Retrieval of spatio-temporal sequence in asynchronous neural network
- 1 March 1990
- journal article
- research article
- Published by American Physical Society (APS) in Physical Review A
- Vol. 41 (6) , 3346-3354
- https://doi.org/10.1103/physreva.41.3346
Abstract
We develop an argument that a neural network with asynchronous updating dynamics is capable of sequential retrieval of properly embedded spatial patterns. It is not necessary that the patterns be random, but they have to satisfy certain restrictions. The network is a generalization of the Hopfield model with a specific type of asymmetric synaptic connections. No time delay is introduced in signal transmission, in contrast to some of the recently proposed networks.Keywords
This publication has 10 references indexed in Scilit:
- Nonlinear Master Equation Approach to Asymmertrical Neural Networks of the Hopfield-Hemmen TypeJournal of the Physics Society Japan, 1989
- Glauber dynamics of the Little-Hopfield modelZeitschrift für Physik B Condensed Matter, 1988
- The Hebb Rule: Storing Static and Dynamic Objects in an Associative Neural NetworkEurophysics Letters, 1988
- Image evolution in Hopfield networksPhysical Review A, 1988
- Temporal sequences and chaos in neural netsPhysical Review A, 1988
- Noise-Driven Temporal Association in Neural NetworksEurophysics Letters, 1987
- Temporal Association in Asymmetric Neural NetworksPhysical Review Letters, 1986
- Sequential state generation by model neural networks.Proceedings of the National Academy of Sciences, 1986
- Spin-glass models of neural networksPhysical Review A, 1985
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982