This story is from the category The Brain
Date posted: 20/03/2007
On the spur of hearing one brain modelling project is being shut down, we bring news of another one starting up. An ambitious project to model the cerebral cortex in silicon is under way at Stanford. The man-made brain could help scientists understand how the most recently evolved part of our brain performs its complex computational feats, allowing us to understand language, recognise faces, and schedule the day. It could also lead to new neural prosthetics.
"Brains do things in technically and conceptually novel ways--they can solve rather effortlessly issues which we cannot yet resolve with the largest and most modern digital machines," says Rodney Douglas, a professor at the Institute of Neuroinformatics, in Zurich. "One of the ways to explore this is to develop hardware that goes in the same direction."
Kwabena Boahen, a neuroengineer at Stanford University, is planning the most ambitious neuromorphic project to date: creating a silicon model of the cortex. The first-generation design will be composed of a circuit board with 16 chips, each containing a 256-by-256 array of silicon neurons. Groups of neurons can be set to have different electrical properties, mimicking different types of cells in the cortex. Engineers can also program specific connections between the cells to model the architecture in different parts of the cortex.
"We want to be able to explore different ideas, different connectivity patterns, different operations in these areas. It's not really possible to explore that right now."
The million-neuron grid will have a processing speed equivalent to 300 teraflops, meaning that unlike computer-software simulations of the cortex, the hardwired silicon model will be able to run in real time. "Instead of running a thousand software instructions, it's just current running through transistors, just like real neurons," says Boahen.
Boahen ultimately plans to build chips that other scientists can buy and use to test their own theories of how the cortex operates. That new knowledge can then be built into the next generation of chips.
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