Computer scientists have tried to emulate the brain since the 1940s, when they first devised software structures called artificial neural networks. Most of today’s fanciest AIs use some modern form of this architecture: There are deep neural networks, convolutional neural networks, recurrent neural networks, and so on.
Loosely inspired by the brain’s structure, these networks consist of many computing nodes called artificial neurons, which perform small discrete tasks and connect to each other in ways that allow the overall systems to accomplish impressive feats.
Neural networks haven’t been able to copy the anatomical brain more closely because science still lacks basic information about neural circuitry. R. Jacob Vogelstein, the Microns project manager at IARPA, says researchers have typically worked on either the micro or macro scale. “The tools we used either involved poking individual neurons or aggregating signals across large swaths of the brain,” he says.
“The big gap is understanding operations on a circuit level—how thousands of neurons work together to process information.”
