To construct their model, the researchers used an fMRI machine, which measures blood flow through the brain, to track neural activity in three people as they looked at pictures of everyday settings and objects.

As in the earlier study, they looked at parts of the brain linked to the shape of objects. Unlike before, they looked at regions whose activity correlates with general classifications, such as "buildings" or "small groups of people."

Once the model was calibrated, the test subjects looked at another set of pictures. After interpreting the resulting neural patterns, the researchers' program plucked corresponding pictures from a database of 6 million images.

Frank Tong, a Vanderbilt University neuroscientist who studies how thoughts are manifested in the brain, said the Neuron study wasn't quite a pure, draw-from-scratch reconstruction. But it was impressive nonetheless, especially for the detail it gathered from measurements that are still extremely coarse.

The researchers' fMRI readings bundled the output of millions of neurons into single output blocks. "At the finer level, there is a ton of information. We just don't have a way to tap into that without opening the skull and accessing it directly," said Tong.

Gallant hopes to develop methods of interpreting other types of brain activity measurement, such as optical laser scans or EEG readings.

He mentioned medical communication devices as a possible application, and computer programs for which visual thinking makes sense -- CAD-CAM or Photoshop, straight from the brain.

Such applications are decades away, but "you could use algorithms like this to decode other things than vision," said Gallant. "In theory, you could analyze internal speech. You could have someone talk to themselves, and have it come out in a machine."

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