Researchers are preparing to simulate the human brain in a supercomputer

Thanks to significant progress made with some of the world's most powerful supercomputers, scientists at the Jülich Research Center in Germany are setting an ambitious goal: a full-scale simulation of the human brain, reports futurism.com.

In 2024, researchers completed a map of the circuits in the brain of a fruit fly for the first time. Despite its tiny size, this organ crams around 150 meters of “wiring” and 54.5 million synapses into a volume the size of a grain of sand—an amazing feat of computational neuroscience research that allows scientists to better understand how signals travel through the brain.

But the challenges for such a simulation of the human brain are of a completely different scope. Previous attempts a decade ago, such as the Human Brain Project, have largely failed despite considerable government funding. As New Scientist magazine reports, the Jülich researchers still believe they can take things further. The idea is to bring together multiple models of smaller brain regions and use a supercomputer to run simulations of the billions of neurons in action.

The team, led by neurophysics professor Markus Diesmann from Jülich, will use the Joint Undertaking Pioneer for Innovative and Transformative Exascale Research (JUPITER) supercomputer to carry out the simulation.

JUPITER is currently the fourth most powerful supercomputer in the world, according to the TOP500 reference ranking, and has thousands of graphics processing units.

Last month, the team demonstrated that a spiking neural network can be scaled up and run on JUPITER, effectively reaching the 20 billion neurons and 100 trillion connections of the cerebral cortex.

Diesmann told New Scientist that once up and running, the simulation could represent a major leap forward in understanding how the brain works compared to previous, much smaller simulations.

“We now know that large networks can do qualitatively different things than small ones,” says the scientist, noting that “it's clear that large networks are different.”

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