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Description: Spatial attention enhances sensory processing of goal-relevant information and improves perceptual sensitivity. The specific mechanisms linking neural changes to changes in performance are still contested. Here, we examine different attention mechanisms in spiking deep convolutional neural networks. We directly contrast effects of noise suppression (precision) and two different gain modulation mechanisms on performance on a visual search task with complex real-world images. Unlike standard artificial neurons, biological neurons have saturating activation functions, permitting implementation of attentional gain as gain on a neuron's input or on its outgoing connection.