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One of the more interesting goals in neuroscience is to reconstruct perceived images by analyzing brain scans. The idea is to work out what people are looking at by monitoring the activity in their visual cortex.

The difficulty, of course, is finding ways to efficiently process the data from functional magnetic resonance imaging (fMRI) scans. The task is to map the activity in three-dimensional voxels inside the brain to two-dimensional pixels in an image.

That turns out to be hard. fMRI scans are famously noisy, and the activity in one voxel is well known to be influenced by activity in other voxels. This kind of correlation is computationally expensive to deal with; indeed, most approaches simply ignore it. And that significantly reduces the quality of the image reconstructions they produce.

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Image: By Dwayne Reed at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=4390827

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MIT Technology Review

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