01-20-2013, 08:31 AM
We all know that computers owe their existence to humans. On the other hand, the study of computers and their development has led to many questions that man would have never get answers if it weren’t for computers.
To understand who we are and what we are, we often the first look for answers around ourselves, so that we can learn to ask questions that will help us to better comprehend ourselves.
This mechanism of indirect learning is happening all the time, and one example of the way by which our brains manage to cope with a large number of visual information received in the course of even a single day, and not to mention the whole life.
How do we actually remember all those images that come to us?
The story of memory and data storage, begins with the invention of computers. When an image is saved to the specific place on hard drive or CD, actually the specific mechanism is activated to optimize and reduce the amount of information needed to preserve a certain image, and more importantly, to allow its faithful reproduction. There are a lot of different algorithms, and all of those were invented by people, and among them are best known and most commonly used formats JPG and PNG. There are also ancient BMP, GIF and giant TIFF, and beside them, a whole gallery of algorithms that are in use today. All these algorithms are still caught between the quality (to preserve the credibility of content - pictures) and the amount of data required to preserve the credibility. Therefore, there is a constant confrontation of the two characteristics: quality and quantity. All these algorithms are solutions to the computer, because without them, sending files would even today took a long time and require us to be very patient when sending and receiving e-mail.
Finally, based on the above, we recognize that our brain has to deal with similar situation, or if you like – similar problem. The cells in the retina, which are susceptible to light, have the ability of capturing the image which is measured in megapixels. The brain, on the other hand, does not have the ability or memory capacity to handle such graphic formats during the life. Therefore, the brain is forced to choose the most vital information and to understand the visual world using that information.
In a recent edition of the Current Biology journal, the research team led by scientists Ed Connor and Zhang Kechen from Johns Hopkins University, describe the following piece of knowledge that will help us to better understand how the brain compresses and stores visual information (any similarity to the computers is no accident).
Researches have shown that there are cells in the brain of primates (meaning not only in humans) that are highly selective for the parts of the image that contain harsh and severe curvature. When we say "curvature" we do not mean just the line but also the entire area that is in a way, very, different from the rest of the picture. Area of the brain where the cells are marked with the "V4" and which is located in the central part of the the brain, is responsible for processing the image. Simply put, in the brain, the information obtained is filtered, where the parameter by which to filter, in fact, is the information about curves that represent the picture. For these cells, straight edges and gentle curves are not interesting at all, but sharp and angular are very well noted.
Keeping this in mind, researchers, and one of the co-authors have developed a computer model of the cells from area V4. The cells were carefully "trained" by thousands of images that show a variety of objects from nature. After viewing a picture, the cell is forced to invoke that particular image back. Computer V4 cells reacted in the opposite way, as for those cells the straight edges and gentle curves were more attractive.
However, the number of artificial cells that were involved in the process of image reconstruction was not limited. The next phase of the experiment sought to substantively reduce the number of cells, with each new scan of the picture. The more number of active cells decreased, the more their selectivity lead to sharp and angular aspect of the image. So, modeled V4 cells were done not bad, they just had a better position than natural. As soon as the conditions were made equal, they both reacted in the same way.
What's so important about these sharp edges?
Sharpness or sharp line is several times rarer in nature than straight line or gentle curve. Using sharpness as a critical element of recognition and reproduction of the object, in visual terms, keeping the image is much more economical. Uniqueness has always attracted more attention than, widely present, ordinariness.
"At this moment, our computers are defeating us in chess and are solveing certain mathematical problems better than we do, but we are still unbeatable when it comes to the ability to differentiate, recognize, understand, and manipulate memory objects that make up our world." underlines Connor.
This advantage is achieved due to the human ability to condense the information to the level of recognition and tracking, rather than storing complete information. From the Computer’s point of view, the human brain is still the best compression algorithm for visual information.
"If I wanted to search for a particular sexy old car or an elegant dinner jacket, wouldn't it be great if I could take a picture of what I wanted, upload it and tell Google to find what's in the picture?" said Garrett Kenyon, a neurophysicist at Los Alamos National Laboratory who is studying the way the brain processes visual information. "If we could help computers understand what underlies the 'aha!' moment when we recognize something, we would be able to communicate better with it about what we were looking for visually. The computer would be able to understand." That would probably become possible as soon as we teach the computer to act in the same manner as the human brain.
To understand who we are and what we are, we often the first look for answers around ourselves, so that we can learn to ask questions that will help us to better comprehend ourselves.
This mechanism of indirect learning is happening all the time, and one example of the way by which our brains manage to cope with a large number of visual information received in the course of even a single day, and not to mention the whole life.
How do we actually remember all those images that come to us?
The story of memory and data storage, begins with the invention of computers. When an image is saved to the specific place on hard drive or CD, actually the specific mechanism is activated to optimize and reduce the amount of information needed to preserve a certain image, and more importantly, to allow its faithful reproduction. There are a lot of different algorithms, and all of those were invented by people, and among them are best known and most commonly used formats JPG and PNG. There are also ancient BMP, GIF and giant TIFF, and beside them, a whole gallery of algorithms that are in use today. All these algorithms are still caught between the quality (to preserve the credibility of content - pictures) and the amount of data required to preserve the credibility. Therefore, there is a constant confrontation of the two characteristics: quality and quantity. All these algorithms are solutions to the computer, because without them, sending files would even today took a long time and require us to be very patient when sending and receiving e-mail.
Finally, based on the above, we recognize that our brain has to deal with similar situation, or if you like – similar problem. The cells in the retina, which are susceptible to light, have the ability of capturing the image which is measured in megapixels. The brain, on the other hand, does not have the ability or memory capacity to handle such graphic formats during the life. Therefore, the brain is forced to choose the most vital information and to understand the visual world using that information.
In a recent edition of the Current Biology journal, the research team led by scientists Ed Connor and Zhang Kechen from Johns Hopkins University, describe the following piece of knowledge that will help us to better understand how the brain compresses and stores visual information (any similarity to the computers is no accident).
Researches have shown that there are cells in the brain of primates (meaning not only in humans) that are highly selective for the parts of the image that contain harsh and severe curvature. When we say "curvature" we do not mean just the line but also the entire area that is in a way, very, different from the rest of the picture. Area of the brain where the cells are marked with the "V4" and which is located in the central part of the the brain, is responsible for processing the image. Simply put, in the brain, the information obtained is filtered, where the parameter by which to filter, in fact, is the information about curves that represent the picture. For these cells, straight edges and gentle curves are not interesting at all, but sharp and angular are very well noted.
Keeping this in mind, researchers, and one of the co-authors have developed a computer model of the cells from area V4. The cells were carefully "trained" by thousands of images that show a variety of objects from nature. After viewing a picture, the cell is forced to invoke that particular image back. Computer V4 cells reacted in the opposite way, as for those cells the straight edges and gentle curves were more attractive.
However, the number of artificial cells that were involved in the process of image reconstruction was not limited. The next phase of the experiment sought to substantively reduce the number of cells, with each new scan of the picture. The more number of active cells decreased, the more their selectivity lead to sharp and angular aspect of the image. So, modeled V4 cells were done not bad, they just had a better position than natural. As soon as the conditions were made equal, they both reacted in the same way.
What's so important about these sharp edges?
Sharpness or sharp line is several times rarer in nature than straight line or gentle curve. Using sharpness as a critical element of recognition and reproduction of the object, in visual terms, keeping the image is much more economical. Uniqueness has always attracted more attention than, widely present, ordinariness.
"At this moment, our computers are defeating us in chess and are solveing certain mathematical problems better than we do, but we are still unbeatable when it comes to the ability to differentiate, recognize, understand, and manipulate memory objects that make up our world." underlines Connor.
This advantage is achieved due to the human ability to condense the information to the level of recognition and tracking, rather than storing complete information. From the Computer’s point of view, the human brain is still the best compression algorithm for visual information.
"If I wanted to search for a particular sexy old car or an elegant dinner jacket, wouldn't it be great if I could take a picture of what I wanted, upload it and tell Google to find what's in the picture?" said Garrett Kenyon, a neurophysicist at Los Alamos National Laboratory who is studying the way the brain processes visual information. "If we could help computers understand what underlies the 'aha!' moment when we recognize something, we would be able to communicate better with it about what we were looking for visually. The computer would be able to understand." That would probably become possible as soon as we teach the computer to act in the same manner as the human brain.
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