Denis Turcu is a postdoctoral researcher at the Allen Institute and the University of Washington. He develops computational models to uncover general principles of brain organization. In this Postdoc Portrait interview, he shares how one course changed the trajectory of his academic career and his newfound appreciation of biology.
Uncovering Principles of Computational Neuroscience
Q | What drew you to neuroscience?
I grew up captivated by mathematics, drawn to its precision and logic. When I started studying physics, I found something even more attractive to me. It had the same rigor but its concepts tied to how we experience the world. I took detours into astrophysics and explored industry positions, but I kept searching for a field where building hypotheses, making predictions, and testing them experimentally felt connected to everyday life.
As I advanced in physics, the topics I enjoyed most grew increasingly removed from daily experience, a natural consequence of the field's maturity. Then, I took a computational neuroscience course that drew me in immediately. For me, this was a field that used the same mathematical tools that I loved to ask questions about how we sense, move, think, and learn. I was motivated by the rich problems, fascinating experiments and behaviors, and connection to how we experience the world.
As for my specific research direction, studying how the brain's constraints shape its computational solutions, I was fortunate to have the freedom to explore various ideas and guided by outstanding mentors who helped me discover what naturally motivates me most.
Q | What scientific problem are you trying to solve?
The brain must sense, decide, and act, but it does so under tight constraints that limit the solutions available. I want to understand how neural circuits navigate these constraints and find elegant, efficient computational strategies despite them.
Some constraints are physical. Weakly electric fish, for example, sense their surroundings through self-generated electric fields, but the signals they detect are tiny and warp dramatically with distance. During my PhD, I built computational models to understand how these fish extract reliable information from such challenging signals. Other constraints are biomechanical, such as limb motion, and the nervous system must coordinate movement within those limits. And architectural constraints, such as structured, sparsely connected circuits, restrict what computations the brain can perform.
What fascinates me is that the brain doesn't just cope with these constraints, rather it finds remarkably creative solutions to overcome them. I suspect that studying how different constraints have shaped neural computation over evolutionary time will reveal general principles about why the brain is organized the way it is.
Bridging Biological Complexity and Machine Intelligence
Q | What’s one thing you learned during your switch from physics to neuroscience that you didn’t expect?
I didn't expect to become so drawn to the biology. When I transitioned from physics into neuroscience, I assumed I would primarily work on mathematically elegant, abstract problems, the kind of work that attracts many physicists to the field. And while I deeply enjoy reading that literature, I found that I struggled to stay motivated when the questions, however beautiful mathematically, felt too removed from computation in the brain for me.
Instead, I learned that biology is full of surprises. As just one example, recently completed maps of the fruit fly's brain wiring have revealed circuit structures that weren't anticipated, challenging existing theories and opening entirely new questions. The more I learned about how real circuits are wired, how animals sense their environments and how movement is coordinated, the more I wanted to build models grounded in those biological details. I learned that embracing the complexity of biology and realistic constraints faced by animals, rather than abstracting them away, is where I find the most interesting research questions.
Q | If your research succeeds, what could it change for science or society?
For science, I hope that studying neural computation through the lens of constraints will provide common ground across subfields. Every circuit faces physical, biomechanical, or architectural limitations, and recognizing shared principles could bridge communities that don't always speak the same language. For society, understanding how the brain finds efficient solutions under tight constraints could inform the design of more adaptable, energy-efficient autonomous machines and robots.
Q | What question are you most excited to answer next?
I’m looking forward to answering how the brain combines sensory feedback with internal models of the body and the world to interact with uncertain, imperfect environments, and how it learns those internal models that help generate rich behaviors, such as an athlete's beautiful performance in their field.
Responses have been edited for length and clarity.
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