, D., & Chelmis, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.10245 · Cited by 2

A Study of Neural Training with Non-Gradient and Noise Assisted Gradient Methods.Preprint

Mukherjee, A., & Muthukumar, R. (2020) · arXiv (Cornell University) · Cited by 2

Deep learning for promoter recognition: a robust testing methodology

Martell, P., & Iván, R. (2020) · UVic’s Research and Learning Repository (University of Victoria) · Cited by 2

Improving Bluetooth-based Indoor Positioning Using Vision and Artificial Networks

Naghdi, S. (2020) · doi:10.11575/prism/38004 · Cited by 2

Using neural networks to predict icephobic performancePreprint

Ramachandran, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00966 · Cited by 2

Photometric redshifts for S-PLUS using machine learning techniques

de Lima, E. V. R. (2019) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.14.2019.tde-14082020-174722 · Cited by 2

Testing and Emulating Modified Gravity on Cosmological ScalesPreprint

Tamosiunas, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.08786 · Cited by 2

Learning Halfspaces with Pairwise Comparisons: Breaking the Barriers of Query Complexity via Crowd Wisdom.Preprint

Zeng, S., & Shen, J. (2020) · arXiv (Cornell University) · Cited by 2

Spatially Correlated Patterns in Adversarial ImagesPreprint

Chattopadhyay, N., Zhi, L. Y. E., Xing, B. T. B., & Chattopadhyay, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.10794 · Cited by 2

Editorial: Artificial Intelligence: A New Transactions

Abbass, H. A. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2020.3035827 · Cited by 2

Modern Machine and Deep Learning Systems as a way to achieve Man-Computer SymbiosisPreprint

Gupta, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.10534 · Cited by 2

Indicators of mastitis and milk quality in dairy cows : data, modeling, and prediction in automatic milking systems

Anglart, D. (2021) · Epsilon Open Archive (Sveriges lantbruksuniversitet biblioteket (Swedish University of Agricultural Sciences)) · Cited by 2

Agnostic learning with unknown utilitiesPreprint

Bhatia, K., Bartlett, P. L., Dragan, A. D., & Steinhardt, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08482 · Cited by 2

Covariance-based information processing in reservoir computing systemsPreprint

Lawrie, S., Moreno‐Bote, R., & Gilson, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.30.441789 · Cited by 2

Artificial Intelligence in Medicinal Chemistry

Griffen, E., Dossetter, A. G., Leach, A. G., & Montague, S. (2021) · Wiley eBooks · doi:10.1002/0471266949.bmc267 · Cited by 2

A Spiking Bidirectional Associative Memory Neural Network

Johnson, M. G. (2021) · uO Research (University of Ottawa) · doi:10.20381/ruor-26444 · Cited by 2

Quantum Perceptron Revisited: Computational-Statistical TradeoffsPreprint

Roget, M., Molfetta, G. D., & Kadri, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02496 · Cited by 2

On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial NoisePreprint

Shen, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.10793 · Cited by 2

A modular U-Net for automated segmentation of X-ray tomography images in composite materialsPreprint

Bertoldo, J. P. C., Decencière, É., Ryckelynck, D., & Proudhon, H. (2021) · Research Square · doi:10.21203/rs.3.rs-721240/v1 · Cited by 2

On the Intersection of Deep Learning and Chest Radiography: Background and ProspectsPreprint

Roy, S. S., & Choudhury, A. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3861229 · Cited by 2

Convolutional neural networks in osteoporotic fracture risk prediction using spine DXA images

Nissinen, T. (2019) · UEF eRepo (University of Eastern Finland) · Cited by 2

A New Spin on Optimal Portfolios and Ecological EquilibriaPreprint

Garnier-Brun, J., Benzaquen, M., Ciliberti, S., & Bouchaud, J. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3817718 · Cited by 2

Learning Models To Control Redundancy In Robotics

Salaün, C. (2010) · HAL · Cited by 2

Fundamentos para el Submódulo Evaluador en Sistemas Tutores Inteligentes: Diagnóstico, predicción y autoevaluación

Cataldi, Z., Salgueiro, F. A., & Lage, F. J. (2007) · El Servicio de Difusión de la Creación Intelectual (National University of La Plata) · Cited by 2

The evolutionary emergence of neural organisation in computational models of primitive organisms

Jones, B. H. D. (2010) · University of Birmingham Institutional Research Archive (University of Birmingham) · Cited by 2

Integrating parsing and word alignment in syntax-based machine translation

Abney, S. P., & Fossum, V. (2010) · Deep Blue (University of Michigan) · Cited by 2

Evaluation and Forecasting Meteorological Drought, Case Study: Kohgilooyeh and Boyer AhmadPreprint

Razmkhah, H., Rostami, E., Ravari, A. R., & Fararouie, A. (2021) · Research Square · doi:10.21203/rs.3.rs-989454/v1 · Cited by 2

Utilidad de las nuevas tecnologías en la mejora de la comunicación médico-paciente en el área de salud mental: aportaciones de la inteligencia artificial y el procesamiento del lenguaje natural

Escrivá, J. V. S. (2021) · doi:10.6035/14111.2021.301397 · Cited by 2

Fast Quaternion Product Units for Learning Disentangled Representations in SO(3)Preprint

Qin, S., Zhang, X., Xu, H., & Xu, Y. (2022) · doi:10.36227/techrxiv.17791574 · Cited by 2

New methods for adaptive equalization based on Information Theoretic Learning

Fantinato, D. G. (2017) · doi:10.47749/t/unicamp.2017.982099 · Cited by 2

Deep learning em dois estágios para detecção e classificaçãode doenças em folhas de plantas com aplicação em dispositivos móveis

Leite, T. D. M. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2021.tde-02022022-121135 · Cited by 2

Literatur

Schwefel, H. (1977) · Birkhäuser Basel eBooks · doi:10.1007/978-3-0348-5927-1_8 · Cited by 2

Prediction of Aedes aegypti breeding distribution through spatiotemporal analysis and machine learning: A case study in Recife, PernambucoPreprint

de Lima, C. L., da Silva, C. C., da Silva, A. C. G., da Silva, A. A., de Almeida, F. R., de Gusmão, C. M. G., Moreno, G. M. M., Musah, A., Aldosery, A., Browning, E., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1200442/v1 · Cited by 2

Artificial Intelligence Software Structured to Simulate Human Working Memory, Mental Imagery, and Mental ContinuityPreprint

Reser, J. E. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.05138 · Cited by 2

Adverse medical event reduction technologies: identification of primary risk factors of adverse medical events using artificial neural networks

Rideout, K. (2006) · doi:10.22215/etd/2006-07712 · Cited by 2

Medical imaging

Sun, Y., & Nekovei, R. (1998) · Neural network systems techniques and applications · doi:10.1016/s1874-5946(98)80027-8 · Cited by 2

Perceptrons

Sussner, P. (1999) · Wiley eBooks · doi:10.1002/047134608x.w5112 · Cited by 2

It may be time to improve the neuron of artificial neural networkPreprint

Liu, G. (2020) · doi:10.36227/techrxiv.12477266.v8 · Cited by 2

The Semantic Librarian: A Search Engine Built from Vector-Space Models of SemanticsPreprint

Aujla, H., Crump, M. J. C., Cook, M. T., & Jamieson, R. K. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/svmtd · Cited by 2

Review article

Nelson, R. J. (1980) · Synthese · doi:10.1007/bf00870296 · Cited by 2

A Comparison of Machine-Learning Assisted Optical and Thermal Camera Systems for Beehive Activity CountingPreprint

Williams, S. M., Bariselli, S., Palego, C., Holland, R., & Coss, P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.3990570 · Cited by 2

It may be time to improve the neuron of artificial neural networkPreprint

Liu, G. (2020) · doi:10.36227/techrxiv.12477266.v7 · Cited by 2

It may be time to improve the neuron of artificial neural networkPreprint

Liu, G. (2021) · doi:10.36227/techrxiv.12477266.v9 · Cited by 2

DIRECT AND INDIRECT QUOTATION EXTRACTION FOR PORTUGUESE

SILVA, R. D. R. (2017) · doi:10.17771/pucrio.acad.30254 · Cited by 2

It may be time to perfect the neuron of artificial neural networkPreprint

Liu, G. (2020) · doi:10.36227/techrxiv.12477266.v2 · Cited by 2

Bibliography

(2014) · Elsevier eBooks · doi:10.1016/b978-0-12-800953-6.00020-7 · Cited by 2

A first undergraduate course in neural networks

Abunawass, A. M., Bukhres, O., Fisher, T. G., & Magel, K. I. (1990) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/323410.323464 · Cited by 2

Online Learning

Auer, P. L. (2010) · Springer eBooks · doi:10.1007/978-0-387-30164-8_613 · Cited by 2

Contributions to Representation Learning with Graph Autoencoders and Applications to Music RecommendationPreprint

Salha-Galvan, G. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.14651 · Cited by 2

3D Convolutional Neural Networks for Solving Complex Digital Agriculture and Medical Imaging Problems

Hamila, O. (2022) · doi:10.36939/ir.202206021141 · Cited by 2

Wasserstein GAN: Deep Generation Applied on Financial Time SeriesPreprint

Pfenninger, M., Bigler, D. N., Rikli, S., & Osterrieder, J. R. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3885659 · Cited by 2

(Non)-neutrality of science and algorithms: Machine Learning between fundamental physics and societyPreprint

Lampo, A., Mancarella, M., & Piga, A. (2018) · arXiv (Cornell University) · doi:10.13131/1724-451x.labsquarterly.axx.n4.117-145 · Cited by 2

People's Councils for Ethical Machine LearningPreprint

McQuillan, D. (2018) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/sjpmc · Cited by 2

Multi-scale modeling of crystal-fluid interactions: State-of-the-art, challenges and prospects

Kurganskaya, I., Rohlfs, R. D., & Lüttge, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85669-0.00034-9 · Cited by 2

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)