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To do that, they are teaching the computers to analyze the brain slices, using a common computer science technique called automated machine learning, which allows computers to change their behavior in response to new data.

With machine learning, the researchers teach computers to learn by example. They feed their computer electron micrographs as well as human tracings of these images. The computer then searches for an algorithm that allows it to imitate human performance.

Their eventual goal is to use computers to process the bulk of the images needed to create connectomes, but they expect that humans will still need to proofread the computers’ work.

Last year, the National Institutes of Health announced a five-year, $30 million Human Connectome Project to develop new techniques to figure out the connectivity of the human brain. That project is focused mainly on higher level, region-to-region connections. Sporns says he believes that a good draft of higher-level connections could be achieved within the five-year timeline of the NIH project, and that significant progress will also be made toward a neuron-to-neuron map.

Though only a handful of labs around the world are working on the connectome right now, Jain and Turaga expect that to change as tools for diagramming the brain improve. “It’s a common pattern in neuroscience: A few people will come up with new technology and pioneer some applications, and then everybody else will start to adopt it,” says Jain.