Order-parameter flow in the fully connected Hopfield model near saturation
- 1 March 1994
- journal article
- research article
- Published by American Physical Society (APS) in Physical Review E
- Vol. 49 (3) , 1921-1934
- https://doi.org/10.1103/physreve.49.1921
Abstract
We present an exact dynamical theory, valid on finite time scales, to describe the fully connected Hopfield model near saturation in terms of deterministic flow equations for order parameters. Two transparent assumptions allow us to perform a replica calculation of the distribution of intrinsic-noise components of the alignment fields. Numerical simulations support our assumptions and indicate that our equations describe the shape of the intrinsic-noise distribution and the macroscopic dynamics correctly in the region where replica symmetry is stable. In equilibrium our theory reproduces the saddle-point equations obtained in the thermodynamic analysis by Amit, Gutfreund, and Sompolinsky [Phys. Rev. A 32, 1007 (1985); Phys. Rev. Lett. 55, 1530 (1985)], the only difference being the absence in the present formalism of negative entropies at low temperatures.Keywords
This publication has 20 references indexed in Scilit:
- Transients and basins of attraction in neutral network modelsZeitschrift für Physik B Condensed Matter, 1989
- Glauber dynamics of the Little-Hopfield modelZeitschrift für Physik B Condensed Matter, 1988
- Image evolution in Hopfield networksPhysical Review A, 1988
- Temporal sequences and chaos in neural netsPhysical Review A, 1988
- An Exactly Solvable Asymmetric Neural Network ModelEurophysics Letters, 1987
- Zero temperature parallel dynamics for infinite range spin glasses and neural networksJournal de Physique, 1987
- Saturation Level of the Hopfield Model for Neural NetworkEurophysics Letters, 1986
- 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
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982