An international team of scientists in Europe has created a silicon chip designed to function like a human brain. With 200,000 neurons linked up by 50 million synaptic connections, the chip is able to mimic the brain’s ability to learn more closely than any other machine.
Although the chip has a fraction of the number of neurons or connections found in a brain, its design allows it to be scaled up, says Karlheinz Meier, a physicist at Heidelberg University, in Germany, who has coordinated the Fast Analog Computing with Emergent Transient States project, or FACETS.
The hope is that recreating the structure of the brain in computer form may help to further our understanding of how to develop massively parallel, powerful new computers, says Meier.
This is not the first time someone has tried to recreate the workings of the brain. One effort called the Blue Brain project, run by Henry Markram at the Ecole Polytechnique Fédérale de Lausanne, in Switzerland, has been using vast databases of biological data recorded by neurologists to create a hugely complex and realistic simulation of the brain on an IBM supercomputer.
FACETS has been tapping into the same databases. “But rather than simulating neurons,” says Karlheinz, “we are building them.” Using a standard eight-inch silicon wafer, the researchers recreate the neurons and synapses as circuits of transistors and capacitors, designed to produce the same sort of electrical activity as their biological counterparts.
A neuron circuit typically consists of about 100 components, while a synapse requires only about 20. However, because there are so much more of them, the synapses take up most of the space on the wafer, says Karlheinz.
The advantage of this hardwired approach, as opposed to a simulation, Karlheinz continues, is that it allows researchers to recreate the brain-like structure in a way that is truly parallel. Getting simulations to run in real time requires huge amounts of computing power. Plus, physical models are able to run much faster and are more scalable. In fact, the current prototype can operate about 100,000 times faster than a real human brain. “We can simulate a day in a second,” says Karlheinz.
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.
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