“The Digital Brain” –Amazing New Algorithm Recreates Human Brain to Understand Intelligence

 

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With a digital brain in place, scientists can test out current theories of cognition or explore the parameters that lead to a malfunctioning mind. As philosopher Dr. Nick Bostrom at the University of Oxford argues, simulating the human mind is perhaps one of the most promising (if laborious) ways to recreate—and surpass—human-level ingenuity.


There’s just one problem: our computers can’t handle the massively parallel nature of our brains reports Singularity Hub. Squished within a three-pound organ are over 100 billion interconnected neurons and trillions of synapses.

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.”

An increasingly influential subfield of neuroscience has taken Feynman’s words to heart. To theoretical neuroscientists, the key to understanding how intelligence works is to recreate it inside a computer. Neuron by neuron, these whizzes hope to reconstruct the neural processes that lead to a thought, a memory, or a feeling.

Even the most powerful supercomputers today balk at that scale: so far, machines such as the K computer at the Advanced Institute for Computational Science in Kobe, Japan can tackle at most ten percent of neurons and their synapses in the cortex. This ineptitude is partially due to software. As computational hardware inevitably gets faster, algorithms increasingly become the linchpin towards whole-brain simulation.

This month, an international team completely revamped the structure of a popular simulation algorithm, developing a powerful piece of technology that dramatically slashes computing time and memory use.

 

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Using today’s simulation algorithms, only small progress (dark red area of center brain) would be possible on the next generation of supercomputers. However, the new technology allows researchers to simulate larger parts of the brain while using the same amount of computer memory. This makes the new technology more appropriate for future use in supercomputers for whole-brain level simulation. Image Credit: Forschungszentrum Jülich/Frontiers

The new algorithm is compatible with a range of computing hardware, from laptops to supercomputers. When future exascale supercomputers hit the scene—projected to be 10 to 100 times more powerful than today’s top performers—the algorithm can immediately run on those computing beasts.

“With the 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 at the Jülich.

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