A key problem in systems neuroscience is to understand how neural populations integrate relevant sensory inputs during decision-making. Here, we address this problem by training a structured recurrent neural network to reproduce both psychophysical behavior and neural responses recorded from monkey prefrontal cortex during a context-dependent per-ceptual decision-making task. Our approach yields a one-to-one mapping of model neurons to recorded neurons, and explicitly incorporates sensory noise governing the animal’s performance as a function of stimulus strength. We then analyze the dynamics of the resulting model in order to understand how the network computes context-dependent decisions. We find that network dynamics preserve both relevant and irrelevant stimulus information, and exhibit a grid of fixed points for different stimulus conditions as opposed to a one-dimensional line attractor. Our work provides new insights into context-dependent decision-making and offers a powerful framework for linking cognitive function with neural activity within an artificial model.