  We are concerned with the complexity reduction of a stochastic system of
differential equations governing the dynamics of a neuronal circuit describing
a decision-making task. This reduction is based on the slow-fast behavior of
the problem and holds on the whole phase space and not only locally around the
spontaneous state. Macroscopic quantities, such as performance and reaction
times, computed applying this reduction are in agreement with previous works in
which the complexity reduction is locally performed at the spontaneous point by
means of a Taylor expansion.
