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A notable feature of neural activity is sparseness – namely, that only a small fraction of neurons in a local circuit have high activity at any moment. Not only is sparse neural activity observed experimentally in most areas of the brain, but sparseness has been proposed as an optimization or design principle for neural circuits. Sparseness can increase the energy efficiency of the neural code as well as allow for beneficial computations to be carried out. But how does the brain achieve sparseness? Here, we found that when neurons in the primary visual cortex were passively exposed to a set of images over several days, neural responses became more sparse. Sparsification was driven by a decrease in the response of neurons with low or moderate activity, while highly activeneurons retained similar responses. We also observed a net decorrelation of neural activity. These changes sculptneural activity for greater coding efficiency.