  Local networks of neurons are nonlinear systems driven by synaptic currents
elicited by its own spiking activity and the input received from other brain
areas. Synaptic currents are well approximated by correlated Gaussian noise.
Besides, the population dynamics of neuronal networks is often found to be
multistable, allowing the noise source to induce state transitions. State
changes in neuronal systems underlies the way information is encoded and
transformed. The characterization of the escape time from metastable states is
then a cornerstone to understand how information is processed in the brain. The
effects of correlated input forcing bistable systems have been studied for over
half a century, nonetheless most results are perturbative or valid only when a
separation of time scales is present. Here, we present a novel and exact result
holding when the correlation time of the noise source is identical to that of
the neural population, hence solving in a very general setting the mean escape
time problem.
