We study the effect of additive noise on integro-differential neural field
equations. In particular, we analyze an Amari-type model driven by a Q-Wiener
process and focus on noise-induced transitions and escape. We argue that
proving a sharp Kramers' law for neural fields poses substanial difficulties
but that one may transfer techniques from stochastic partial differential
equations to establish a large deviation principle (LDP). Then we demonstrate
that an efficient finite-dimensional approximation of the stochastic neural
field equation can be achieved using a Galerkin method and that the resulting
finite-dimensional rate function for the LDP can have a multi-scale structure
in certain cases. These results form the starting point for an efficient
practical computation of the LDP. Our approach also provides the technical
basis for further rigorous study of noise-induced transitions in neural fields
based on Galerkin approximations.Comment: 29 page