  Derived from spiking neuron models via the diffusion approximation, the
moment activation (MA) faithfully captures the nonlinear coupling of correlated
neural variability. However, numerical evaluation of the MA faces significant
challenges due to a number of ill-conditioned Dawson-like functions. By
deriving asymptotic expansions of these functions, we develop an efficient
numerical algorithm for evaluating the MA and its derivatives ensuring
reliability, speed, and accuracy. We also provide exact analytical expressions
for the MA in the weak fluctuation limit. Powered by this efficient algorithm,
the MA may serve as an effective tool for investigating the dynamics of
correlated neural variability in large-scale spiking neural circuits.
