The brain is faced with a similar problem. The images captured by light-sensitive cells in the retina are on the order of a megapixel. The brain does not have the transmission or memory capacity to deal with a lifetime of megapixel images. Instead, the brain must select out only the most vital information for understanding the visual world.
In today's online issue of Current Biology, a Johns Hopkins team led by neuroscientists Ed Connor and Kechen Zhang describes what appears to be the next step in understanding how the brain compresses visual information down to the essentials.
They found that cells in area "V4," a midlevel stage in the primate brain's object vision pathway, are highly selective for image regions containing acute curvature. Experiments by doctoral student Eric Carlson showed that V4 cells are very responsive to sharply curved or angled edges, and much less responsive to flat edges or shallow curves.
To understand how selectivity for acute curvature might help with compression of visual information, co-author Russell Rasquinha (now at University of Toronto) created a computer model of hundreds of V4-like cells, training them on thousands of natural object images. After training, each image evoked responses from a large proportion of the virtual V4 cells -- the opposite of a compressed format. And, somewhat surprisingly, these virtual V4 cells responded mostly to flat edges and shallow curvatures, just the opposite of what was observed for real V4 cells.
The results were quite different when the model was trained to limit the number of virtual V4 cells responding to each image. As this limit on responsive cells was tightened, the selectivity of the cells shifted from shallow to acute curvature. The tightest limit produced an eight-fold decrease in the number of cells responding to each image, comparable to the file size reduction achieved by compressing photographs into the .jpeg format. At this level, the computer model produced the same strong bias toward high curvature observed in the real V4 cells.
Why would focusing on acute curvature regions produce such savings? Because, as the group's analyses showed, high-curvature regions are relatively rare in natural objects, compared to flat and shallow curvature. Responding to rare features rather than common features is automatically economical.
Despite the fact that they are relatively rare, high-curvature regions are very useful for distinguishing and recognizing objects, said Connor, a professor in the Solomon H. Snyder Department of Neuroscience in the School of Medicine, and director of the Zanvyl Krieger Mind/Brain Institute.
"Psychological experiments have shown that subjects can still recognize line drawings of objects when flat edges are erased. But erasing angles and other regions of high curvature makes recognition difficult," he explained
Brain mechanisms such as the V4 coding scheme described by Connor and colleagues help explain why we are all visual geniuses.
"Computers can beat us at math and chess," said Connor, "but they can't match our ability to distinguish, recognize, understand, remember, and manipulate the objects that make up our world." This core human ability depends in part on condensing visual information to a tractable level. For now, at least, the .brain format seems to be the best compression algorithm around.
To learn more about the Mind/Brain Institute, go here: http://krieger.jhu.edu/mbi/.
Lisa DeNike | EurekAlert!
The dense vessel network regulates formation of thrombocytes in the bone marrow
25.07.2017 | Rudolf-Virchow-Zentrum für Experimentelle Biomedizin der Universität Würzburg
Fungi that evolved to eat wood offer new biomass conversion tool
25.07.2017 | University of Massachusetts at Amherst
Strong light-matter coupling in these semiconducting tubes may hold the key to electrically pumped lasers
Light-matter quasi-particles can be generated electrically in semiconducting carbon nanotubes. Material scientists and physicists from Heidelberg University...
Fraunhofer IPA has developed a proximity sensor made from silicone and carbon nanotubes (CNT) which detects objects and determines their position. The materials and printing process used mean that the sensor is extremely flexible, economical and can be used for large surfaces. Industry and research partners can use and further develop this innovation straight away.
At first glance, the proximity sensor appears to be nothing special: a thin, elastic layer of silicone onto which black square surfaces are printed, but these...
3-D shape acquisition using water displacement as the shape sensor for the reconstruction of complex objects
A global team of computer scientists and engineers have developed an innovative technique that more completely reconstructs challenging 3D objects. An ancient...
Physicists have developed a new technique that uses electrical voltages to control the electron spin on a chip. The newly-developed method provides protection from spin decay, meaning that the contained information can be maintained and transmitted over comparatively large distances, as has been demonstrated by a team from the University of Basel’s Department of Physics and the Swiss Nanoscience Institute. The results have been published in Physical Review X.
For several years, researchers have been trying to use the spin of an electron to store and transmit information. The spin of each electron is always coupled...
What is the mass of a proton? Scientists from Germany and Japan successfully did an important step towards the most exact knowledge of this fundamental constant. By means of precision measurements on a single proton, they could improve the precision by a factor of three and also correct the existing value.
To determine the mass of a single proton still more accurate – a group of physicists led by Klaus Blaum and Sven Sturm of the Max Planck Institute for Nuclear...
21.07.2017 | Event News
19.07.2017 | Event News
12.07.2017 | Event News
25.07.2017 | Physics and Astronomy
25.07.2017 | Earth Sciences
25.07.2017 | Life Sciences