Mean-field dynamics of sequence processing neural networks with finite connectivity

Abstract

A recent dynamic mean-field theory for sequence processing in fully connected neural networks of Hopfield-type (During, Coolen and Sherrington, 1998) is extended and analized here for a symmetrically diluted network with finite connectivity near saturation. Equations for the dynamics and the stationary states are obtained for the macroscopic observables and the precise equivalence is established with the single-pattern retrieval problem in a layered feed-forward network with finite connectivity.

16 pages Latex

References in corpus (2)

• Transient dynamics for sequence processing neural networks
• Retrieval behavior and thermodynamic properties of symmetrically diluted Q-Ising neural networks

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• Transient dynamics for sequence processing neural networks: effect of degree distributions
• Pattern reconstruction and sequence processing in feed-forward layered neural networks near saturation
• Symmetric sequence processing in a recurrent neural network model with a synchronous dynamics