In 2007, I spent the summer before my junior year of college removing little bits of brain from rats, growing them in tiny plastic dishes, and poring over the neurons in each one. For three months, I spent three or four hours a day, five or six days a week, in a small room, peering through a microscope and snapping photos of the brain cells. The room was pitch black, save for the green light emitted by the neurons.
I was looking to see whether a certain growth factor could protect the neurons from degenerating the route they do in patients with “Parkinsons disease”. This various kinds of work, which is common in neuroscience research, necessitates day and a borderline pathological attention to detail. Which is precisely why my Pu developed me, a lowly undergrad, to do it–just as, decades earlier, someone had developed him.
Now, researchers think they can train machines to do that grunt work.
In a study described in the latest issue of the publication Cell, scientists led by Gladstone Institute and UC San Francisco neuroscientist Steven Finkbeiner collaborated with researchers at Google to train a machine learning algorithm to analyze neuronal cells in culture.