Advertisement

AI Learns a New Trick: Measuring Brain Cells

AI Learns a New Trick: Measuring Brain Cells
From Wired - April 12, 2018

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 itjust 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. It relies on pattern recognition: Feed the system enough training datawhether it's pictures of animals, moves from expert players of the board game Go, or photographs of cultured brain cellsand it can learn to identify cats, trounce the world's best board-game players, or suss out the morphological features of neurons.

Two of the most difficult things about training an AI in this fashion are generating a sufficiently large dataset and getting people to annotate that dataset. Fortunately, most neuroscience labs have an abundance of cell cultures to convert into training data (Finkbeiner's lab, which has automated various other parts of the microscopy process, already produces more images than it can analyze), and plenty of lab hands to label that data for training purposes.

How AI Could Revolutionize Research

Advertisement

Continue reading at Wired »