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Scientists help explain visual system's remarkable ability to recognize complex objects

John Reynolds, Anirvan Nandy and Tatyana Sharpee

From left: Scientists John Reynolds, Anirvan Nandy and Tatyana Sharpee

How can a human eye figure out twisted and looped letters, like those in the little security tests Internet users are often given on websites? The task is so complex, no one has been able to write computer code that translates these distorted letters the same way that neural networks can. That's why the test is used to distinguish a human response from computer bots that try to steal sensitive information.

Two studies by Tatyana Sharpee and John Reynolds published in Neuron and the Proceedings of the National Academy of Sciences (PNAS), have demonstrated how complex a visual task decoding the test or any image made of simple and intricate elements actually is to the brain. Sharpee and Reynolds sought to figure out how a part of the visual cortex known as area V4 is able to distinguish between different visual stimuli even as the stimuli move around in space.

"Neurons in the visual system are sensitive to regions of space—they are like little windows into the world," says Reynolds. In the earliest stages of processing, these windows, known as receptive fields, are small, with access only to information within a restricted region of space.

Neurons in V4 have a larger receptive field that can also compute more complex shapes, such as contours.

Both studies investigated the ability of a neuron to recognize the same stimulus within its receptive field no matter where it is in space, or where it happens to fall within the receptive field. The researchers found that neurons that respond to more complicated shapes need that complicated curve to be in a more restricted range for them to detect it and understand its meaning. On the other hand, neurons in V4 tuned to recognize simpler shapes don't care where the stimulus they are tuned to is as long as it is within their receptive field.

The results indicate that there is a deeper mystery to be solved, Reynolds says. "What we have done is unpack part of the machinery for achieving integration of parts into wholes."