While it may sound implausible, neurons are actually very slow, at least compared to computers, says Thomas Serre, a computational neuroscience researcher at MIT. "The reason why computers seem much slower is that they are serial machines, while our brains run in parallel," he says.

FACETS is not the only group taking this approach. Researchers at Stanford University have also been creating neuronal circuits and the Defense Advanced Research Projects Agency recently started funding a similar project.

"Where FACETS is ahead of anybody else is that they use these complex synapses," says Markram. While the neurons are quite simple, he says, the synapses are designed to use a very powerful distributed algorithm--developed by Markram--called spike-timing dependent plasticity, that allows the device to learn and adapt to new situations.

Building such complex circuits has required close collaboration with neurobiologists, says Markram. In fact, the project, whose current budget is €10.5 million (US$14.1 million), relies upon the contributions of 15 scientific groups from seven different countries. Among the challenges they face is recreating the three-dimensional structure of the brain in a 2-D piece of silicon, he says.

Despite efforts to make the chips as biologically plausible as possible, Markram admits they are still crude compared to what can be achieved in simulation. "It's not a brain. It's a more of a computer processor that has some of the accelerated parallel computing that the brain has," he says.

Because of this, Markram doubts that the hardware approach will offer much insight into how the brain works. For example, unlike Blue Brain, researchers won't be able to perform "in silico" drug testing, simulating the effects of drugs on the brain. "It's more a platform for artificial intelligence than understanding biology," he says.

The FACETS group now plans to further scale up their chips, connecting a number of wafers to create a superchip with a total of a billion neurons and 10¹³ synapses.