There was a time when neuroscientists could only dream of having such a problem. Now the fantasy has come true, and they are struggling to solve it. Brilliant new exploratory devices are overwhelming the field with an avalanche of raw data about the nervous system's inner workings. The trouble is that even starting to make sense of this bonanza of information has become a superhuman challenge.
Just about every branch of science is facing a similar disruption. As laboratory-bench research migrates into the digital realm, programming is becoming an indispensable part of the process. At the same time, previously dependable sources of financial support are drying up. The result has been a painful scarcity of jobs and grants—which, in turn, is impelling far too many gifted researchers to focus on their narrow areas of specialization rather than investing time and energy into acquiring new, computer-age skills. In fields where data growth is especially out of control, such as neuroscience, the demand for computer expertise is growing as quickly as the information itself.
Science urgently needs hackers—hackers in the original, Tech Model Railroad Club of the Massachusetts Institute of Technology sense of the word. Their engineering and design skills will be useful, but what is most desirable is the true hacker's resourcefulness, curiosity and appetite for fresh challenges. Particularly in a field like neuroscience, helpers could be invaluable in exploring the daunting wilderness of newly revealed neural networks.
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A few pioneers are leading the way. One is H. Sebastian Seung, a professor at the Neuroscience Institute and in the department of computer science at Princeton University. A few years ago he and his collaborators set out to map the retina's neural connections. As they collected an overwhelming mass of electron microscopy data, the question was how they would ever manage to interpret it all. Seung's familiarity with state-of-the-art computing told him that no artificial-intelligence algorithm in existence could possibly handle the task alone.
The solution—then almost unheard of in lab science—was to enlist thousands of human volunteers alongside a state-of-the art AI and harness their collective brainpower. On December 10, 2012, Seung and his team launched the online game EyeWire, in which players score points by helping to improve a neural map. About a year and a half later the game's creators published their first discoveries in Nature, together with a note sharing coveted co-author credit with the 2,183 players who had reached the game's top ranks and made the paper possible. (Scientific American is part of Springer Nature.)
Hackers are finding their own routes into neuroscience. In late 2013 Brooklyn, N.Y.–based designers Joel Murphy and Conor Russomanno introduced OpenBCI, an “open-source brain-computer interface”—basically a home-brewed electroencephalographic device. Kits and plans are available from their Web site for just a fraction of a standard EEG's cost, and by all accounts it works just as well as the big-budget models. Their two-monthlong Kickstarter campaign sold nearly 1,000 units and caught the attention of academic research labs. It's just another example of how traditional barriers are crumbling between institutional science and individuals with new ideas. In fact, some labs have begun posting research challenges with cash prizes on crowdsourcing sites such as Kaggle and InnoCentive. These days if a research entity chooses not to explore such collaborative approaches, it is in danger of being left behind.
The software-design community has demonstrated over the past 20 years that massive online collaborations can work wonders. Today the physical sciences are only beginning to discover that potential. Established scientists would do well to recognize that true hackers are motivated by challenge and honest pride in seeing what they can do.
