MIT researchers and their colleagues 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—say, 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," says Tomaso Poggio, a professor of brain and
cognitive sciences at MIT and director of the Center for Brains, Minds, and
Machines (CBMM), a multi-institution research consortium funded by the National
Science Foundation and headquartered at MIT. "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.
Poggio, who is also a primary
investigator at MIT's McGovern Institute for Brain Research, is the senior
author on a paper describing the new work, which appeared today in the
journal Computational Biology.
He's joined on the paper by several
other members of both the CBMM and the McGovern Institute: first author Joel
Leibo, a researcher at Google DeepMind, who earned his PhD in brain and
cognitive sciences from MIT with Poggio as
his advisor; Qianli Liao, an MIT graduate student in electrical engineering and
computer science; Fabio Anselmi, a postdoc in the IIT@MIT Laboratory for
Computational and Statistical Learning, a joint venture of MIT and the Italian
Institute of Technology; and Winrich Freiwald, an associate professor at the
Rockefeller University.
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