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 $α$, 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 (Aα) $. Moreover, we find that the logarithm of average relaxation time is only linearly associated with $α$ and the turning point of coupling degree is located for examining robustness of networks.