However, Boahen explains, the chip has the ability to change the strength of these connections, imitating what happens with neurons during Hebbian learning. The silicon cells monitor when their neighbors fire. If a cell fires just before its neighbor does, then the programmed connection between the two cells is strengthened. “We want to capture the associative memory function, so we want connections between the cells to turn on or off depending on whether cells are activated together,” Boahen says.
Sitting at his desk with the circuit board and a laptop in front of him, Arthur, who is now a postdoc in Boahen’s lab, demonstrates the chip’s ability to remember. First he sends electrical signals to the chip from the laptop, which also records the output of the chip’s silicon neurons. He repeatedly triggers activity only in neurons that form a U shape on the array; his laptop screen shows flashes of light that reproduce that pattern, representing the activity in the chip. Each neuron fires at a slightly different time, constantly monitoring the firing of its 21 connected neighbors. Gradually, connections between the neurons that make up the U are strengthened: the chip has “learned” the pattern. When Arthur then triggers activity in just the upper left corner of the U, flashes of light on the screen spontaneously re-create the rest of the pattern, as electrical activity spreads among silicon neurons on the chip. The chip has effectively recalled the rest of the U.
The Stanford researchers plan to add circuitry to the chip so that it will also model a layer of the hippocampus known as the dentate, which receives signals from the cortex and sends them to CA3. They hope this model will be able to lay down memories that are even more complex. “We want to be able to give it an A and have it recall the whole alphabet,” says Boahen.
The team is also in the process of developing other neuromorphic chips. Its latest project–and the most ambitious neuromorphic effort anywhere to date–is a model of the cortex, the most recently evolved part of our brain. The intricate structure of the cortex allows us to perform complex computational feats, such as understanding language, recognizing faces, and planning for the future. The model’s first-generation design will consist of a circuit board with 16 chips, each containing a 256-by-256 array of silicon neurons.
By creating chips that are able to mimic the cortex, the hippocampus, and the retina, Boahen hopes to better comprehend the brain and, eventually, to design neural prosthetics, such as an artificial retina. “Kwabena is one of the few people straddling two perspectives: those who want to engineer better chips and those who want to understand the brain,” says Terry Sejnowski, a computational neuroscientist at the Salk Institute in La Jolla, CA. “I think he’s one of those people who is ahead of his time.”
Emily Singer is the biotechnology and life sciences editor of Technology Review.