Retrieval via non-equilibrium states in neural networks
- 21 June 1988
- journal article
- Published by IOP Publishing in Journal of Physics A: General Physics
- Vol. 21 (12) , L667-L671
- https://doi.org/10.1088/0305-4470/21/12/009
Abstract
The authors describe a simple variant of Hopfield's (1982) or Little's (1974) model for neural networks, which is able to act as an associative memory even in the heavily overloaded spin-glass phase, using non-equilibrium states as attractors.Keywords
This publication has 10 references indexed in Scilit:
- Statistical mechanics of neural networks near saturationPublished by Elsevier ,2004
- Information processing in synchronous neural networksJournal de Physique, 1988
- Information storage and retrieval in synchronous neural networksPhysical Review A, 1987
- Maximum Storage Capacity in Neural NetworksEurophysics Letters, 1987
- Associative recall of memory without errorsPhysical Review A, 1987
- Zero temperature parallel dynamics for infinite range spin glasses and neural networksJournal de Physique, 1987
- Structure of metastable states in the Hopfield modelJournal of Physics A: General Physics, 1986
- Collective computational properties of neural networks: New learning mechanismsPhysical Review A, 1986
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982
- The existence of persistent states in the brainMathematical Biosciences, 1974