If all goes well, 2009 will be the year that bioengineering Associate Professor Kwabena Boahen unveils a unique computer that he has been dreaming of for decades. Modeled on the brain but built in silicon, it will have the equivalent of a million brain cells, or neurons, giving it the intellectual power of, well, a honeybee. Not impressed? Step back and reconsider that it will be a silicon brain, implementing nature as an information technology to an unprecedented degree.

“It’s not going to be very smart,” Boahen acknowledges. “The more important point is that it’s large enough for us to be able to model areas of cortex and have them talk to each other in enough detail that we can identify what these neurons are doing and how that affects how they interact.”

That’s the beginning stage of replicating cognition on a chip, an achievement that could not only yield new insight into the fog that sits between neuroscience and psychology, but also produce a very efficient and inexpensive supercomputer. Boahen estimates that one of the world’s faster supercomputers, IBM’s Blue Gene, processes information at roughly the same speed as the human brain, but uses 100,000 times as much power. Nature has a much more efficient architecture.

And so, he strives to make computers based on neurobiology by arranging conventional transistors—the binary electrical switches on which computer chips are based—into groupings that emulate the workings of nuerons.

In August 2008 Boahen’s research group celebrated a milestone. They commissioned the manufacturing of a new chip, called NeuroCore, with 65,536 silicon neurons. Each NeuroCore is made to be directly connected to up to three others, forming a 16-chip system called a NeuroGrid. That million-neuron computer will be, by far, the most sophisticated neurologically based, or “neuromorphic,” computer ever made. Once assembled, the NeuroGrid will allow Boahen to run experiments that will advance the goal of using a neural architecture for computation.

Building up the nerve

Boahen first became interested in how the nervous system processes information while a junior studying electrical engineering at Johns Hopkins University. He had come to Baltimore from his hometown of Accra, Ghana, where as a teenager he was both fascinated by and disappointed in his first computer, a Radio Shack TRS-80. The way the computer handled information, with a rigidly timed series of 0s and 1s, seemed inelegant to him. When Boahen attended a lecture by Johns Hopkins neural network researcher Terrence J. Sejnowski, who in 1986 had co-developed a system called NETtalk that could read text aloud, Boahen became inspired to think that neuroscience provided a better computational model.

“I thought that was very elegant,” Boahen said. “That got me very interested in the biological approach.”

Before coming to Stanford in 2005, Boahen was a professor at the University of Pennsylvania. That’s where he began building neural circuitry in silicon. His first real success wasn’t with the brain, but with another better-studied member of the nervous system: the retina. Using what amounted to a “blueprint” developed by neuroscientists, he figured out how to build it with transistors. The silicon retina successfully captures images much like a real part of the retina does, by tracking the moving edges of objects (the eye looks at things many different ways, leaving the brain to reconcile the information to produce the images we perceive). In recent years Boahen has also made a chip that acts as an artificial inner ear.

From binary to brain-like

In some ways, transistors and neurons couldn’t be more different. Transistors are precise, punctual, and reliable, but neurons are sloppy and slow. Microprocessors are methodical, serial, and fixed, while the brain is spontaneous, parallel, and constantly remaking itself. It turns out that to design a circuit that can properly mimic the behavior of even a simplified neuron, Boahen needs about 340 transistors. That means each NeuroCore chip employs more than 22 million transistors to create 65,536 neurons.

Most of the transistors compose an electrical analogue of the ion channels that allow real neurons to send and receive signals electrochemically.

“The motivation for this work is to build a model that has a lot of biological fidelity,” Boahen says. “Each neuron has a repertoire of ion channels and that determines their specific behavior.”

Boahen’s design allows for his neurons to have four such “channel populations,” or to combine those into two more complex ones. This flexibility allows the neurons to be programmable, taking on different types and behaviors and establishing or breaking connections, much as real neurons in the brain do.

NueroCore dedicates about a million transistors to creating a network infrastructure that allows Boahen to study how individual nuerons are working and to reporgram them. That infrastructure is what makes the chip a useful research platform.

This is not the first neuromorphic chip, as both Boahen and others in his small field have made some before. But Boahen’s is the biggest and best architected one so far; the previous best system included four chips for a total of 45,000 neuron equivalents. And while the prior systems were all simply strung together like beads on a necklace, the NeuroGrid will arrange its 16 chips in a much more tightly integrated “tree” structure, in which each chip is connected to one above and two below.

The tree structure and the integrated network infrastructure on the NeuroCore chips combine to allow the NeuroGrid to achieve its 20-fold improvement in scale. Ultimately many more innovations and probably decades will likely be necessary before Boahen or anyone else could create a machine that rivals the human brain. But for now, NeuroGrid, with its honeybee brain, seems certain to strike many researchers as the “bee’s knees.”