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A few days ago, several scientists at Stanford University in California studied and invented a new type of circuit board that helps people understand the true power of their own brains. This neural grid is modeled on the human brain and can simulate a million nerve cells and billions of synapses, far beyond other brain-like devices.
On April 27th, according to foreign media reports, several scientists in the laboratory of Stanford University in California recently researched and invented a new type of circuit board that can help people understand the true power of their own brains. This neural grid is modeled on the human brain and can simulate a million nerve cells and billions of synapses, far beyond other brain-like devices.
Researchers at Stanford University are currently working on the BRAIN project to develop new tools for measuring brain activity. The size of the neural grid is comparable to that of the iPad, and it has 16 special "neural core" chips that are more efficient and more 9000 times faster than regular computers.
Kwabena Boahen, an associate professor of bioengineering at Stanford University, said: "From a purely energy perspective, the brain is very difficult to match." But Boahen and his bioengineers hope that the project will pave the way for advances in robotics and computing. And find new ways to understand how the brain works.
Only individuals who understand the human brain can program neural grids, but Boahen plans to create a "neural compiler" that can be used by scientists who don't have neurobiological knowledge. Researchers at Stanford University are now collaborating to develop a prosthesis controlled by a chip similar to a neural grid that decodes brain signals directly into motion.
Boahen said that the neural grid is the most cost-effective tool ever developed, but each loop circuit still costs about $400, and researchers are now looking for ways to reduce costs. In any case, research that mimics the human brain still has a long way to go. Boahen said: "The human brain has 80,000 times more neurons than the neural grid, but consumes only three times as much energy. Reaching this level of energy efficiency is the ultimate challenge."
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