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

An Assessment of Students’ Satisfaction in Higher Education

Figueiredo, M., Fernandes, A., Ribeiro, J., Neves, J., Dias, A., & Vicente, H. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-52538-5_16 · Cited by 4

Gender Perception From Gait: A Comparison Between Biological, Biomimetic and Non-biomimetic Learning Paradigms

Sarangi, V., Pelah, A., Hahn, W. E., & Barenholtz, E. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00320 · Cited by 4

INVITED: Efficient Synthesis of Compact Deep Neural Networks

Xia, W., Yin, H., & Jha, N. K. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218529 · Cited by 4

Intelligence Is beyond Learning: A Context-Aware Artificial Intelligent System for Video Understanding

Ghozia, A., Attiya, G., Adly, E., & El-Fishawy, N. A. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8813089 · Cited by 4

Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing

Palacios, D., Arzamendia, M., Gregor, D. O., Cikel, K., Ovelar, R. L., & Villagra, M. (2021) · AgriEngineering · doi:10.3390/agriengineering3010001 · Cited by 4

A Survey on Mathematical Aspects of Machine Learning in GeoPhysics: The Cases of Weather Forecast, Wind Energy, Wave Energy, Oil and Gas Exploration

Kosanić, M., & Milutinović, V. M. (2021) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco52532.2021.9460245 · Cited by 4

Ray-Tracing Driven ANN Propagation Models for Indoor Environments at 28 GHz

Seretis, A., Hashimoto, T., Zeng, K., & Sarris, C. D. (2020) · Digest - IEEE Antennas and Propagation Society. International Symposium · doi:10.1109/ieeeconf35879.2020.9330037 · Cited by 4

Neural Network-Based Prediction Model for Evaporation Using Weather Data

Batra, K., & Gandhi, P. (2021) · Agricultural Research · doi:10.1007/s40003-021-00537-z · Cited by 4

Single perceptron at 12 GigaOPs based on a microcomb for versatile, high-speed scalable, optical neural networks

Tan, M., Xu, M., Wu, J., boes, A., Corcoran, B., Nguyen, T. G., Chu, S. T., Little, B. E., Morandotti, R., Mitchell, A., et al. (2021) · Smart Photonic and Optoelectronic Integrated Circuits · doi:10.1117/12.2584011 · Cited by 4

Characterisation of environment type and difficulty for streamed data classification problems

Ellis, M., Bosman, A. S., & Engelbrecht, A. P. (2021) · Information Sciences · doi:10.1016/j.ins.2021.05.023 · Cited by 4

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

Campbell, N. H., Grauer, J. A., & Gregory, I. M. (2021) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero50100.2021.9438231 · Cited by 4

Deciphering musculoskeletal artificial intelligence for clinical applications: how do I get started?

Mutasa, S., & Yi, P. H. (2021) · Skeletal Radiology · doi:10.1007/s00256-021-03850-4 · Cited by 4

Imagen y poder en la era de la visión artificial: una interpretación a partir de Gilbert Simondon y Gilles Deleuze

Bueno, C. C. (2021) · Atenea · doi:10.29393/atat523-417ccip10417 · Cited by 4

A GA approach to Optimization of Convolution Neural Network

Naulia, P. S., Watada, J., Aziz, I. A., & Roy, A. (2021) · International Conference on Computer and Information Sciences · doi:10.1109/iccoins49721.2021.9497147 · Cited by 4

Deep Learning Credit Risk Modeling

Manzo, G., & Qiao, X. (2020) · The Journal of Fixed Income · doi:10.3905/jfi.2021.1.121 · Cited by 4

Role of Artificial Intelligence in Medicine and Clinical Research

Sarma, J. (2021) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2021.37617 · Cited by 4

Fast Detection of Centrifugal Pumps Condition by Structural Analysis of MEMS Sensor Signals

de Araújo, R. D. B., Rocha, J. M. M., dos Santos, M. A., & Ramalho, G. L. B. (2021) · Journal of Control Automation and Electrical Systems · doi:10.1007/s40313-021-00806-w · Cited by 4

Accelerating Deep Neural Networks Using FPGAs and ZYNQ

Lee, H. S., & Jeon, J. W. (2021) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp52854.2021.9550853 · Cited by 4

Encoding a Categorical Independent Variable for Input to TerrSet’s Multi-Layer Perceptron

Evenden, E., & Pontius, R. G. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10100686 · Cited by 4

Application of Computational Chemistry for Contaminant Adsorption on the Components of Soil Surfaces

Jenness, G. R., Lystrom, L., McAlexander, H. R., & Shukla, M. K. (2021) · Springer eBooks · doi:10.1007/978-3-030-83244-5_5 · Cited by 4

Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks

Hillar, C. J., Chan, T., Taubman, R., & Rolnick, D. (2021) · Entropy · doi:10.3390/e23111494 · Cited by 4

Evolution of the Marr-Albus-Ito Model

Yamazaki, T. (2021) · Contemporary clinical neuroscience · doi:10.1007/978-3-030-75817-2_11 · Cited by 4

Nonlinearity encoding to improve extrapolation capabilities for unobserved physical states

Na, G. S., Jang, S., & Chang, H. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp04450h · Cited by 4

Artificial Intelligence for Brain Molecular Imaging

Cross, D. J., Komori, S., & Minoshima, S. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.08.001 · Cited by 4

An Intelligent Tool to Support Diagnosis of Covid-19 by Texture Analysis of Computerized Tomography X-ray Images and Machine Learning

de Santana, M. A., Gomes, J. C., Barbosa, V. A. D. F., de Lima, C. L., Bandeira, J., Valença, M., de Souza, R. E., Masood, A. I., & dos Santos, W. P. (2021) · Springer eBooks · doi:10.1007/978-3-030-79753-9_15 · Cited by 4

Solving nonlinear classification and regression problems by wave interference with a single-layer complex-valued perceptron

Dellen, B. K., & Jaekel, U. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9659865 · Cited by 4

Scaling deep learning for whole-core reactor simulation

Shriver, F., & Watson, J. (2022) · Progress in Nuclear Energy · doi:10.1016/j.pnucene.2022.104134 · Cited by 4

THEORY AND DIGITAL TECHNOLOGIES OF INTELLECTUAL SUPPORT FOR DECISION-MAKING TO INCREASE THE BIO-PRODUCTIVITY OF AGROECOSYSTEMS BASED ON NEURAL NETWORK MODELS

(2021) · doi:10.32786/2071-9485-2021-04-42 · Cited by 4

Mathematical Model of a Recurrent Neural Network for Programmable Devices Focused on Fog Computing

Podlesnykh, I. A., & Bakhtin, V. V. (2022) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/elconrus54750.2022.9755677 · Cited by 4

Multi-Sensor Approach to Automated Classification of Sea Ice Image Data

Bobylev, L., Alexandrov, V., Sandven, S., Johannessen, O., & Bogdanov, A. (2006) · CRC Press eBooks · doi:10.1201/9781420003130-32 · Cited by 4

The Historiography of the Sciences of the Brain and Nervous System

Chirimuuta, M. (2021) · Historiographies of science · doi:10.1007/978-3-319-90119-0_23 · Cited by 4

AI Fundamentals

Lee, R. (2020) · Springer eBooks · doi:10.1007/978-981-15-7695-9_2 · Cited by 4

Self-Replication in Neural Networks

Gabor, T., Illium, S., Zorn, M., Lenta, C., Mattausch, A., Belzner, L., & Linnhoff–Popien, C. (2022) · Artificial Life · doi:10.1162/artl_a_00359 · Cited by 4

Feature Activation through First Power Linear Unit with Sign

Duan, B., Yang, Y., & Dai, X. (2022) · Electronics · doi:10.3390/electronics11131980 · Cited by 4

Deep learning

Cannataro, M., Guzzi, P. H., Agapito, G., Zucco, C., & Milano, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822952-1.00014-0 · Cited by 4

Quantum Data Classification by a Dissipative Protocol with a Superconducting Quantum Circuit Implementation

Korkmaz, U., Sanga, C., & Türkpençe, D. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-01984-5_13 · Cited by 4

An Electric Circuit Model of Central Auditory Processing that Replicates Low-level Features of the Mouse Mismatch Response

O’Reilly, J. A. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871275 · Cited by 4

Toward a Better Understanding of Mobile Users’ Behavior: A Web Session Repair Scheme

Charmet, F., Morikawa, T., & Takahashi, T. (2022) · IEEE Access · doi:10.1109/access.2022.3206402 · Cited by 4

Cybernétique, interactivité et conversation : retour sur les effets des expériences de Gordon S. Pask en architecture

Claeys, D., & Roobaert, L. (2022) · SHS Web of Conferences · doi:10.1051/shsconf/202214706002 · Cited by 4

Magyar halandósági ráták előrejelzése visszacsatolt neurális hálózatokkal

Szentkereszti, G., & Vékás, P. (2022) · Statisztikai Szemle · doi:10.20311/stat2022.10.hu0905 · Cited by 4

Empirical, Statistical, and Machine Learning Techniques for Predicting Surface Settlement Induced by Tunnelling

Huat, C. Y., Armaghani, D. J., Momeni, E., & Lai, S. H. (2023) · Emerging trends in mechatronics · doi:10.1007/978-981-19-8790-8_2 · Cited by 4

Neural networks to retrieve in water constituents applied to radiative transfer models simulating coastal water conditions

Hadjal, M., Paterson, R., & McKee, D. (2023) · Frontiers in Remote Sensing · doi:10.3389/frsen.2023.973944 · Cited by 4

Integrative Scoring System for Survival Prediction in Patients With Locally Advanced Nasopharyngeal Carcinoma: A Retrospective Multicenter Study

Zhang, B., Luo, C. L., Zhang, X., Hou, J. C., Liu, S., Gao, M., Zhang, L., Jin, Z., Chen, Q., Yu, X., et al. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00015 · Cited by 4

Artificial Intelligence Accelerators

Mishra, A., Yadav, P., & Kim, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-22170-5_1 · Cited by 4

Statistical Downscaling in the Tropics and Midlatitudes: A Comparative Assessment over Two Representative Regions

Hernanz, A., Correa, C., Domínguez, M., Rodríguez‐Guisado, E., & Camino, E. R. (2023) · Journal of Applied Meteorology and Climatology · doi:10.1175/jamc-d-22-0164.1 · Cited by 4

Inflow Prediction of Centralized Reservoir for the Operation of Pump Station in Urban Drainage Systems Using Improved Multilayer Perceptron Using Existing Optimizers Combined with Metaheuristic Optimization Algorithms

Lee, E. H. (2023) · Water · doi:10.3390/w15081543 · Cited by 4

Generative Adversarial Networks: A Brief History and Overview

Gunasekaran, A. (2023) · Journal of Student Research · doi:10.47611/jsr.v12i1.1848 · Cited by 4

An extension of the complex–real (C–R) calculus to the bicomplex setting, with applications

Alpay, D., Diki, K., & Vajiac, M. (2023) · Mathematische Nachrichten · doi:10.1002/mana.202200475 · Cited by 4

Architecture of Deep Learning and Its Applications

Dawood, A. S., & Faris, Z. M. (2023) · Iraqi Journal of Computer Communication Control and System Engineering · doi:10.33103/uot.ijccce.23.1.4 · Cited by 4

The Machine Learning Methods in Non-Destructive Testing of Dynamic Properties of Vacuum Insulated Glazing Type Composite Panels

Kozanecki, D., Kowalczyk, I., Krasoń, S., Rabenda, M., Domagalski, Ł., & Wirowski, A. (2023) · Materials · doi:10.3390/ma16145055 · Cited by 4

Efficient NLP Model Finetuning via Multistage Data Filtering

Ouyang, X., Ansari, S. M. A., Lin, F. X., & Ji, Y. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/455 · Cited by 4

Single-layer perceptron artificial visual system for orientation detection

Todo, H., Chen, T., Ye, J., Li, B., Todo, Y., & Tang, Z. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1229275 · Cited by 4

RADAR Echo Recognition of Squall Line Based on Deep Learning

Xie, P., Hu, Z., Yuan, S., Zheng, J., Tian, H., & Fen, X. (2023) · Remote Sensing · doi:10.3390/rs15194726 · Cited by 4

Medical image processing using graph convolutional networks: A review

Liu, L., Zhu, X., Wu, J., Hu, Q., Cui, H., Chen, Z., & Xu, T. (2023) · Perioperative Precision Medicine · doi:10.61189/803479emewvv · Cited by 4

Generalization of graph network inferences in higher-order graphical models

Fei, Y., & Pitkow, X. (2023) · Journal of Applied and Computational Topology · doi:10.1007/s41468-023-00147-4 · Cited by 4

Automated Classification of Phonetic Segments in Child Speech Using Raw Ultrasound Imaging

Ani, S. A., Cleland, J., & Zoha, A. (2024) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0012592700003657 · Cited by 4

Malware Detection Using Machine Learning

Sahu, B. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem30244 · Cited by 4

Causality and Artificial Intelligence

Vallverdú, J. (2024) · Springer eBooks · doi:10.1007/978-981-97-3187-9_2 · Cited by 4

Best Scanline Determination of Pushbroom Images for a Direct Object to Image Space Transformation Using Multilayer Perceptron

Nezhad, S. S. A., Zoej, M. J. V., Khoshelham, K., Ghorbanian, A., Farnaghi, M., Jamali, S., Youssefi, F., & Gheisari, M. (2024) · Remote Sensing · doi:10.3390/rs16152787 · Cited by 4

Trends Innovations Challenges in Employing Interdisciplinary Approaches to Biomedical Sciences

Ganeshkumar, M., Ayyadhury, S., & Murugan, E. (2024) · Springer eBooks · doi:10.1007/978-981-97-1777-4_20 · Cited by 4

Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium

Carnazza, F., Carollo, F., Andergassen, S., Martius, G., Klopotek, M., & Lesanovsky, I. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad7ad7 · Cited by 4

Using AI to support safety management – Analysing occupational safety data using machine learning within the framework of human factors

Tiikkaja, M., Kannisto, H., Nurmi, A., Puro, V., Heikkilä, T., Kivimäki, I., Asikainen, I., & Teperi, A.-M. (2026) · Safety Science · doi:10.1016/j.ssci.2026.107434 · Cited by 4

Nonconvergent Dynamics and Cognitive Systems

Harter, D., & Kozma, R. (2006) · eScholarship (California Digital Library) · Cited by 4

A Conditional Model for Tonal Analysis

Radicioni, D. P., & Esposito, R. (2006) · Lecture notes in computer science · doi:10.1007/11875604_72 · Cited by 4

Image Transformation: Inductive Transfer between Multiple Tasks Having Multiple Outputs

Silver, D. L., & Tu, L. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-68825-9_28 · Cited by 4

Development of Two GIS-based Modeling Frameworks to Identify Suitable Lands for Sugarcane Cultivation

Jayasinghe, P. K. S. C., & Yoshida, M. (2010) · Nettai Nogyo/Nettai nougyou · doi:10.11248/jsta.54.51 · Cited by 4

Comprehensible hierarchical intelligent (CHI) framework for monitoring and preventive maintenance of aircraft systems

Owotoki, F. M.-L. P. (2005) · Workshop on Intelligent Solutions in Embedded Systems · doi:10.1109/wises.2005.1438726 · Cited by 4

Artificial Neural Networks for Analysis and Recognition of Primate Vocal Communication

Zimmermann, A. P. (1995) · Springer eBooks · doi:10.1007/978-1-4757-9930-9_2 · Cited by 4

Optimum design of ship design system using neural network method in initial design of hull plate

Kim, S., Moon, B.-Y., & Kim, D.-E. (2004) · KSME International Journal · doi:10.1007/bf02990434 · Cited by 4

Interdisciplinary issues for the management of next generation autonomic wireless systems: nature-inspired techniques and organic computing

Rango, F. D., Veltri, F., Tropea, M., Santamaria, A. F., Fazio, P., Malfitano, A., & Marano, S. (2007) · International Journal of Mobile Network Design and Innovation · doi:10.1504/ijmndi.2007.017318 · Cited by 4

COMPLEXITY: METRICS AND MODULES

Bossomaier, T. R. J. (2003) · Advances in Complex Systems · doi:10.1142/s0219525903000992 · Cited by 4

Dynamical trajectories in category learning

Ell, S. W., & Ashby, F. G. (2004) · Perception & Psychophysics · doi:10.3758/bf03195001 · Cited by 4

A multiple approach to data analysis and uncertainty management in knowledge-based systems

Schuster, A., Shapcott, M., Adamson, K., & Bell, D. A. (2000) · International Journal of Intelligent Systems · doi:10.1002/(sici)1098-111x(200002)15:2 · Cited by 4

A TASK-FREE CONCEPT LEARNING MODEL EMPLOYING GENERALIZATION AND ABSTRACTION TECHNIQUES

Brown, D. J. (1979) · Journal of Cybernetics · doi:10.1080/01969727908927615 · Cited by 4

On Existential and Constructive Neural Complexity Results.

Beiu, V. C. (2003) · Cited by 4

Multistate perceptrons: learning rule and perceptron of maximal stability

Elizalde, E., & Gómez, S. (1992) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/25/19/016 · Cited by 4

The algorithm of pipelined gossiping

Florio, V. D., & Blondia, C. L. (2005) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2005.08.001 · Cited by 4

A Generative/Discriminative Hybrid Model: Bayes Perceptron Classifier

Liu, J., Song, J.-Q., & Huang, Y. (2007) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2007.4370618 · Cited by 4

Learning Model Complexity in an Online Environment

Levi, D., & Ullman, S. (2009) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2009.52 · Cited by 4

Finding sensors for homeostasis of biological neuronal networks using artificial neural networks

Günay, C., & Prinz, A. A. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178991 · Cited by 4

Advances in Fully-Automatic and Interactive Phrase-Based Statistical Machine Translation

Valencia, E. D. (2011) · Cited by 4

Within-class and unsupervised clustering improve accuracy and extract local structure for supervised classification

Kulikowski, C. A., & Fradkin, D. (2006) · Cited by 4

A Critical Look at the Mechanisms Underlying Implicit Sequence Learning

Gureckis, T. M., & Love, B. C. (2005) · eScholarship (California Digital Library) · Cited by 4

Introduction to Soft Computing and Intelligent Control Systems

Sinha, N. K., & Gupta, M. M. (2000) · Elsevier eBooks · doi:10.1016/b978-012646490-0/50005-6 · Cited by 4

Origins of Stochastic Computing

Gaines, B. R. (2019) · Springer eBooks · doi:10.1007/978-3-030-03730-7_2 · Cited by 4

Models to Predict Survival after Liver Transplantation

Hoot, N. R. (2005) · Cited by 4

Evolutionary Computation and Parallel Processing Applied to the Design of Multilayer Perceptrons

Albuquerque, A. C. M. L., de Melo, J. D., & Neto, A. D. D. (2006) · Springer eBooks · doi:10.1007/3-540-32364-3_8 · Cited by 4

Perceptrons

Du, K.-L., & Swamy, M. N. S. (2013) · Springer eBooks · doi:10.1007/978-1-4471-5571-3_3 · Cited by 4

A New Perspective on Learning Linear Separators with Large L_qL_p Margins

Balcan, M.-F., & Berlind, C. G. (2014) · Cited by 4

Feature Selection of Microarray Data Using Genetic Algorithms and Artificial Neural Networks

Yacci, P., Gaborski, R. S., & Haake, A. (2009) · ASME Press eBooks · doi:10.1115/1.802953.paper50 · Cited by 4

Light Source Storage and Interpolation for Global Illumination: A Neural Solution

Delepoulle⋆, S., Renaud, C., & Preux, P. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-03452-7_5 · Cited by 4

Q-Tree: Automatic Construction of Hierarchical State Representation for Reinforcement Learning

Mao, T., Cheng, Z., & Ray, L. E. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33503-7_55 · Cited by 4

From Mirror Writing to Mirror Neurons

Arbib, M. A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15193-4_1 · Cited by 4

Model of Auctioneer Estimation of Swordtip Squid (Loligo edulis) Quality

Nakamura, M. M., Matsumoto, K., Morimoto, E., Ezoe, S., Maeda, T., & Hirano, T. (2011) · International Journal of Affective Engineering · doi:10.5057/kei.10.99 · Cited by 4

What has to be learned in motor learning?

Harold Bekkering, A., Heck, D. H., & Sultan, F. R. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0008153x · Cited by 4

A New Spatio-Temporal Data Mining Method and its Application to Reservoir System Operation

Mohan, A. (2014) · Lincoln (University of Nebraska) · Cited by 4

A novel hardware efficient Digital Neural Network architecture implemented in 130nm technology

Josep, C., & Gupta, A. (2010) · International Conference on Computer and Automation Engineering · doi:10.1109/iccae.2010.5452015 · Cited by 4

Self-organizing reservior computing with dynamically regulated cortical neural networks

Yin, J., & Meng, Y. (2012) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252772 · Cited by 4

Learning intialized by topologically correct representation

Hartono, P., & Trappenberg, T. P. (2009) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2009.5346566 · Cited by 4

Neural-net based receiver structures for single- and multi-amplitude signals in interference channels

Bouras, D. P., Mathiopoulos, P. T., & Makrakis, D. (2002) · Machine learning for signal processing ... · doi:10.1109/nnsp.1994.366012 · 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)