From: http://web.mit.edu/press/2009/visual-systems.html

Taking inspiration from genetic screening techniques, researchers from MIT and Harvard have demonstrated a way to build better artificial visual systems with the help of low-cost, high-performance gaming hardware.

The neural processing involved in visually recognizing even the simplest object in a natural environment is profound — and profoundly difficult to mimic. Neuroscientists have made broad advances in understanding the visual system, but much of the inner workings of biologically based systems remain a mystery.

Using Graphics Processing Units (GPUs) — the same technology video game designers use to render life-like graphics — MIT and Harvard researchers are now making progress faster than ever before. “We made a powerful computing system that delivers over hundred fold speed-ups relative to conventional methods,” said Nicolas Pinto, a PhD candidate in James DiCarlo’s lab at the McGovern Institute for Brain Research at MIT. “With this extra computational power, we can discover new vision models that traditional methods miss.” Pinto co-authored the PLoS study with David Cox of the Visual Neuroscience Group at the Rowland Institute at Harvard.

Finding a better way for computers to “see” from Cox Lab @ Rowland Institute on Vimeo.
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From the Evolved Machines Website:

“We simulate neuronal components closely modeled after neurons in the brain, and synthesize arrays which wire themselves by simulating neural circuit growth in three dimensions. We are the first company to harness the power of programmable GPUs for the simulation of neural computation, now achieving 100-fold acceleration of the computing power of conventional cores. We are also designing the first generation of devices truly based on brain circuitry, pioneering the fusion of neuroscience and engineering to develop new categories of machines which embed some of the capacities of biological neural systems.”