A massively parallel architecture for a self-organizing neural pattern recognition machine

Neural networks and physical systems with emergent collective computational abilities.

Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270

The Organization of Behavior

Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825

The organization of behavior.

indicated, N. A. (1950) · Journal of Consulting Psychology · doi:10.1037/h0049612 · Cited by 6,984

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759

Categories and Concepts

Smith, E. E., & Medin, D. L. (1981) · Harvard University Press eBooks · doi:10.4159/harvard.9780674866270 · Cited by 2,653

On the genesis of abstract ideas.

Posner, M. I., & Keele, S. W. (1968) · Journal of Experimental Psychology · doi:10.1037/h0025953 · Cited by 1,983

Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors

Grossberg, S. (1976) · Biological Cybernetics · doi:10.1007/bf00344744 · Cited by 1,910

How Does a Brain Build a Cognitive Code?

Grossberg, S. (1982) · Boston studies in the philosophy of science · doi:10.1007/978-94-009-7758-7_1 · Cited by 1,204

Distributed memory and the representation of general and specific information.

McClelland, J. L., & Rumelhart, D. (1985) · Journal of experimental psychology. General · doi:10.1037/0096-3445.114.2.159 · Cited by 1,160

How does a brain build a cognitive code?

Grossberg, S. (1980) · Psychological Review · doi:10.1037/0033-295x.87.1.1 · Cited by 1,160

ART-EMAP: A Neural Network Architecture for Learning and Prediction by Evidence Accumulation

Carpenter, G. A., & Ross, W. D. (1993) · OpenBU (Boston University) · Cited by 25

A Novel Complex-Valued Counterpropagation Network

Kalra, P. K., Mishra, D., & Tyagi, K. (2007) · IEEE Symposium on Computational Intelligence and Data Mining (CIDM) · doi:10.1109/cidm.2007.368856 · Cited by 24

Age Classification using Fuzzy Lattice Neural Network

Kalamani, D., & Balasubramanie, P. (2006) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2006.8 · Cited by 24

Clustering of Time-Series Data

Özkoç, E. E. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.84490 · Cited by 24

A Hybrid Architecture Combining Reactive Plan Execution and Reactive Learning

Karim, S., Sonenberg, L., & Tan, A. (2006) · Lecture notes in computer science · doi:10.1007/978-3-540-36668-3_23 · Cited by 24

Combining a new data classification technique and regression analysis to predict the Cost-To-Serve new customers

Kone, E. R. S., & Karwan, M. H. (2011) · Computers & Industrial Engineering · doi:10.1016/j.cie.2011.03.009 · Cited by 27

Speaker normalization using cortical strip maps: A neural model for steady-state vowel categorization

Ames, H., & Grossberg, S. (2008) · The Journal of the Acoustical Society of America · doi:10.1121/1.2997478 · Cited by 23

Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment

Rădoi, E., Quinquis, A., & Totir, F. (2006) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/asp/2006/35043 · Cited by 24

Explanations of unsupervised learning clustering applied to data security analysis

Corral, G., Armengol, E., Fornells, A., & Golobardes, E. (2009) · Neurocomputing · doi:10.1016/j.neucom.2008.09.021 · Cited by 24

Towards personalised web intelligence

Tan, A., Ong, H., Pan, H., Ng, J., & Li, Q. (2004) · Knowledge and Information Systems · doi:10.1007/s10115-003-0130-9 · Cited by 24

Angels, Devils, and Censors in the Brain

Levine, D. S. (2004) · Complexus · doi:10.1159/000089747 · Cited by 24

Applications of Diffusion Maps in Gene Expression Data-Based Cancer Diagnosis Analysis

Xu, R., Damelin, S. B., & Wunsch, D. C. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4353367 · Cited by 24

Neural networks for linear inverse problems with incomplete data especially in applications to signal and image reconstruction

Cichocki, A. S., Unbehauen, R., Lendl, M., & Weinzierl, K. (1995) · Neurocomputing · doi:10.1016/0925-2312(94)e0063-w · Cited by 24

A connectionist model for category perception: theory and implementation

Basak, J. K., Murthy, C. A., Chaudhury, S., & Majumder, D. D. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.207613 · Cited by 24

Optimization of the computational load of a hypercube supercomputer onboard a mobile robot

Barhen, J., Toomarian, N. B., & Protopopescu, V. (1987) · Applied Optics · doi:10.1364/ao.26.005007 · Cited by 24

Relational Experience Replay: Continual Learning by Adaptively Tuning Task-Wise Relationship

Wang, Q., Wang, R., Li, Y., Wei, D., Wang, H., Ma, K., Zheng, Y., & Meng, D. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3397048 · Cited by 29

An improved fuzzy ARTMAP and Q-learning agent model for pattern classification

Pourpanah, F., Wang, R., Lim, C. P., Wang, X., Seera, M., & Tan, C. J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.06.002 · Cited by 23

An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective

Hoppe, D. B., Hendriks, P., Ramscar, M., & van Rij, J. (2022) · Behavior Research Methods · doi:10.3758/s13428-021-01711-5 · Cited by 25

DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning

Gao, X., He, Y., Dong, S., Cheng, J., Wei, X., & Gong, Y. (2023) · 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/cvpr52729.2023.02321 · Cited by 26

From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control

Grossberg, S. (2014) · Brain Research · doi:10.1016/j.brainres.2014.11.018 · Cited by 23

Automatic adjustment of the relative importance of different input variables for optimization of counter-propagation artificial neural networks

Kuzmanovski, I., Novič, M., & Trpkovska, M. (2009) · Analytica Chimica Acta · doi:10.1016/j.aca.2009.01.041 · Cited by 23

A neural model of normal and abnormal learning and memory consolidation: adaptively timed conditioning, hippocampus, amnesia, neurotrophins, and consciousness

Franklin, D. J., & Grossberg, S. (2016) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-016-0463-y · Cited by 23

Uncertainty-based modulation for lifelong learning

Brna, A., Brown, R. C., Connolly, P. M., Simons, S. B., Shimizu, R. E., & Aguilar, M. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.011 · Cited by 23

DF classification algorithm for constructing a small sample size of data-oriented DF regression model

Xia, H., Tang, J., Qiao, J., Zhang, J., & Yu, W. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06809-7 · Cited by 24

Winner-Takes-All Associative Memory: A Hamming Distance Vector Quantizer

Pouliquen, P. O., Andreou, A. G., & Strohbehn, K. (1997) · Analog Integrated Circuits and Signal Processing · doi:10.1023/a:1008204616413 · Cited by 23

On the role of interpretive analogy in learning

Indurkhya, B. (1991) · New Generation Computing · doi:10.1007/bf03037095 · Cited by 23

Binary decision clustering for neural-network-based optical character recognition

Wilson, C. L., Grother, P. J., & Barnes, C. S. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00105-0 · Cited by 24

Neural Networks, Knowledge and Cognition: A Mathematical Semantic Model Based upon Category Theory

Healy, M. J. R., & Caudell, T. P. (2004) · UNM’s Digital Repository (University of New Mexico) · Cited by 23

DYNAMICS OF TIME DISCRIMINATION: II. THE EFFECTS OF MULTIPLE IMPULSES

Higa, J. J. (1996) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.1996.66-117 · Cited by 23

Learning and Recognizing 3D Objects from Multiple Views in a Neural System

Seibert, M. C., & Waxman, A. M. (1992) · Elsevier eBooks · doi:10.1016/b978-0-12-741251-1.50028-x · Cited by 23

Recognition of Isolated Handwritten Characters of Gurumukhi Script using Neocognitron

Sharma, D. V., & Jain, U. (2010) · International Journal of Computer Applications · doi:10.5120/1503-2021 · Cited by 22

Semantic Memory Modeling and Memory Interaction in Learning Agents

Wang, W., Tan, A.-H., & Teow, L.-N. (2017) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2016.2531683 · Cited by 22

Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory

Kazerounian, S., & Grossberg, S. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01053 · Cited by 22

Adaptive computer-generated forces for simulator-based training

Teng, T.-H., Tan, A., & Teow, L.-N. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.07.004 · Cited by 22

Development of a flexible and programmable parts feeding system

Tay, M. L., Chua, P. S. K., Sim, S. K., & Gao, Y. (2005) · International Journal of Production Economics · doi:10.1016/j.ijpe.2004.05.019 · Cited by 22

Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

Palma, J., Grossberg, S., & Massimiliano Versace, A. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00042 · Cited by 21

Fractional cell formation in group technology using modified ART1 neural networks

Venkumar, P., & Haq, A. N. (2005) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-004-2421-z · Cited by 22

Quality of Services Provisioning in Wireless Sensor Networks using Artificial Neural Network: A Survey

Mittal, M., & Kumar, K. (2015) · International Journal of Computer Applications · doi:10.5120/20553-2931 · Cited by 22

Issues in evolving GP based classifiers for a pattern recognition task

Teredesai, A. M., & Govindaraju, V. (2004) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2004.1330899 · Cited by 22

Detection and classification of high impedance faults in power distribution networks using ART neural networks

Nikoofekr, I., Sarlak, M., & Shahrtash, S. M. (2013) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2013.6599760 · Cited by 22

Bionic eyeglass: An audio guide for visually impaired

Karacs, K. I., Lázár, A., Wagner, R., Bálya, D., Roska, T., & Szuhaj, M. (2006) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2006.4600340 · Cited by 22

Clustering with competing self-organizing maps

Cheng, Y. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1992.227222 · Cited by 22

Border Vector Detection and Adaptation for Classification of Multispectral and Hyperspectral Remote Sensing Images

Kasapoglu, N. G., & Ersoy, O. K. (2007) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.900699 · Cited by 22

Self-Organizing Neural Architectures and Cooperative Learning in a Multiagent Environment

Xiao, D., & Tan, A. (2007) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2007.907040 · Cited by 22

Incremental Learning With Balanced Update on Receptive Fields for Multi-Sensor Data Fusion

Su, J., Wang, J., & Xi, Y. (2004) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2002.806485 · Cited by 21

In Search of Perceptual Priming in a Semantic Classification Task.

Bowers, J. S., & Turner, E. L. (2003) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.29.6.1248 · Cited by 22

Learning and Decision-Making in the Framework of Fuzzy Lattices

Kaburlasos, V. G., & Petridis, V. (2002) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1803-1_3 · Cited by 22

Adaptive hamming net: A fast-learning ART 1 model without searching

Hung, C.-A., & Lin, S.-F. (1995) · Neural Networks · doi:10.1016/0893-6080(94)00106-v · Cited by 22

Advances in instance-based learning algorithms

Wilson, D. R. (1997) · Cited by 22

Implementing regularly structured neural networks on the DREAM machine

Shams, S., & Gaudiot, J. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.363476 · Cited by 22

A topological and temporal correlator network for spatiotemporal pattern learning, recognition, and recall

Srinivasa, N., & Ahuja, N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.750565 · Cited by 22

Image labelling: a neural network approach

Jamison, T. A., & Schalkoff, R. J. (1988) · Image and Vision Computing · doi:10.1016/0262-8856(88)90010-8 · Cited by 22

A new art-based neural architecture for pattern classification and image enhancement without prior knowledge

Shih, F. Y., Moh, J., & Chang, F.-C. (1992) · Pattern Recognition · doi:10.1016/0031-3203(92)90051-j · Cited by 22

An adaptive computational model for personalized persuasion

Kang, Y., Tan, A., & Miao, C. (2015) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 21

Toward Autonomous Adaptive Intelligence: Building Upon Neural Models of How Brains Make Minds

Grossberg, S. (2020) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.3041476 · Cited by 21

Vision-Based Topological Mapping and Navigation With Self-Organizing Neural Networks

Hu, Y., Subagdja, B., Tan, A., & Yin, Q. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084212 · Cited by 21

Robot Navigation Strategy in Complex Environment Based on Episode Cognition

Yuan, J., Guo, W. P., Hou, Z., Zha, F., Li, M., Sun, L., & Wang, P. (2022) · Journal of Bionic Engineering · doi:10.1007/s42235-022-00265-2 · Cited by 21

Projective ART with buffers for the high dimensional space clustering and an application to discover stock associations

Liu, L., Huang, L., Lai, M., & Ma, C. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.01.020 · Cited by 21

NMR and conformational studies of linear and cyclic oligo-(1→6)-β-d-glucosamines

Grachev, A. A., Gerbst, A. G., Gening, M. L., Titov, D. V., Yudina, O. N., Tsvetkov, Y. E., Shashkov, A. S., Pier, G. B., & Nifantiev, N. E. (2011) · Carbohydrate Research · doi:10.1016/j.carres.2011.08.031 · Cited by 21

The modified fuzzy art and a two-stage clustering approach to cell design

Özdemir, R. G., Gençyılmaz, G., & Aktin, T. (2007) · Information Sciences · doi:10.1016/j.ins.2007.06.027 · Cited by 21

Application of Adaptive Resonance Theory neural networks to monitor solar hot water systems and detect existing or developing faults

He, H., Caudell, T. P., Menicucci, D., & Mammoli, A. A. (2012) · Solar Energy · doi:10.1016/j.solener.2012.05.015 · Cited by 21

Combining semantic and term frequency similarities for text clustering

Soares, V. H. A., Campello, R. J. G. B., Nourashrafeddin, S., Milios, E., & Naldi, M. C. (2019) · Knowledge and Information Systems · doi:10.1007/s10115-018-1278-7 · Cited by 22

An Efficient Two-Level SOMART Document Clustering Through Dimensionality Reduction

Hussin, M. F., Kamel, M. S., & Nagi, M. H. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30499-9_23 · Cited by 21

The application of neural networks in self-tuning constant force control

Huang, S., & Chiou, K. (1996) · International Journal of Machine Tools and Manufacture · doi:10.1016/0890-6955(95)92626-a · Cited by 21

Self-Organizing Cognitive Agents and Reinforcement Learning in Multi-Agent Environment

Tan, A., & Xiao, D. (2006) · IEEE/WIC/ACM International Conference on Intelligent Agent Technology · doi:10.1109/iat.2005.125 · Cited by 23

Neural Computation in Medicine: Perspectives and Prospects

Dybowski, R. (2000) · Perspectives in neural computing · doi:10.1007/978-1-4471-0513-8_4 · Cited by 21

Exact ART: A Complete Implementation of an ART Network

Raijmakers, M. E. J., & Molenaar, P. C. M. (1997) · Neural Networks · doi:10.1016/s0893-6080(96)00111-6 · Cited by 21

Temporal dynamics of 2D and 3D shape representation in macaque visual area V4

Hegdé, J., & Essen, D. C. V. (2006) · Visual Neuroscience · doi:10.1017/s0952523806230074 · Cited by 21

DYNAMIC TRAFFIC PATTERN CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS

Hua, J., & Faghri, A. (1993) · Transportation Research Record Journal of the Transportation Research Board · Cited by 21

An Efficient Mapping of Fuzzy ART onto a Neural Architecture

Blume, M., & Esener, S. C. (1997) · Neural Networks · doi:10.1016/s0893-6080(96)00085-8 · Cited by 21

On possible perceptual worlds and how they shape their environments

Mausfeld, R. J., Niederée, R. M., & Heyer, K. D. (1992) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00067480 · Cited by 21

Learning and generalization of noisy mappings using a modified PROBART neural network

Srinivasa, N. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.640717 · Cited by 21

Distributed Data Processing in Wireless Sensor Networks Based on Artificial Neural-Networks Algorithms

Kulakov, A., & Davcev, D. P. (2005) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2005.52 · Cited by 21

Wavelet-based vector quantization for high-fidelity compression and fast transmission of medical images

Mitra, S. D., Yang, S., & Kustov, V. (1998) · Journal of Imaging Informatics in Medicine · doi:10.1007/bf03168174 · Cited by 21

Recognition of Sign Language Using Neural Networks

Vamplew, P. W. (1996) · UTAS Research Repository · doi:10.25959/23212583 · Cited by 21

GIS-based seismic vulnerability mapping: a comparison of artificial neural networks hybrid models

Yariyan, P., Abbaspour, R. A., Chehreghan, A., Karami, M., & Cerdà, A. (2021) · Geocarto International · doi:10.1080/10106049.2021.1892208 · Cited by 20

Elements of a cybernetic epistemology

Nechansky, H. (2012) · Kybernetes · doi:10.1108/03684921211229514 · Cited by 20

Inter-trial inhibition of attention to features is modulated by task relevance

Levinthal, B. R., & Lleras, A. (2008) · Journal of Vision · doi:10.1167/8.15.12 · Cited by 19

ARTIE and MUSCLE models: building ensemble classifiers from fuzzy ART and SOM networks

Mattos, C. L. C., & Barreto, G. A. (2011) · Neural Computing and Applications · doi:10.1007/s00521-011-0747-7 · Cited by 20

Methodological and Theoretical Issues in Neural Network Models of Frontal Cognitive Functions

Levine, D. S., Parks, R. W., & Prueitt, P. S. (1993) · International Journal of Neuroscience · doi:10.3109/00207459309024110 · Cited by 20

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

Rajaei, K., Khaligh‐Razavi, S., Ghodrati, M., Ebrahimpour, R., & Abadi, M. E. S. A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038478 · Cited by 20

How Humans Consciously See Paintings and Paintings Illuminate How Humans See

Grossberg, S., & Zajac, L. (2017) · Art & Perception · doi:10.1163/22134913-00002059 · Cited by 20

SASEGASA: An Evolutionary Algorithm for Retarding Premature Convergence by Self-Adaptive Selection Pressure Steering

Affenzeller, M., & Wagner, Ş. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44868-3_56 · Cited by 20

Gabor space and the development of preattentive similarity

Santini, S., & Jain, R. (1996) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1996.545988 · Cited by 20

Neural Facades: Visual Representations of Static and Moving Form‐And‐Color‐And‐Depth

Grossberg, S. (1990) · Mind & Language · doi:10.1111/j.1468-0017.1990.tb00171.x · Cited by 20

A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations

Tümer, B., Belfore, L. A., & ROPELLA, K. M. (2003) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2002.804365 · Cited by 20

Resonant spatiotemporal learning in large random recurrent networks

Daucé, E., Quoy, M., & Doyon, B. (2002) · Biological Cybernetics · doi:10.1007/s00422-002-0315-4 · Cited by 20

PsyCOP-a psychologically motivated connectionist system for object perception

Basak, J. K., & Pal, S. K. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.471373 · Cited by 20

Neural networks for complex scene recognition: simulation of a visual system with several cortical areas

Gaussier, P., & Cocquerez, J. P. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1992.227165 · Cited by 20

Self-organizing neural network character recognition on a massively parallel computer

Wilson, C. L., Wilkinson, R. A., & Garris, M. D. (1990) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1990.137734 · Cited by 20

Parallel, self-organizing, hierarchical neural networks with competitive learning and safe rejection schemes

Cho, S. G., Ersoy, O. K., & Lehto, M. R. (1993) · IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing · doi:10.1109/82.257333 · Cited by 20

A Review of Computer-Based Human Behavior Representations and Their Relation to Military Simulations

Morrison, J. E. (2003) · PsycEXTRA Dataset · doi:10.1037/e427382005-001 · Cited by 21

A unified model for abduction-based reasoning

Ayeb, B., Wang, S., & Ge, J. (1998) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/3468.686703 · Cited by 20

Formalizing the Generalization-Forgetting Trade-off in Continual LearningPreprint

Raghavan, K., & Balaprakash, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.14035 · Cited by 20

Biologically-Based Interactive Neural Network Models for Visual Attention and Object Recognition

Saifullah, M. (2012) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 20

Models and Measures for Standardless Measurements of the Composite Materials Characteristics

Babak, V. P., Babak, S. V., Eremenko, V. S., Kuts, Y. V., Myslovych, M. V., Scherbak, L. M., & Zaporozhets, A. O. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-70783-5_6 · Cited by 19

Generalized hamming networks and applications

Koutroumbas, K., & Kalouptsidis, N. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.02.003 · Cited by 19

The interaction between competition, learning, and habituation dynamics in speech perception

Lancia, L., & Winter, B. (2013) · Laboratory Phonology Journal of the Association for Laboratory Phonology · doi:10.1515/lp-2013-0009 · Cited by 19

A self-organizing approach to episodic memory modeling

Wang, W., Subagdja, B., Tan, A., & Starzyk, J. A. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596734 · Cited by 19

Neural Dynamics of Autistic Repetitive Behaviors and Fragile X Syndrome: Basal Ganglia Movement Gating and mGluR-Modulated Adaptively Timed Learning

Grossberg, S., & Kishnan, D. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00269 · Cited by 19

Absolutely stable learning of recognition codes by a self-organizing neural network

Carpenter, G. A., & Grossberg, S. (1986) · AIP conference proceedings · doi:10.1063/1.36223 · Cited by 12

Adaptive Resonance Theory: Stable Self-Organization of Neural Recognition Codes In Response to Arbitrary Lists of Input Patterns

Carpenter, G. A., & Grossberg, S. (1986) · eScholarship (California Digital Library) · Cited by 20

Art 3: Self-Organization of Distributed Pattern Recognition Codes in Neural Network Hierarchies

Carpenter, G. A., & Grossberg, S. (1990) · Springer eBooks · doi:10.1007/978-94-009-0643-3_93 · Cited by 2

ART 2: self-organization of stable category recognition codes for analog input patterns

Carpenter, G. A., & Grossberg, S. (1987) · Applied Optics · doi:10.1364/ao.26.004919 · Cited by 1,443

Computer and Mathematical Modelling of Massively Parallel Architectures for Self-Organizing Neural Pattern Recognition Machines

Grossberg, S. (1992) · Defense Technical Information Center (DTIC)