Thermodynamic properties of extremely diluted symmetric Q-Ising neural networks

Abstract

Using the replica-symmetric mean-field theory approach the thermodynamic and retrieval properties of extremely diluted {\it symmetric} $Q$-Ising neural networks are studied. In particular, capacity-gain parameter and capacity-temperature phase diagrams are derived for $Q=3,4$ and $Q=\infty$. The zero-temperature results are compared with those obtained from a study of the dynamics of the model. Furthermore, the de Almeida-Thouless line is determined. Where appropriate, the difference with other $Q$-Ising architectures is outlined.

16 pages Latex including 6 eps-figures. Corrections, also in most of the figures have been made

References in corpus (1)

• Parallel dynamics of extremely diluted symmetric Q-Ising neural networks

Cited by in corpus (3)

• The Little-Hopfield model on a Random Graph
• Retrieval behavior and thermodynamic properties of symmetrically diluted Q-Ising neural networks
• Synchronous versus sequential updating in the three-state Ising neural network with variable dilution