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Full scan data of 1 cubic mm of the human brain took 1.4 petabytes of memory only for saving sample images. A deep dive into neural mapping was done by researchers from Harvard and artificial intelligence experts at Google. Expanding this experiment to the entire brain would take 1.6 zettabytes, the necessary drives would cost $50 billion, and the data center would occupy 57 hectares.
To image the human brain, the researchers needed to use Google's machine learning technology, which saved years of work. First, the scientists cut the sample into 5,000 plates, the thickness of which is an order of magnitude smaller than a human hair. Electron microscope images of each section were taken and recombined to calculate about 57,000 cells and 150 million synapses. Google AI image processing technology had to be used to recombine these images and accurately map fibers and cells.
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The images created revealed many secrets about the brain. Some cell clusters grew as mirror images of each other, one neuron had more than 5,000 connections to others, and some axons curled tightly into ball shapes for unknown reasons. These and other discoveries made during the research greatly excited scientists.
“We found a lot of things in this data set that are not in the textbooks. We don't understand these things, but I can tell you it means [a gap] between what we already know and what we need to know.” – Jeff Lichtman, professor at Harvard University.
The human brain is incredibly dense and very, very complex; mapping it completely using these methods would be incredibly expensive, time-consuming, and unlikely to be feasible. Just because mapping has been done does not mean that scientists will know what to do with the maps, since only a millionth of the brain will capture the attention of scientists for many years. The study was published in the journal Science.
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Sources: The Guardian, Tom`s Hardware
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