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An essential question confronting neuroscientists and computer vision researchers alike is how objects can be identified by simply “looking” at an image.
Introspectively, it is known that the human brain solves this problem very well. It is known that we only have to look at something to know what it is.
But teaching a computer to “know” what it’s looking at is far harder.
In research published this fall in the Public Library of Science (PLoS) Computational Biology journal, a team from Los Alamos National Laboratory, Chatham University, and Emory University first measured human performance on a visual task — identifying a certain kind of shape when an image is flashed in front of a viewer for a very short amount of time (20-200 milliseconds).
Human performance gets worse, as expected, when the image is shown for shorter time periods. Also as expected, humans do worse when the shapes are more complicated.
But could a computer be taught to recognize shapes as well, and then do it faster than humans?
The team tried developing a computer model based on human neural structure and function, to do what we do, and possibly do it better.
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