Sandia to Referee IARPA Visual Pattern Recognition Project

By John Russell

November 3, 2016

Much has been rightly made of the human brain’s ability to discern patterns from the seemingly visual gobbledygook of the world around us. Indeed, many facial recognition efforts have achieved a fair level of success. Now, Sandia National Laboratories will referee a five-year IARPA (Intelligence Advanced Research Projects Activity) project – Machine Intelligence from Cortical Networks (MICrONS) – seeking to mathematically recreate visual processing capabilities of the brain.

Three teams will map the complex circuitry in the visual cortex, where the brain makes sense of visual input from the eyes. By understanding how our brains see patterns and classify objects, researchers hope to improve how computer algorithms do the same. Such advances could improve how applications help find patterns in huge data sets and even lead to national security and intelligence applications.

The three university teams involved in the challenge include Carnegie Mellon University and the Wyss Institute for Biologically Inspired Engineering at Harvard University; Harvard University; and Baylor College of Medicine, the Allen Institute for Brain Science and Princeton University. The MICrONS project, launched earlier this year, is President Barack Obama’s BRAIN Initiative. The teams each will use different techniques to map the network of interconnecting neurons of the visual cortex and turn those network maps into new models of how the brain works. From the models they hope to create vastly superior computer algorithms for object recognition.

Computational neuroscientist Frances Chance, far left, Sandia National Laboratories primary investigator for the Machine Intelligence from Cortical Networks (MICrONS) program, examines a sample computer-generated object with her team. From left to right, they include Tim Shead, Kim Pfeiffer, Warren Davis, Craig Vineyard and Brad Aimone. (Photo by Randy Montoya)
Computational neuroscientist Frances Chance, far left, Sandia National Laboratories primary investigator for the Machine Intelligence from Cortical Networks (MICrONS) program, examines a sample computer-generated object with her team. From left to right, they include Tim Shead, Kim Pfeiffer, Warren Davis, Craig Vineyard and Brad Aimone. (Photo by Randy Montoya)

An account of the project is posted on the Sandia website (Sandia to evaluate if computational neuroscientists are on track). Researchers led by Frances Chance will test the resulting algorithms and evaluate whether they are on the right path. “Research in neuroscience is a new area for Sandia, with potential to contribute to new scientific discoveries and mission needs in signal processing, high-consequence decision making, low-power high-performance computing and auto-associative memory,” said John Wagner, cognitive sciences manager at Sandia.

“What is nice about the way the MICrONS program has been structured is that it is a beginning-to-end path. It goes from very basic neuroscience — anatomy at the nanometer and micrometer level — moving up into functional behavior of neural circuits in vivo, to theoretical models of how the brain works, and then machine learning models to do real-world applications. It’s the ideal of what brain-inspired algorithms should be, but it’s rarely done like this,” said Brad Aimone, a computational neuroscientist, whose group will evaluate whether the algorithms can sort images of computer-generated objects in the same manner as humans, termed similarity discrimination.

“MICrONS is like the Hubble telescope. We’re building better tools to see things that we were unable to see before and we’re trying to come up with theories to explain what we’ve observed. Just like Hubble didn’t explain everything about the universe, MICrONS isn’t going to solve the brain. The hope is it will tell us something that will make our models better so we could use them to do interesting things,” said Aimone.

Link to full Sandia article: https://share.sandia.gov/news/resources/news_releases/microns_machine_learning/#.WBojHYXJg7A

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