But in order for this training brand to solve the problem of debt distribution without hitting a “pause” in creating sensations, their model requires another important piece. The Naud and Richards team suggested that neurons have different chambers up and down that process neural code in a variety of ways.
“[Our model] it shows that you can have two signs, one going up and the other down, and it can cross, ”said Naud.
To this end, their model suggests that tree-like branches that receive inputs to the surface of neurons simply respond to sound-induced signals – to change their connections and reduce errors. The preparation takes place from top to bottom, just as it does in the back, because in their model, the neurons at the top control the chance that the neurons below them send out the explosion. Researchers have shown that when networks are more volatile, neurons tend to increase their connectivity strength, while the power of connectivity decreases while the explosive signatures are smaller. The idea is that the explosive signal tells the neurons that they must be active during the operation, strengthening their connections, if doing so minimize the fault. Explosions tell neurons that they should not work and may need to weaken their connections.
At the same time, the lower bouts featured two cutaways, for easier access to the higher frets.
“Looking back, the idea presented seems plausible, and I think it speaks to its beauty,” he said. John Sacrament, a neuroscientist at the University of Zurich and ETH Zurich. “I think that’s very good.”
Others have tried to apply the same principle in the past. Twenty years ago, Konrad Kording at the University of Pennsylvania and Peter King Osnabrück University in Germany provided a learning framework consisting of two-dimensional neurons. But their ideas did not contain much of the new model that is relevant to life, and it was just an idea – they could not prove that it could solve the debt problem.
“In the past, we didn’t have the ability to test these assumptions,” Kording said. He sees the new paper as “a great work” and will follow it in his lab.
With the power of modern computational power, Naud, Richards, and colleagues have closely followed their example, with exploding neurons playing a part in the command of learning. They showed that it solves the problem of the credit segment in the old XOR function, which requires learning to respond when one of the two inputs (but not both) and 1. algorithm for graphical problems. But there is room for improvement, since the backpropagation algorithms were very accurate, and did not correspond to human potential.
“There has to be a lot that we don’t have, and we have to create a good image,” Naud said. “The main purpose of this paper is to state that mechanical training can be compared to the way the body works.”