Brains and computers, it has been observed before, are at once similar and different. The things in our heads and at our fingertips are both information processing systems, but they go about it in very different ways—which is to be suspected when comparing an organ crafted gradually over millions of years with a device that didn’t exist so much as a few decades ago. A team of British researchers is looking to close the gap, by building a computer that more closely mimics how the brain actually works. ARM is putting up a million of its processors to help in the task—but even with that processing power, the project only hopes to simulate about 1 percent of the human brain. The project has about $8 million in funding from EPRSC.
At the heart of the project is Steve Furber, a professor of computer engineering at the University of Manchester. One of his central interests is how it is that brains manage to continue to function when parts of it fail–a phenomenon known as plasticity—while the same is not true of computers. “We don’t know how to design things with that resilience,” he told ZDNet UK. His team will use the chips as part of its SpiNNaker project (a stylistically spelled “abbreviation” of Spiking Neural Network architecture), which, according to the project’s site, uses “massive parallelism and redundancy” to mimic the brain’s structure, which after all operates by conscripting billions of neurons to work in tandem.
The idea is that once the SpiNNaker project is fully operational, researchers from around the world could use it to test theories about how the human brain works. (A colorful recent example of research like this came when scientists at Yale and the University of Texas essentially gave a computer schizophrenia.) ZDNet UK poses the question that naturally arises from such research, though: Will we ever get to the point of building a fully digital brain–one with the same number of connections as the human one? Some thinkers say the answer is yes, among them the scientist Henry Markram, who recently predicted that we should be able to get there by 2023. Interestingly, both Markram and Furber underline academic funding models as one of the chief limitations to attaining such a goal.
In the meantime, at least, we’ll have researchers simulating parts of the brain—like these Stanford academics who recently developed a nanoelectric device that mimics human synapses. Taking a cue from evolution itself, perhaps we’ll finally arrive at a full digital brain only by building several different modules at once, and then cobbling them all together.