Supercomputer Launches World’s Largest Neuronal-Network Simulation

Researchers in Japan and Germany have carried out what’s being described as the largest neuronal network simulation to date.

That simulation leveraged open-source NEST software running on K computer, a Fujitsu-manufactured supercomputer based at the RIKEN Advanced Institute for Computational Science (AICS) in Japan. K computer ranked fourth on the most recent Top500 list, a ranking of the world’s fastest supercomputers; the platform, armed with 705,024 cores, is capable of 10,510 teraflops of performance (as measured via the Linpack benchmark; in theory, the system could push that to 11,280.4 teraflops).

In conjunction with a research team at the Institute of Neuroscience and Medicine at Jülich, K computer simulated a neuronal network of 1.73 billion nerve cells connected by 10.4 trillion synapses. That sounds like a whole lot of nerve cells and synapses, but in fact it’s only 1 percent of the neuronal network in the brain.

“If peta-scale computers like the K computer are capable of representing 1 percent of the network of a human brain today,” team leader Markus Diesmann wrote in a statement, “then we know that simulating the whole brain at the level of the individual nerve cell and its synapses will be possible with exa-scale computers hopefully available within the next decade.”

The researchers didn’t intend for the experiment to discover anything new about the brain—the nerve cells were randomly connected—but they wanted to see how well a supercomputer could model that sort of network.

Other researchers are using supercomputers to uncover the mysteries of the human cortex. A project underway at the Texas Advanced Computing Center (TACC), for example, is using the OpenfMRI platform (which draws data from functional magnetic resonance imaging (fMRI) machines) to craft models of how the brain’s neurons operate. The ultimate goal is to build a comprehensive model of the brain at work, from the most basic processes all the way up to higher cognition, which could give some insight into how and why brains break down (and lead in turn to future treatments).

 

Image: sfam_photo/Shutterstock.com

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