The attractors in sequence processing neural networks

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.