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

DOI10.1007/BF00205976zbMath0825.92009OpenAlexW2014208770WikidataQ113909785 ScholiaQ113909785MaRDI QIDQ1346043

Niels Kunstmann, Paul Tavan, Claus Hillermeier, Bernhard Rabus

Publication date: 28 November 1995

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

Full work available at URL: https://doi.org/10.1007/bf00205976

Mathematics Subject Classification ID

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

Related Items (2)

Topological feature maps with self-organized lateral connections: A population-coded, one-layer model of associative memory ⋮ GENOMIC ORGANIZATION AND HOPFIELD'S MODEL OF ASSOCIATIVE MEMORY

Cites Work

• Unnamed Item
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• Self-organization and associative memory
• Self-organization of associative memory and pattern classification: Recurrent signal processing on topological feature maps
• A model for neuronal oscillations in the visual cortex. I: Mean-field theory and derivation of the phase equations
• A model for neuronal oscillations in the visual cortex. II: Phase description of the feature dependent synchronization
• Topological feature maps with self-organized lateral connections: A population-coded, one-layer model of associative memory
• A biologically motivated and analytically soluble model of collective oscillations in the cortex. I. Theory of weak locking
• Modeling Brain Function
• Neural networks and physical systems with emergent collective computational abilities.

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