The answer, as it happens, has roots in an abstruse and controversial idea in theoretical physics: criticality. For one of the first times, the team observed an abstract “pull” that lures neural networks back into an optimal functional state, so they never stray far from their dedicated “set points” determined by evolution. Even more mind-blowing? That attractive force somehow emerges from a hidden universe of physical laws buried inside the architecture of entire neural networks, without any single neuron dictating its course.
“It’s an elegant idea: that the brain can tune an emergent property to a point neatly predicted by the physicists,” said lead author Dr. Keith Hengen.
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