Our Brains Are Sophisticated Computers -
They Have Remarkable Ability To Recognize Complex Objects

3 July, 2013

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Follow us: - How is it possible for a human eye to figure out letters that are twisted and looped in crazy directions, like those in the little security test internet users are often given on websites?

It seems easy to us - the human brain just does it, but this task is far from simple.

As a matter of fact, the task is so complex that no one has been able to write computer code that translates these distorted letters the same way that neural networks can.

There is a help in a type of challenge-response test - CAPTCHA ("Completely Automated Public Turing test to tell Computers and Humans Apart") - used in computing to determine whether or not the user is human.

Now, a team of neuroscientists at the Salk Institute for Biological Studies has explored how the brain a ccomplishes this remarkable task.

The research-involving recordings from hundreds of neurons-may also have future clinical and practical implications, says the study's senior co-authors, Salk neuroscientists Tatyana Sharpee and John Reynolds.

"Understanding how the brain creates a visual image can help humans whose brains are malfunctioning in various different ways-such as people who have lost the ability to see," says Sharpee, an associate professor in the Computational Neurobiology Laboratory.

"One way of solving that problem is to figure out how the brain-not the eye, but the cortex-processes information about the world. If you have that code then you can directly stimulate neurons in the cortex and allow people to see."

According to Reynolds, a professor in the Systems Neurobiology Laboratory, an indirect benefit of understanding the way the brain works is the possibility of building computer systems that can act like humans.

"The reason that machines are limited in their capacity to recognize things in the world around us is that we don't really understand how the brain does it as well as it does," he says.

But the mystery isn't solved yet and many long-term goals must be achieved.

"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. They only have access to information within a restricted region of space. Each of these neurons sends brain signals that encode the contents of a little region of space-they respond to tiny, simple elements of an object such as edge oriented in space, or a little patch of color."

Both new studies investigated the issue of translation invariance-the ability of a neuron to recognize the same stimulus within its receptive field no matter where it is in space, where it happens to fall within the receptive field.

"The accepted understanding is that individuals neurons are tuned to recognize the same stimulus no matter where it was in their receptive field," says Sharpee.

For example, a neuron might respond to a bit of the curve in the number 5 in a CAPTCHA image, no matter how the 5 is situated within its receptive field.

Researchers believed that neuronal translation invariance-the ability to recognize any stimulus, no matter where it is in space-increases as an image moves up through the visual processing hierarchy.

"But what both studies show is that there is more to the story," she says. "There is a trade off between the complexity of the stimulus and the degree to which the cell can recognize it as it moves from place to place."

Previous studies of object recognition were based on assumption that neuronal responses at later stages in visual processing remain the same regardless of basic visual transformations to the object's image.

"It is important that results from the two studies are quite compatible with one another, that what we find studying just lines and curves in one first experiment matches what we see when the brain experiences the real world," says Sharpee, who is well known for developing a computational method to extract neural responses from natural images.

"What this tells us is that there is a deeper mystery here to be solved," Reynolds says.

"We have not figured out how translation invariance is achieved. What we have done is unpacked part of the machinery for achieving integration of parts into wholes."

The findings of the studies are published June 19, 2013, in Neuron and June 24, 2013, in the Proceedings of the National Academy of Sciences (PNAS).

See also:

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