How do brain cells perform the computations that underlie the vast repertoire of behaviors and cognitive powers that make us human? It’s a demanding question that requires an eye for both fine detail and the big picture to answer. It’s also just the kind of brain-mind question that consumes Lea Duncker, PhD, of the Zuckerman Institute’s Center for Theoretical Neuroscience.

A new arrival at the institute in 2024, Dr. Duncker wants to take on some of neuroscience’s loftiest challenges. Among these are to develop theoretical and mathematical models of movement in all of its diversity and of learning and decision-making, which play into so many of our cognitive processes. At the same time, she eagerly anticipates collaborating with her experimentalist colleagues to anchor her model-making in data from biological brains as they orchestrate the complex business of life. 

“We can execute and vary many intricate behaviors and learn new skills throughout our lives that don’t interfere with what we already know how to do,” Dr. Duncker said. “I find this fascinating and would like to better understand the network-level mechanisms in brains underlying this flexibility and adaptability.”

Dr. Duncker’s research style merges two approaches. One is theoretical, in which the goal is to develop models of computational networks that can solve the tough problems actual brain circuits achieve every day. The other centers on statistical analyses of data from brain recordings by her experimental colleagues. In recent years, she has been turning to that latter data-analysis challenge with help from machine learning. That’s a type of artificial intelligence that can help scientists discern otherwise hidden patterns in the ever growing amounts of neuroscience data. 

“The culture at the Theory Center is to integrate these two approaches and I am excited about discovering new connections and inspirations from these synergies,” said Dr. Duncker. “I like to have one foot in experimental data and one foot in theory because I think that is a formula for making important discoveries in neuroscience.”

Dr. Duncker grew up in Germany with a pair of physician parents. That’s partly why she has been “science adjacent” for as long as she can remember. There also was plenty of art and music in the family culture and Duncker says she is grateful to now have a mix of science and art in her ways of thinking and doing.

She pegs one origin of her interest in neuroscience to a documentary she saw as a teenager about autism and the miraculous feats of memory that some people with autism can execute. “I was just amazed by what the brains of these individuals with Savant Syndrome are capable of,” she said, adding, jokingly, that in contrast she sometimes finds it hard to remember what she had for lunch.

As an undergraduate, Duncker studied Natural Sciences at University College London. This enabled her to pursue a combination of neuroscience, math and statistics and to discover her interest in the intersection of these subjects. She continued on at UCL to earn a master’s degree in computational statistics and machine learning, during which she took classes and conducted research at the Gatsby Computational Neuroscience Unit. She then committed to her long-standing fascination with the brain by pursuing a PhD in Theoretical Neuroscience at the Gatsby Unit. 

Duncker noted that her affection for math and statistics has deep roots for her, but that ending up pursuing these fields in her career pathway relied on a few lucky decisions and on encouragement from teachers and colleagues. For instance, Duncker initially went to UCL wanting to concentrate on neuroscience and chemistry, but when she was confronted with actually having to choose her focus, she realized that math and statistics were better fits for her. 

Now, Dr. Duncker is launching a new phase of her career. “There are so many different and creative experimental groups at the institute and so I am thrilled about how many directions there will be to grow,” she said.