  Signal transmission delays tend to destabilize dynamical networks leading to
oscillation, but their dispersion contributes oppositely toward stabilization.
We analyze an integro-differential equation that describes the collective
dynamics of a neural network with distributed signal delays. With the gamma
distributed delays less dispersed than exponential distribution, the system
exhibits reentrant phenomena, in which the stability is once lost but then
recovered as the mean delay is increased. With delays dispersed more highly
than exponential, the system never destabilizes.
