A recent poster by Los Alamos National Laboratory researchers led by Steven Brumby, titled "Visual Cortex on a Chip: Large-scale, real-time functional models of mammalian visual cortex on a GPGPU", shows another interesting application of graphic processing units (GPUs) to computational neuroscience. What is GPGPU?
The term GPGPU stands for General-Purpose computation on Graphics Processing Units. GPUs, chips whose main technological push comes from huge revenues from the gaming market, are in reality high-performance many-core processors that can be used to accelerate a wide range of applications, going from physics, to chemestry, to computer vision, to neuroscience.
The poster by Brumby et al. is remarkable from being an attempt to implement, in a Neocognitron-type hierarchical model, brain areas of the the ventral pathway of visual processing (V1, V2, V4, and inferotemporal cortex, or IT). This pathway is the one believed to be responsible for object recognition.
Brumby et al. conclude that "GPGPU acceleration may be the key enabling technology for this type of application, as exploiting the GPGPU enables a better than order-of-magnitude speed-up of execution of the model on workstations, enabling learning of better models and faster execution of the final model."
Having played with GPGPU before the introduction of CUDA (harder times, but good ones nevertheless: 10X to 100X speedup on spiking neurons simulations were not unfeasible...), and witnessed the progressive expansion of GPGPU in more and more application domains, it will not be unexpected to see larger brain areas simulated on GPUs in the near future.
And yes, when you will buy your next video game, remember: you are indirectly enabling better computational neuroscience simulations!