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Computers see as well as primates

31.12.2014

For decades, neuroscientists have been trying to develop computer networks that could mimic the visual skills that the human brain does very quickly and accurately, such as recognizing objects.

So far, no computer model has been able to match the primate brain in terms of visual recognition of objects in a short glance. However, a new study from MIT scientists shows that the latest generation of so-called "deep" neural networks are well matched to the capabilities of the primate brain.

Scientists began building neural networks in the 1970s in hopes of mimicking the brain's ability to process visual information, recognize speech, and understand language. Neural networks are based on the hierarchical principle of representation of visual information in the brain: from the retina to the primary visual cortex, and then to the inferior temporal cortex, at each level, detailing up to complete identification. To mimic this process, scientists create multiple layers of computation in their neural network models. Each level performs a certain mathematical operation, and at each level, representations of a visual object become more and more complex, and unnecessary information, such as the location of the object or its movement, is discarded.

In the current study, scientists measured the brain's ability to recognize objects for the first time by implanting electrodes in the primate cerebral cortex, and then compared the results with the results of deep neural network calculations. The results showed that neural networks have reached the level of visual information processing corresponding to the primate brain.

Now scientists are going to improve neural networks, giving them the ability to track the movement of objects and recognize three-dimensional shapes.

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Trap of eternal desires 10.08.2022

Research shows that people are constantly trying to get more and more things, such as a new smartphone, or clothes and shoes, although this does not always bring them happiness. American scientists have found out what explains such a desire for a large number of new material goods.

"People constantly strive to be happy and often this happiness lies in material things. A person constantly needs more and more. But many people cannot achieve happiness in their imagination and this causes depression. Therefore, people fall into the trap of "eternal desires" when for to find happiness, they need more and more things. But as studies show, this does not always lead to the desired result and people become, on the contrary, unhappy. Our study explains why we continue this eternal struggle, although it negatively affects us, "says Rahit Dubey from Princeton University, USA.

Scientists believe that our brain is programmed to have more and more material goods. This is due to the fact that a person is constantly worried about the difference between what he has and what he wants to have. Also, a person in search of happiness often focuses on his past experience. For example, you traveled last year and had a good time, and you expect a similar experience to repeat. Also, scientists have come to the conclusion that we are constantly striving for more because of getting used to a good life, and we are also influenced by different social standards.

Scientists have created computer models that mimic the way the human brain works, which can match different situations with different actions. Research has shown that even getting what you want doesn't really make you happy. We constantly compare ourselves with other people and strive to meet certain standards, as well as trying to repeat past positive experiences.

On the other hand, as scientists say, if a person is not satisfied with what he has, he tries to improve his situation. But it's almost an eternal struggle.

"Our research also shows that in the pursuit of happiness, we often devalue what we already have, and this leads to depression and overconsumption, that is, the acquisition of more and more material things," Dubey says.

But scientists say they need to be careful about the results of their work, because a computer model may not fully correspond to human behavior.

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