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.
Next steps: The researchers say that their high-throughput approach could be applied to other areas of computer vision, such as face identification, object tracking, pedestrian detection for automotive applications, and gesture and action recognition. Moreover, as scientists better understand what components make a good artificial vision system, they can use these hints to better understand the human brain as well.
Source: Pinto N, Doukhan D, DiCarlo JJ, Cox DD. A high-throughput screening approach to good forms of biologically-inspired visual representation. PLoS Computational Biology. Nov 26 2009.
Funding: National Institutes of Health, McKnight Endowment for Neuroscience, Jerry and Marge Burnett, the McGovern Institute for Brain Research at MIT, and the Rowland Institute at Harvard. Hardware support provided by the NVIDIA Corporation.
MIT and Harvard researchers demonstrate a better way for computers to ‘see’