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From Student’s Research, a New Way to Decode Brain Signals

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Working with one of her Fordham professors at the intersection of machine learning and neuroscience, Rabia Gondur devised an innovative way to understand how an insect’s brain functions during natural movements.

When you do something simple like pick up your phone or wash your hands, what’s happening in your brain? Quite a lot, actually—neurons are firing everywhere because of all your minor movements, not to mention background activities like respiration.

“Your brain is not just stopping to do this one activity,” said Rabia Gondur, FCLC ’22, a computational research scientist at Cold Spring Harbor Laboratory on Long Island. “It’s very noisy in the brain.”

Cutting through this noise to see which movements fire which neurons is the subject of her research, which she’ll soon present at a prestigious international conference on machine learning.

Gondur devised an innovative approach with help from one of her professors, Stephen Keeley, Ph.D.—a collaboration that began easily during her senior year when his presentation in one of her capstone courses spoke to her interest in research. “I just reached out to him, and he was super accommodating,” she said.

They worked on the research while Gondur—an integrative neuroscience major—completed the requirements for the accelerated master’s degree program in data science at Fordham’s Graduate School of Arts and Sciences, after which she landed her job at Cold Spring, where she is part of a computational neuroscience research group.

She will present her research at one of the world’s leading forums for machine learning, the annual International Conference on Learning Representations, taking place in Vienna, Austria, in May.

Using Machine Learning to Study Day-to-Day Brain Function

Gondur’s research is one of many studies seeking to understand a brain’s response during complex, natural behaviors, building on prior studies of more basic movements—for instance, what happens in a monkey’s brain when it reaches left versus right in response to a prompt.

The eventual goal is to get beyond laboratory studies to see, in detail, how the human brain naturally functions. “That’s ultimately what neuroscientists are interested in understanding, is how the brain works in our day-to-day lives,” said Keeley, an assistant professor of natural science who runs a machine learning lab on the Lincoln Center campus.

But to work toward this goal, scientists have to start small—literally. For their study, Keeley and Gondur examined the brains of insects: a fly grooming itself and a moth flitting around to follow a moving image of a flower. For this, they relied on data that their collaborators at other universities gathered using brain imaging technology.

Keeley and Gondur devised a machine learning algorithm to find links between the bugs’ brain signals and the subtleties of their movements, as captured in video stills. It differs from similar algorithms because they added processes to make the measurements more precise and the results easier to interpret.

A New Tool for Brain Research

Such techniques could one day illuminate everything from brain-based diseases to variances in people’s motor skills, Keeley said. For now, their model gives a new tool to scientists trying to tease out relationships hidden in complex data. “If you are interested in genomics, if you’re interested in medicine, if you’re interested in just anything, you can basically tweak the model,” Gondur said.

Keeley is always working with undergraduates on research projects tailored to their skill level. “Rabia came in with quite a good amount of talent, and so I was able to give her a very challenging project, and she was very successful,” he said.

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