Introducing the brain: temporal cortex interprets objects (Introduction)

by David Turell , Friday, June 05, 2020, 19:37 (1420 days ago) @ David Turell

Specific areas carefully designed for specialized recognitions:

https://medicalxpress.com/news/2020-06-mathematical-brain-visual.html

"The brain's inferotemporal (IT) cortex is a critical center for the recognition of objects. Different regions or "patches" within the IT cortex encode for the recognition of different things. In 2003, Tsao and her collaborators discovered that there are six face patches; there are also patches that encode for bodies, scenes, and colors. But these well-studied islands only make up some of IT cortex, and the functions of the brain cells located in between them have not been well understood.

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"Working with nonhuman primates, Bao first stimulated a region of IT cortex that did not belong to any of the previously defined patches and measured how other parts of IT responded to stimulation using functional magnetic resonance imaging (fMRI). In doing so, he discovered a new network: three regions of the IT cortex that were driven by the stimulation. He called this network the "no man's land network," since it belonged to an uncharted region of IT cortex.

"To determine what kind of objects the new network responded to, Bao showed the primates images of thousands of different objects while he measured neurons' activity in the new network. He found that the neurons responded strongly to a group of objects that seemingly had nothing in common, except for one curious feature: they all contained thin "protrusions." That is, spiky objects such as spiders, helicopters, and chairs triggered the activity of the cells of the new network. Round, smooth objects like faces triggered almost no activity in this network.

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"Astonishingly, he found a network of cortical regions that did respond only to stubby objects, as predicted by the model. This means the deep network had successfully predicted the existence of a previously unknown set of brain regions.

"Why was each quadrant represented by a network of multiple regions? Earlier, Tsao's lab had found that different face patches throughout IT cortex encode an increasingly abstract representation of faces. Bao found that the two networks he had discovered showed this same property: cells in more anterior regions of the brain responded to objects across different angles, while cells in more posterior regions responded to objects only at specific angles. This shows that the temporal lobe contains multiple copies of the map of object space, each more abstract than the preceding.

"Finally, the team was curious how complete the map was. They measured the brain activity from each of the four networks comprising the map as the primates viewed images of objects and then decoded the brain signals to determine what the primates had been looking at. The model was able to accurately reconstruct the images viewed by the primates.

"'We now know which features are important for object recognition," says Bao. "The similarity between the important features observed in both biological visual systems and deep networks suggests the two systems might share a similar computational mechanism for object recognition. Indeed, this is the first time, to my knowledge, that a deep network has made a prediction about a feature of the brain that was not known before and turned out to be true. I think we are very close to figuring out the how the primate brain solves the object recognition problem.'"

Comment: How did the brain learn to organize itself in this way, changing electrical signals into recognizable images? Not by chance.