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MU Engineering hosts NIH-backed computational neuroscience course

A group stands in front of the Columns on the Quad.

Attendees included Ph.D. students, post-docs, faculty members and more from institutions such as Yale, Stanford, Cornell, Virginia and many, many more. Photo by Amy Parris.

In June, the College of Engineering played host to the third edition of its National Institutes of Health Brain Initiative Summer Course on Models and Neurobiology, attracting 24 attendees from across the country for a two-week, intensive course on utilizing computer modeling in neurobiology research.

Attendees included Ph.D. students, post-docs, faculty members and more from institutions such as Yale, Stanford, Cornell, Virginia and many, many more.

“It’s good to define what is computational neurobiology. Broadly, it’s basically treating the brain like an information processing device and trying to learn how it integrates all the information from the environment and kind of pieces it together to build interpretations,” Electrical Engineering and Computer Science graduate student and event organizer Ben Latimer said.

“We basically talk about the principles of computational neurobiology, do some software tutorials and teach them how to use some of the tools we use in our research.”

The course was founded by EECS Professor Satish Nair and colleagues David Schulz and Andy McClellan of Biology and David Bergin of Education, and the goal of the course is as follows: “Integrative and interdisciplinary training in neuroscience is necessary to help develop scientists who can work together to address this goal by using approaches from diverse fields including biology, psychology, computer science, electrical engineering, and physics. Our training course is designed to introduce and strengthen the quantitative skills of researchers with biological backgrounds and increase the knowledge of neuroscience concepts for those from quantitative backgrounds.”

Latimer explained that the importance of computational neurobiology is to allow researchers to use software to come up with hypotheses related to the brain and be able to test them before they have to work on the brains of animals or humans. It allows researchers to play around with different ideas before narrowing their focus and beginning work on actual, physical brains.

Participants in the course, according to post-course surveys, believe they got what they came for, if not more. The biggest challenge, Latimer said, is bringing people from various backgrounds up to speed — the quantitative researchers on the neurobiology aspects, and the students with biomedical backgrounds on the quantitative side. But according to the surveys, the MU Engineering team pulls it off with aplomb.

“We find that everyone comes out knowing more than they did at the start, whether that’s more on the math side or more on the neuro side,” Latimer explained. “Everyone comes away with something, and our hope is that they can take it back to the lab and use it on their own research.”

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