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From The Next Web:
Turns out, brain models don't have to be so complicated
A team of physicists at Emory University recently published research indicating they’d successfully managed to reduce a mouse’s brain activity to a simple predictive model. This could be a breakthrough for artificial neural networks. You know: robot brains.
Let there be mice: Scientists can do miraculous things with mice such as grow a human ear on one’s back or control one via computer mouse. But this is the first time we’ve heard of researchers using machine learning techniques to grow a theoretical mouse brain.
Per a press release from Emory University:
The dynamics of the neural activity of a mouse brain behave in a peculiar, unexpected way that can be theoretically modeled without any fine tuning.
In other words: We can observe a mouse’s brain activity in real-time, but there are simply too many neuronal interactions for us to measure and quantify each and every one – even with AI. So the scientists are using the equivalent of a math trick to make things simpler.
How’s it work? The research is based on a theory of criticality in neural networks. Basically, all the neurons in your brain exist in an equilibrium between chaos and order. They don’t all do the same thing, but they also aren’t bouncing around randomly....
....MUCH MORE (the applications)
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