Self-Organized Formation of Topologically Correct Feature Maps.

Kohonen, T. (1982). Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics, 43(1), 59-69.

Type

Journal article

Intellectual base

Primary research

Year

1982

DOI

10.1007/bf00337288

Link

https://doi.org/10.1007/BF00337288

Cited from

neuroscience

Cited by

3 citations across 3 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Hebbian Learning
  • Hebb's rule, refined into normalizing and threshold variants, underlies accounts of associative memory and receptive-field formation. Machine learning. Hebbian and competitive rules underlie self-organizing maps, Hopfield networks, sparse coding, and early unsupervised pretraining; normalizing Hebbian rules connect to principal-component extraction.

    This sourceIntroduces self-organizing maps — competitive Hebbian learning producing organized structure from input statistics without supervision.

• Topographic Map
  • In machine learning, Kohonen self-organising maps explicitly optimise neighbourhood preservation from a high-dimensional source onto a 2D substrate, and t-SNE and UMAP embed high-dimensional data with neighbourhood preservation as the objective.

    This sourceIntroduces the self-organizing map, optimizing neighbourhood preservation from a high-dimensional source onto a low-dimensional substrate.

Domain-specific

• Self-organizing map
  • A self-organizing map is an unsupervised competitive-learning algorithm that adapts lattice-indexed prototypes so nearby lattice units represent nearby regions of the input distribution.

Verification

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