The attractors in sequence processing neural networks

Abstract

The average length and average relaxation time of attractors in sequence processing neural networks are investigated. The simulation results show that a critical point of $\alpha$, the loading ratio, is found. Below the turning point, the average length is equal to the number of stored patterns; conversely, the ratio of length and numbers of stored patterns, grow with an exponential dependence $\exp (A\alpha )$. Moreover, we find that the logarithm of average relaxation time is only linearly associated with $\alpha$ and the turning point of coupling degree is located for examining robustness of networks.

3 pages, 6 figures

References in corpus (2)

• Phase Diagram and Storage Capacity of Sequence Processing Neural Networks
• Transition from regular to complex behaviour in a discrete deterministic asymmetric neural network model

Cited by in corpus (1)

• Coupling parameter in synchronization of diluted neural networks