A Johns Hopkins study has shed light on how the brain compresses visual information to understand it.
A team, led by neuroscientists Ed Connor and Kechen Zhang, 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 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.
"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.
The findings have been published in the journal Current Biology.