Continuous-time dynamics of asymmetrically diluted neural networks
- 1 November 1987
- journal article
- research article
- Published by American Physical Society (APS) in Physical Review A
- Vol. 36 (9), 4421-4427
- https://doi.org/10.1103/physreva.36.4421
Abstract
We study the continuous-time dynamics of a strongly diluted Hopfield model with asymmetric synaptic connections. The model is exactly soluble for static as well as dynamic properties. The time evolution of the autocorrelation, the susceptibility, and the overlap function of two configurations are given in explicit form.Keywords
This publication has 17 references indexed in Scilit:
- An Exactly Solvable Asymmetric Neural Network ModelEurophysics Letters, 1987
- Path-integral approach to Ising spin-glass dynamicsPhysical Review Letters, 1987
- Statistical mechanics of neural networks near saturationAnnals of Physics, 1987
- Storing Infinite Numbers of Patterns in a Spin-Glass Model of Neural NetworksPhysical Review Letters, 1985
- Spin-glass models of neural networksPhysical Review A, 1985
- Neurons with graded response have collective computational properties like those of two-state neurons.Proceedings of the National Academy of Sciences, 1984
- Relaxational dynamics of the Edwards-Anderson model and the mean-field theory of spin-glassesPhysical Review B, 1982
- 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
- Time-Dependent Statistics of the Ising ModelJournal of Mathematical Physics, 1963