  In this paper, we examine the effects of correlated Gaussian noise on a
two-dimensional neuronal network that is locally modeled by the Rulkov map.
More precisely, we study the effects of the noise correlation on the variations
of the mean firing rate and the correlations among neurons versus the noise
intensity. Via numerical simulations, we show that the mean firing rate can
always be optimized at an intermediate noise intensity, irrespective of the
noise correlation. On the other hand, variations of the population coherence
with respect to the noise intensity are strongly influenced by the ratio
between local and global Gaussian noisy inputs. Biological implications of our
findings are also discussed.
