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The brain is a deviously complex biological computing device that even the fastest supercomputers in the world fail to emulate. Well, that’s not entirely true anymore. Researchers at the Okinawa Institute of Technology Graduate University in Japan and Forschungszentrum Jülich in Germany have managed to simulate a single second of human brain activity in a very, very powerful computer.

This feat of computational might was made possible by the open source simulation software known as NEST. Of course, some serious computing power was needed as well. Luckily, the team had access to the fourth fastest supercomputer in the world — the K computer at the Riken research institute in Kobe, Japan.

Using the NEST software framework, the team led by Markus Diesmann and Abigail Morrison succeeded in creating an artificial neural network of 1.73 billion nerve cells connected by 10.4 trillion synapses. While impressive, this is only a fraction of the neurons every human brain contains. Scientists believe we all carry 80-100 billion nerve cells, or about as many stars as there are in the Milky Way.

Knowing this, it shouldn’t come as a surprise that the researchers were not able to simulate the brain’s activity in real time. It took 40 minutes with the combined muscle of 82,944 processors in K computer to get just 1 second of biological brain processing time. While running, the simulation ate up about 1PB of system memory as each synapse was modeled individually.

The neurons were arranged randomly, and the short time scale makes the practical applications minimal. This project was intended to prove our capacity to model biological systems has reached a critical juncture. Science can finally describe a sufficiently complicated system to model the brain.

Sure, this takes unbelievable mountains of computing resources now, but that’s been the case with every problem computer science has tackled since the days of vacuum tubes. At first, only the fastest computers on Earth could play chess or render 3D graphics, but not anymore.

Computing power will continue to ramp up while transistors scale down, which could make true neural simulations possible in real time with supercomputers. Eventually scientists without access to one of the speediest machines in the world will be able to use cluster computing to accomplish similar feats. Maybe one day a single home computer will be capable of the same thing.

Perhaps all we need for artificial intelligence is a simulation of the brain at least as complex as ours. That raises the question, if you build a brain, does it have a mind? For that matter, what happens if you make a simulated brain MORE complex than the human brain? It may not be something we want to know.

Now read: Computerized brain made of GPUs could be the future of artificial intelligence