This new algorithm will let tomorrow’s exascale supercomputers simulate an entire human brain

The renowned physicist Dr. Richard Feynman once said “What I cannot create, I do not understand. Know how to solve every problem that has been solved,” and now an increasingly influential subfield of neuroscience has taken Feynman’s words to heart. To theoretical neuroscientists the key to understanding how intelligence works is, apparently, to recreate it inside a supercomputer, neuron by neuron, and they’re hoping a new algorithm they’ve developed will very soon let them reconstruct the neural processes that produce a thought, a memory, or a feeling, and ultimately allow them to create a real life digital simulation of the entire human brain – something that’s never been done before.

With a digital brain in place they’ll be able to test out current theories of cognition and explore the parameters that lead to a “malfunctioning mind” and conditions like dementia. As philosopher Dr. Nick Bostrom at the University of Oxford argues, simulating the human mind is perhaps one of the most promising, albeit laborious, ways to recreate and surpass human level ingenuity, but there’s just one problem – traditional supercomputers still can’t handle the massively parallel nature of our brains. Squashed into our 3 pound organ are over 100 billion interconnected neurons and trillions of synapses, and that’s a lot for a computer, even a supercomputer, to get its circuits around.

Even the most powerful supercomputers today balk and fall over at that scale, for example, so far, machines like the ultra-powerful K computer at the Advanced Institute for Computational Science in Kobe, Japan can only simulate at most 10 percent of neurons and their synapses.

This is partially due to the software the systems are running, as computational hardware inevitably gets faster, it’s the algorithms that increasingly become the bottleneck that’s holding us back from realising our goal of 100 percent whole-brain simulation.

This month though an international team completely revamped the structure of a popular brain simulation algorithm, developing a powerful new algorithm that dramatically slashes computing time and memory use, and better yet it’s compatible with a wide range of computing hardware, from laptops to supercomputers, so when future exascale supercomputers hit the scene, which should be in the next year or two, which are projected to be 10 to 100 times more powerful than today’s top performers, the algorithm will be able to immediately run on them and “do its thing.”

“With this new technology we can exploit the increased parallelism of modern microprocessors a lot better than previously, which will become even more important in exascale computers,” said study author Jakob Jordan from the Jülich Research Center in Germany, who published the work in Frontiers in Neuroinformatics.

“It’s a decisive step towards creating the technology to achieve simulations of brain-scale networks,” the authors said.

Current supercomputers are composed of hundreds of thousands of subdomains called nodes, and each node has multiple processing centers that can support a handful of virtual neurons and their connections, but the main issue in brain simulation is how to effectively represent those millions of neurons and their connections inside these processing centers in a way that cuts down time and power.

One of the most popular simulation algorithms today is one called the “Memory Usage Model.” Before scientists simulate changes in their simulated neuronal networks they need to first create all the neurons and their connections within the virtual brain using the algorithm, but here’s the rub – for any neuronal pair, the model stores all information about connectivity in each node that houses the receiving neuron, something called the Postsynaptic neuron.