The brain put to the test by supercomputers | Festina Lente - Your leading source of AI news | Turtles AI

Simulating a human brain with a supercomputer means reproducing the activity of networks of neurons and synapses on the computer, trying to capture the biological complexity of the brain to explore how it operates, from neurological circuits to possible pathology.

Key Points:

• Modeling networks of neurons and synapses requires enormous computing power.
• Current simulations reproduce parts of animal brains, not the entire human brain.
• Neuromorphic technologies seek to biologically mimic brain processes.
• Pan-European digital infrastructures bring together data and tools for simulations.

Think of an ocean of electrical signals: billions of waves that intersect and communicate in less than the blink of an eye. Simulating a human brain on a supercomputer is a bit like trying to capture that ocean in tidy binary data, transforming biological impulses into lines of code and digital connections. The most powerful supercomputers available today are pushing this frontier forward, moving from simplified models to increasingly detailed representations of neural networks. Recent projects have already made it possible to create simulations of the mouse cerebral cortex with almost ten million neurons and tens of billions of synapses, using machines capable of carrying out quadrillions of calculations per second to see models of the animal brain "in action" and observe phenomena such as the propagation of damage or the pattern of brain waves.

But the leap from mouse to human is enormous: a human brain contains orders of magnitude more neurons (about 100 billion) and many more connections, and simulating them all in real time still exceeds the capabilities of current computer systems. Even with the most advanced supercomputers, computation remains so vast that often only a fraction of a second of brain activity can be simulated, or patterns reproduced at a higher level of abstraction than actual biology.

To try to get closer to this complexity, scientists are also developing hardware and software inspired by the structure of the brain itself, called neuromorphic: computing architectures that imitate not only neural logic, but also the way neurons communicate via electrical spikes, so as to make simulations more efficient and less energy-intensive. Systems of this type, like some machines currently being activated, already boast the ability to process trillions of synaptic operations per second, approaching in scale what happens in biological gray matter.

At the same time, international collaborative initiatives and open digital infrastructures are gathering data and tools to advance brain simulation, allowing researchers to grapple with complex neural network models and manipulate connections in virtual environments in ways that traditional cell phones or laboratories would not allow.

Ultimately, the idea of ​​“simulating the human brain” is like trying to translate the poem of a consciousness into a digital language: a feat of precision that each step forward makes clearer and more fascinating, without ever exhausting the depth of what remains to be understood.