Complexity reduction of rate-equations models for two-choice decision-making

Abstract

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