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Primeur weekly 2016-12-05

Exascale supercomputing

Hewlett Packard Enterprise demonstrates world's first Memory-Driven Computing architecture ...

Crowd computing

Einstein@home discovers new gamma-ray pulsar ...

Computing tackles the mystery of the dark universe ...

Quantum computing

Construction of practical quantum computers radically simplified ...

Researchers take first look into the 'eye' of Majoranas ...

More reliable way to produce single photons for quantum information imprinting ...

Focus on Europe

Deadline for ISC research paper submission extended to December 16 ...

PRACE to hold awards ceremony at Cineca for Summer of HPC 2016 ...

An open harbour for research data ...

New MareNostrum4 supercomputer to be 12 times more powerful than MareNostrum3 ...

Hardware

Lincoln Laboratory's supercomputing system ranked most powerful in New England ...

PolyU and Huawei jointly set up the first lab in optical communication and advanced computing system in Hong Kong ...

DDN Infinite Memory Engine burst buffer exceeds 1 TB per second file system performance for Japan's fastest supercomputer ...

Fujitsu develops in-memory deduplication technology to accelerate response for large-scale storage ...

Fujitsu announces start of operations for Japan's fastest supercomputer ...

Applications

Using supercomputers to illuminate the renaissance ...

IBM unveils Watson-powered imaging solutions for health care providers ...

New algorithm could explain human face recognition ...

Researchers use Stampede supercomputer to study new chemical sensing methods, desalination and bacterial energy production ...

Physicists spell 'AV' by manipulating Abrikosov vortices ...

BSC researchers to study the response of European climate to Arctic sea ice depletion ...

IBM and Pfizer to accelerate immuno-oncology research with Watson for Drug Discovery ...

Fujitsu offers deep learning platform with world-class speed and AI services that support industries and operations ...

The Cloud

Cloud Systems Management Software Market: Global Industry Analysis and Opportunity Assessment 2016-2026 ...

Juniper Networks simplifies Cloud transition for enterprises with carrier-grade routing and unified security for AWS marketplace ...

New algorithm could explain human face recognition

1 Dec 2016 Cambridge - 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 centre - 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", stated 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 Tomaso Poggio calls a "biologically plausible" model of the nervous system, will inevitably yield intermediary representations that are indifferent to angle of rotation.

Tomaso 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 journalComputational 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 Tomaso 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.

The new paper is "a nice illustration of what we want to do in CBMM, which is this integration of machine learning and computer science on one hand, neurophysiology on the other, and aspects of human behaviour", Tomaso Poggio stated. "That means not only what algorithms does the brain use, but what are the circuits in the brain that implement these algorithms."

Tomaso Poggio has long believed that the brain must produce "invariant" representations of faces and other objects, meaning representations that are indifferent to objects' orientation in space, their distance from the viewer, or their location in the visual field. Magnetic resonance scans of human and monkey brains suggested as much, but in 2010, Freiwald published a study describing the neuroanatomy of macaque monkeys' face-recognition mechanism in much greater detail.

Winrich Freiwald showed that information from the monkey's optic nerves passes through a series of brain locations, each of which is less sensitive to face orientation than the last. Neurons in the first region fire only in response to particular face orientations; neurons in the final region fire regardless of the face's orientation - an invariant representation.

But neurons in an intermediate region appear to be "mirror symmetric": That is, they're sensitive to the angle of face rotation without respect to direction. In the first region, one cluster of neurons will fire if a face is rotated 45 degrees to the left, and a different cluster will fire if it's rotated 45 degrees to the right. In the final region, the same cluster of neurons will fire whether the face is rotated 30 degrees, 45 degrees, 90 degrees, or anywhere in-between. But in the intermediate region, a particular cluster of neurons will fire if the face is rotated by 45 degrees in either direction, another if it's rotated 30 degrees, and so on.

This is the behavior that the researchers' machine-learning system reproduced. "It was not a model that was trying to explain mirror symmetry", Tomaso Poggio stated. "This model was trying to explain invariance, and in the process, there is this other property that pops out."

The researchers' machine-learning system is a neural network, so called because it roughly approximates the architecture of the human brain. A neural network consists of very simple processing units, arranged into layers, that are densely connected to the processing units - or nodes - in the layers above and below. Data are fed into the bottom layer of the network, which processes them in some way and feeds them to the next layer, and so on. During training, the output of the top layer is correlated with some classification criterion - say, correctly determining whether a given image depicts a particular person.

In earlier work, Tomaso Poggio's group had trained neural networks to produce invariant representations by, essentially, memorizing a representative set of orientations for just a handful of faces, which Tomaso Poggio calls "templates". When the network was presented with a new face, it would measure its difference from these templates. That difference would be smallest for the templates whose orientations were the same as that of the new face, and the output of their associated nodes would end up dominating the information signal by the time it reached the top layer. The measured difference between the new face and the stored faces gives the new face a kind of identifying signature.

In experiments, this approach produced invariant representations: A face's signature turned out to be roughly the same no matter its orientation. But the mechanism - memorizing templates - was not, Tomaso Poggio said, biologically plausible.

So instead, the new network uses a variation on Hebb's rule, which is often described in the neurological literature as "neurons that fire together wire together". That means that during training, as the weights of the connections between nodes are being adjusted to produce more accurate outputs, nodes that react in concert to particular stimuli end up contributing more to the final output than nodes that react independently - or not at all.

This approach, too, ended up yielding invariant representations. But the middle layers of the network also duplicated the mirror-symmetric responses of the intermediate visual-processing regions of the primate brain.

Source: Massachusetts Institute of Technology - MIT

Back to Table of contents

Primeur weekly 2016-12-05

Exascale supercomputing

Hewlett Packard Enterprise demonstrates world's first Memory-Driven Computing architecture ...

Crowd computing

Einstein@home discovers new gamma-ray pulsar ...

Computing tackles the mystery of the dark universe ...

Quantum computing

Construction of practical quantum computers radically simplified ...

Researchers take first look into the 'eye' of Majoranas ...

More reliable way to produce single photons for quantum information imprinting ...

Focus on Europe

Deadline for ISC research paper submission extended to December 16 ...

PRACE to hold awards ceremony at Cineca for Summer of HPC 2016 ...

An open harbour for research data ...

New MareNostrum4 supercomputer to be 12 times more powerful than MareNostrum3 ...

Hardware

Lincoln Laboratory's supercomputing system ranked most powerful in New England ...

PolyU and Huawei jointly set up the first lab in optical communication and advanced computing system in Hong Kong ...

DDN Infinite Memory Engine burst buffer exceeds 1 TB per second file system performance for Japan's fastest supercomputer ...

Fujitsu develops in-memory deduplication technology to accelerate response for large-scale storage ...

Fujitsu announces start of operations for Japan's fastest supercomputer ...

Applications

Using supercomputers to illuminate the renaissance ...

IBM unveils Watson-powered imaging solutions for health care providers ...

New algorithm could explain human face recognition ...

Researchers use Stampede supercomputer to study new chemical sensing methods, desalination and bacterial energy production ...

Physicists spell 'AV' by manipulating Abrikosov vortices ...

BSC researchers to study the response of European climate to Arctic sea ice depletion ...

IBM and Pfizer to accelerate immuno-oncology research with Watson for Drug Discovery ...

Fujitsu offers deep learning platform with world-class speed and AI services that support industries and operations ...

The Cloud

Cloud Systems Management Software Market: Global Industry Analysis and Opportunity Assessment 2016-2026 ...

Juniper Networks simplifies Cloud transition for enterprises with carrier-grade routing and unified security for AWS marketplace ...