The DOI matches its Crossref record, with a small difference in title or year; 12 of the 12 references it cites were found in a registry or an index.
Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“TOpologyPreserving knowledge InCrementer [71] relies on the NG network to preserve the feature space topology using a Hebbian learning [42].”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [42]
Citations
Cites
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
Kohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414
Sequence regularity and geometry of orientation columns in the monkey striate cortex
Hubel, D. H., & Wiesel, T. N. (1974) · The Journal of Comparative Neurology · doi:10.1002/cne.901580304 · Cited by 1,043
How patterned neural connections can be set up by self-organization
Willshaw, D. J., & von der Malsburg, C. (1976) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1976.0087 · Cited by 822
Multiple Representations of the Body Within the Primary Somatosensory Cortex of Primates
Kaas, J. H., Nelson, R. J., Sur, M., Lin, C.-S., & Merzenich, M. M. (1979) · Science · doi:10.1126/science.107591 · Cited by 710
A dimension reduction framework for understanding cortical maps
Durbin, R. M., & Mitchison, G. (1990) · Nature · doi:10.1038/343644a0 · Cited by 440
Computational Maps In The Brain
Knudsen, E. I., du Lac, S., & Esterly, S. D. (1987) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.10.030187.000353 · Cited by 390
Variants of self-organizing maps
Kangas, J. A., Kohonen, T., & Laaksonen, J. T. (1990) · IEEE Transactions on Neural Networks · doi:10.1109/72.80208 · Cited by 329
Topology-conserving maps for learning visuo-motor-coordination
Ritter, H. J., Martinetz, T., & Schulten, K. J. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90001-4 · Cited by 263
Self-citation
Research works printed but not linked: 8
- Ritter H, Martinetz T, Schulten K (1992), Neural Computation and Self-Organizing Maps. Addison-Wesley, Reading, Mass., 1992.
- Kohonen T, Mäkisara K, Saramäki T (1984), Proc. 7th Int. Conf. on Pat. Recog., Montreal, pp 182–185.
- Brandt WD, Behme H, Strube HW (1991), Fortschritte zur Akustik-DAGA 91, Bad Honnef, DPG-GMBH, Germany, pp 1057–1060.
- Nasrabadi NM, Feng Y (1988), IEEE Int. Conf. on Neural Networks, San Diego, 1988, pp 1101–1108.
- Fritzke B (1991), Artificial Neural Networks, vol I, Kohonen T. et al. (eds.), North Holland, Amsterdam, pp. 403–408.
- Martinetz T, Schulten K (1991), Artificial Neural Networks, vol I, Kohonen T. et al. (eds.), North Holland, Amsterdam, pp. 397–402.
- Hebb D (1949), Organization of Behavior. Wiley, New York.
- Martinetz T, Berkovich S, Schulten K (1992), IEEE Transactions on Neural Networks (in press).
Cited by
Sorted by citations
Items 201–256 of 256
A Self-organized Incremental Network for Online Supervised Learning and Topology Learning
Kamiya, Y., Shen, F., Ishii, T., & Hasegawa, O. (2006) · SCIS & ISIS SCIS & ISIS 2006 · doi:10.14864/softscis.2006.0.1448.0 · Cited by 1
WITHDRAWN: Time Series Learning Network Using Fast Dynamic Time Warping
Xu, H., Shen, F., Xing, Y., & Zhao, J. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.03.057 · Cited by 1
An Incremental One Class Learning Framework for Large Scale Data
Deng, Q., Yang, Y., Shen, F., Luo, C., & Zhao, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46672-9_46 · Cited by 1
LEARNING TO TEACH TO NEURAL NETWORKS HOW TO LEARN WELL WITH SOH, A SELF-OBSERVING HEURISTIC
Mariage, J.-J. (2014) · International Journal of Computing · doi:10.47839/ijc.3.1.254 · Cited by 1
Clustering I: Basic Clustering Models and Algorithms
Du, K.-L., & Swamy, M. N. S. (2019) · Springer eBooks · doi:10.1007/978-1-4471-7452-3_9 · Cited by 1
CBR System for Leukemia Patients Diagnosis.
Paz, J. F. D., Rodrı́guez, S., Bajo, J., & Corchado, J. M. (2008) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 1
Using a growing probabilistic neural network to reinforce a semi supervised support vector machine
Hebboul, A., Hachouf, F., & Boulemnadjel, A. (2015) · International Symposium on Programming and Systems (ISPS) · doi:10.1109/isps.2015.7244990 · Cited by 1
Approximate Spectral Clustering Using Topology Preserving Methods and Local Scaling
Alshammari, M. A., & Takatsuka, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04212-7_10 · Cited by 1
Rule Insertion Technique for Dynamic Cell Structure Neural Network
(2020) · International Journal of Neural Networks and Advanced Applications · doi:10.46300/91016.2020.7.3 · Cited by 1
Cirrincione, G., Randazzo, V., Barbiero, P., Ciravegna, G., & Pasero, E. G. A. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-3592-5_5 · Cited by 1
Non-Time Reference Gait Planning and Stability Control for Bipedal Walking
Ke, X., Qian, J., & Gong, Z. (2007) · doi:10.5772/5076 · Cited by 1
Fritzke, B. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48238-5_17 · Cited by 1
Evaluating approximations generated by the GNG3D method for mesh simplification
Navarro, P. R., Tortosa, L., Vicent, J. F., & Zamora, A. (2008) · RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante) · Cited by 1
The growing neural map: An on-line competitive clustering algorithm
Mattone, R. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2002.1014329 · Cited by 1
A self-structurizing neural network for online incremental learning
Hasegawa, O., & Shen, F. (2004) · Tokyo Tech Research Repository (Tokyo Institute of Technology) · doi:10.11499/sicep.2004.0_84_2 · Cited by 1
Uma Rede Neural Auto-Organizável Construtiva para Aprendizado Perpétuo de Padrões Espaço-Temporais
Bastos, E. N. F. (2007) · Americanae (AECID Library) · Cited by 1
SOME REMARKS ON THE SELF-ORGANIZING FEATURE MAPS
Tran, D. M., & Le, H. K. (2005) · OUKA (Osaka University Knowledge Archive) (Osaka University) · Cited by 1
Large-scale deep class-incremental learning
Belouadah, E. (2021) · theses.fr (ABES) · doi:10.70675/427ce7e6z19e6z48e8za9ddz81eebeef3f01 · Cited by 1
A Hebbian model for Space Representation
Frisone, F., & Morasso, P. (1998) · Perspectives in neural computing · doi:10.1007/978-1-4471-1520-5_7
Automatically Building 2D Statistical Shapes Using the Topology Preservation Model GNG
García‐Rodríguez, J., Angelopoulou, A., Ψαρρού, Α., & Revett, K. (2006) · Lecture notes in computer science · doi:10.1007/11612032_53
Using CBR Systems for Leukemia Classification
Corchado, J. M., & Paz, J. F. D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87656-4_85
Adaptive Representation of Objects Topology Deformations with Growing Neural Gas
García‐Rodríguez, J., Flórez‐Revuelta, F., & García‐Chamizo, J. M. (2007) · Springer eBooks · doi:10.1007/978-3-540-73007-1_30
Spicules for Unsupervised Learning
Gómez‐Ruiz, J. A., Pérez, J. M., & García-Bernal, M. A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02478-8_24
Yamakawa, T., Horio, K., & Tanaka, T. (2006) · Lecture notes in computer science · doi:10.1007/11893028_108
Martí, L., Policriti, A., & García, L. (2004) · Studies in fuzziness and soft computing · doi:10.1007/978-3-540-39615-4_3
Spicules-based competitive neural network.
Gómez‐Ruiz, J. A., Pérez, J. M., García-Bernal, M. A., & López‐Rubio, E. (2007)
A Nearest-Neighbour Method with Self-organizing Incremental Neural Network
Furao, S., & Hasegawa, O. (2007) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/ijcnn.2007.4371119
GLLE: An improved nonlinear manifold learning method with global topological preservation
Yin, J., Chen, G., & Li, D. (2008) · IEEE International Conference on Granular Computing (GrC) · doi:10.1109/grc.2008.4664706
Indicator-based MONEDA: A comparative study of scalability with respect to decision space dimensions
Martí, L., Garcı́a, J., Berlanga, A., & Molina, J. M. (2011) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2011.5949721
COMPUTATIONAL COST OF GNG3D ALGORITHM FOR MESH SIMPLIFICATION
Álvarez, R., Noguera, J. L. V., Tortosa, L., & Zamora, A. (2007) · RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante)
Adaptive RBFN model for 2D spatial interpolation
Zhang, Q. P., Ma, Y., & Lai, L. L. (2009) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2009.5212717
A topology based multi-classifier system
Prudent, Y., & Ennaji, A. (2005) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2005.37
Sheikhan, M., & Khalili, A. H. (2009)
The Cooperative Behavior of a Human Work Group: A Distributed Learning Approach.
Santra, T., Venkatesh, K. S., & Mukerjee, A. (2006) · ISAIM
Autonomous learning of object behavior concepts and models through robotic interaction
Ovalle, S. R. (2016) · Publications of the UdS (Saarland University) · doi:10.22028/d291-26677
Contributions to 3D Data Registration and Representation
Morell, V. (2014) · RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante)
A Cortically Inspired Learning Model
Hashmi, A. G., & Lipasti, M. H. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-27534-0_25
The Application of the SPAWNN Toolkit to the Socioeconomic Analysis of Chicago, Illinois
Hagenauer, J., & Helbich, M. (2017) · Geotechnologies and the environment · doi:10.1007/978-3-319-52522-8_5
Comprehensive Study & Overview of Neural Networks for Classification of Diseases
Patil, S., Wararkar, P., Joshi, D., & Awate, A. (2018) · International Journal of Innovative Research in Electronics and Communications
Gradient-Based Competitive Learning: Theory
Cirrincione, G., Randazzo, V., Barbiero, P., Ciravegna, G., & Pasero, E. G. A. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10225-5
Kyan, M. J., Muneesawang, P., Jarrah, K., & Guan, L. (2014) · doi:10.1002/9781118875568.refs
Comparing SONN Types for Efficient Robot Motion Planning in the Configuration Space
Steffen, L., Weyer, T., Glueck, K., Ulbrich, S., Roennau, A., & Dillmann, R. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-22216-0_13
Doerschuk, P. I., Grossberg, S., Koutsougeras, C., Fritzke, B., Carpenter, G. A., & Verleysen, M. (1996) · Taylor & Francis eBooks · doi:10.1201/9781420050646.ptc2
The Self-Organising Hierarchical Variance Map
Kyan, M. J., & Guan, L. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.1716617
Fault identification using competitive learning
Machado, M. V. B., & Gattass, M. (2007) · doi:10.1190/sbgf2007-102
Perschewski, J.-O., & Stober, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15919-0_25
Continual Learning for Classification Tasks
Xue-jun, H. (2023) · doi:10.22215/etd/2023-15555
DSG: Dual-Semantic Guidance from LLM to Token Distillation for Few-Shot Incremental Learning
Tao, Z., Shen, T., Liu, J., Wang, K., & Wang, K. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11465116
(49484), X. Z., Yuan, F., Sun, Q., Zhang, H., Qiu, X., & Yang, X. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11486646
DAT$$^3$$: Dual-Teacher Topology Adversarial Training for Defending Against Adversarial Attacks
Huaxing, F., Li, Y., Yin, H., Hu, C., Ahmed, S. A. A., & Awais, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31452-9_37
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks
Belouadah, E., Popescu, A., & Kanellos, I. (2020) · arXiv · doi:10.48550/arxiv.2011.01844 · Cited by 235
Metadata
- Authors
- Martinetz, T.
- Affiliations
- Siemens (Germany) (DE)
- Corresponding author
- Martinetz, Thomas
- ORCID
- Martinetz, Thomas
- Venue
- ICANN ’93
- Publisher
- Springer London
- Published
- 1993
- Pages
- 427-434
- OpenAlex
- W2112796159
- Cited by
- 256
- References
- 11
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 2 · 2023: 7 · 2022: 7 · 2021: 8 · 2020: 6 · 2019: 6 · 2018: 9 · 2017: 8 · 2016: 14 · 2015: 10 · 2014: 11 · 2013: 6 · 2012: 10
- FWCI
- 3.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- Hebbian learning, competitive learning, feature manifold, Delaunay triangulation, lateral connections, neighborhood relations, neural network architecture, manifold learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review