In a recent research published in Computational Biology, scientists have developed a new computational model of the human brain's face-recognition mechanism that seems to capture aspects of human neurology that previous models have missed.
The researchers designed a machine-learning system that implemented their model and they trained it to recognize particular faces by feeding it a battery of sample images.
They found that the trained system included an intermediate processing step that represented a face's degree of rotation ie 45 degrees from center -- but not the direction -- left or right.
This property wasn't built into the system; it emerged spontaneously from the training process. But it duplicates an experimentally observed feature of the primate face-processing mechanism.
The researchers consider this an indication that their system and the brain are doing something similar.
"This is not a proof that we understand what's going on," said lead researcher Tomaso Poggio.
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Adding, "Models are kind of cartoons of reality, especially in biology. So I would be surprised if things turn out to be this simple. But I think it's strong evidence that we are on the right track."
Indeed, the researchers' new paper includes a mathematical proof that the particular type of machine-learning system they use, which was intended to offer what Poggio calls a 'biologically plausible' model of the nervous system, will inevitably yield intermediary representations that are indifferent to angle of rotation.
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