Self-organized formation of topologically correct feature maps
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Unsupervised learning algorithms such as Self-Organizing Maps and Adaptive Resonance Theory provide mechanisms for orthographic chunking from parallel letter input.
“Unsupervised learning algorithms, such as implemented in Self-Organizing Maps (e.g., Kohonen, 1982; Dufau et al., 2010) or Adaptive Resonance Theory (e.g., Grossberg, 1987; Glotin et al., 2010), provide specific mechanisms for performing orthographic chunking on the basis of a parallel letter input.”
From A Dual-Route Approach to Orthographic Processing (2011) · cites this paper as Kohonen, 1982
A system combining an unsupervised SOM network with LVQ was proposed to provide high performance in classifying the roughness of the ocean floor from raw sonar data.
“In this work, a system that combines an unsupervised self-organised mapping (SOM) network [99] with learning vector quantisation (LVQ) [100] was proposed to provide high performance in classifying the roughness of the ocean floor from raw sonar data.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [99]
In the early 1980s, three new classes of NNs emerged: Convolutional Neural Network, Self-Organizing Map, and Hopfield network.
“In the early 1980s, three new classes of NNs emerged, namely Convolutional Neural Network (CNN) [157], Self-Organizing Map (SOM) [249], and Hopfield network [195].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [249]
SOM was designed for dimensionality reduction.
“Similarly to CNN, SOM was also designed for a specific application domain; dimensionality reduction [249].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [249]
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- Authors
- Kohonen, T.
- Corresponding author
- Kohonen, Teuvo
- Venue
- Biological Cybernetics
- Published
- 1982
- Volume
- 43
- Issue
- 1
- Pages
- 59-69
- ISSN
- 0340-1200 (ISSN-L)
- 1432-0770
- OpenAlex
- W65738273
- Cited by
- 9,529
- References
- 23
- Citations by year
- 2026: 260 · 2025: 378 · 2024: 322 · 2023: 385 · 2022: 407 · 2021: 450 · 2020: 449 · 2019: 393 · 2018: 375 · 2017: 321 · 2016: 352 · 2015: 358 · 2014: 346 · 2013: 350 · 2012: 322
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Biological Cybernetics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Nonlinear Dynamics and Pattern Formation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- self-organization, topological maps, feature maps, adaptive networks, threshold logic units, signal representation, artificial neural networks, topological order
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