  We derive a analytic evolution equation for overlap parameters including the
effect of degree distribution on the transient dynamics of sequence processing
neural networks. In the special case of globally coupled networks, the
precisely retrieved critical loading ratio $\alpha_c = N ^{-1/2}$ is obtained,
where $N$ is the network size. In the presence of random networks, our
theoretical predictions agree quantitatively with the numerical experiments for
delta, binomial, and power-law degree distributions.
