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

Complexity Reduction of Rate-Equations Models for Two-Choice Decision-Making