Graph Degree Sequence Solely Determines the Expected Hopfield Network Pattern Stability

DOI10.1162/NECO_a_00685zbMath1414.92008OpenAlexW2130596241WikidataQ51014817 ScholiaQ51014817MaRDI QIDQ5380189

Daniel Berend, Ariel Hanemann, Shlomi Dolev

Publication date: 4 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00685

zbMATH Keywords

Hopfield neural networkpattern stabilitygraph degree sequence

Mathematics Subject Classification ID

Applications of graph theory (05C90) Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (1)

Edge crossings in random linear arrangements

Cites Work

• The Hopfield model on a sparse Erdös-Renyi graph
• Lower bounds on the restitution error in the Hopfield model
• Rigorous bounds on the storage capacity of the dilute Hopfield model
• Rigorous results for the Hopfield model with many patterns
• Capacity of an associative memory model on random graph architectures
• Emergence of Scaling in Random Networks
• The capacity of the Hopfield associative memory
• Error Detecting and Error Correcting Codes
• Collective dynamics of ‘small-world’ networks
• Neural networks and physical systems with emergent collective computational abilities.
• On the critical capacity of the Hopfield model.

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