Topological feature maps with self-organized lateral connections: A population-coded, one-layer model of associative memory

DOI10.1007/BF00205975zbMath0821.92008OpenAlexW2021098237MaRDI QIDQ1346042

Claus Hillermeier, Paul Tavan, Bernhard Rabus, Niels Kunstmann

Publication date: 17 September 1995

Published in: Biological Cybernetics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00205975

zbMATH Keywords

Markov chainscluster analysisself-organizationpopulation codinghierarchical classificationfeature maplateral connectionsone-layer model for auto-associationrecurrent attractor dynamicsunsupervised statistical data analysis

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural biology (92C20) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (1)

An associative memory that can form hypotheses: A phase-coded neural network

Cites Work

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• Synergetics. An introduction. Nonequilibrium phase transitions and self-organization in physics, chemistry, and biology. 3rd rev. and enl. ed
• Self-organization of associative memory and pattern classification: Recurrent signal processing on topological feature maps
• How sensory maps could enhance resolution through ordered arrangements of broadly tuned receivers
• Self-organized formation of topologically correct feature maps
• An associative memory that can form hypotheses: A phase-coded neural network
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• Asymptotic quantization error of continuous signals and the quantization dimension
• Neural networks and physical systems with emergent collective computational abilities.

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