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 glow emitted by the neurons. I was looking to see whether a certain growth factor could protect the neurons from degenerating the way they do in patients with Parkinson’s disease.

This kind of work, which is common in neuroscience research, requires time and a borderline pathological attention to detail. Which is precisely why my PI trained me, a lowly undergrad, to do it—just as, decades earlier, someone had trained him.

Now, researchers think they can train machines to do that grunt work. In a study described in the latest issue of the journal Cell, scientists led by Gladstone Institutes and UC San Francisco neuroscientist Steven Finkbeiner collaborated with researchers at Google to train a machine learning algorithm to analyze neuronal cells in culture.

The researchers used a method called deep learning, the machine learning technique driving advancements not just at Google, but Amazon, Facebook, Microsoft. You know, the usual suspects. Read more from…

thumbnail courtesy of