Transient dynamics for sequence processing neural networks: effect of degree distributions

Abstract

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.

11 pages, 6 figures

References in corpus (1)

• Performance of networks of artificial neurons: The role of clustering