Synchronous versus sequential updating in the three-state Ising neural network with variable dilution

Abstract

The three-state Ising neural network with synchronous updating and variable dilution is discussed starting from the appropriate Hamiltonians. The thermodynamic and retrieval properties are examined using replica mean-field theory. Capacity-temperature phase diagrams are derived for several values of the pattern activity and different gradations of dilution, and the information content is calculated. The results are compared with those for sequential updating. The effect of self-coupling is established. Also the dynamics is studied using the generating function technique for both synchronous and sequential updating. Typical flow diagrams for the overlap order parameter are presented. The differences with the signal-to-noise approach are outlined.

21 pages Latex, 12 eps figures and 1 ps figure

References in corpus (7)

• The Little-Hopfield model on a Random Graph
• The signal-to-noise analysis of the Little-Hopfield model revisited
• One+Infinite Dimensional Attractor Neural Networks
• Retrieval behavior and thermodynamic properties of symmetrically diluted Q-Ising neural networks
• Parallel dynamics of extremely diluted symmetric Q-Ising neural networks
• Thermodynamic properties of extremely diluted symmetric Q-Ising neural networks
• The three-state layered neural network with finite dilution

Cited by in corpus (2)

• Instability of frozen-in states in synchronous Hebbian neural networks
• Symmetric sequence processing in a recurrent neural network model with a synchronous dynamics