Dr. Bing Brunton
Associate Professor, University of Washington
Postdoctoral Researcher, University of Washington
PhD in Molecular Biology & Neuroscience, Princeton University
Written by Sheila Steiner
Dr. Bing Brunton, an Associate Professor at the University of Washington, got her start in neuroscience by modeling which factors best explain the trajectory of neural recordings across time. When it comes to her own trajectory to neuroscience, a long-standing fascination with math and one fateful slice of free pizza proved to be particularly essential factors. Now, Dr. Brunton is a computational neuroscientist who performs modeling of the relationship between the brain and behavior in a wide variety of contexts, from insect flight to brain-computer interfaces.
As an undergraduate at Caltech, Bing became fascinated with quantitative biology while working in a lab studying microbes. She intended to continue this research when she started her PhD at Princeton, planning to join a microbiology lab she had set her sights on early in her PhD. However, when free pizza, the universal vice of graduate students, was offered at a seminar on behavioral neuroscience, Bing eagerly attended. Here, she listened to a talk by Dr. Carlos Brody, who uses dynamical systems to study decision making behaviors. Dynamical systems is a type of math in which the evolution of a certain system is studied over time. This type of math is well suited to study biological systems that change with time, such as the growth of a bacterial colony, or in the case of Dr. Brody’s lab, neural recordings collected over the span of a behavioral task. Bing was fascinated by the mathematical ideas Dr. Brody was presenting, but they were all being used to analyze neural data, a far cry from the microbiology she was used to. However, motivated by her fascination with the math of dynamical systems, she went up to talk to Dr. Brody after the seminar and scheduled a rotation in his lab. She joined the lab and quickly became a bonafide computational neuroscientist. In Dr. Brody’s laboratory, she built a series of behavioral paradigms meant to determine an organism’s ability to make decisions in uncertain conditions. She then tested these paradigms on rats and humans and built dynamical systems models to determine which best fit the experimental data collected. This modeling provided insight into how rats and humans accumulate evidence to make a decision, and what brain regions and patterns of neural activity encode this accumulation of evidence over time.
Bing had her first child while in graduate school, and she was pregnant with her second while defending. After taking the summer off post-defense to road-trip around the country with her husband and first child, Bing arrived at her post-doc at the University of Washington seven months pregnant. After conversations with her PIs, Dr. Tom Daniel and Dr. Nathan Kutz, Bing decided she wanted to focus on theoretical work for the remainder of her pregnancy to have a more flexible work schedule and potentially shift back to a balance of experimental and theoretical work later. However, as her project evolved, she found herself mostly focused on theoretical work throughout her postdoc. Bing found that dynamic mode decomposition, a way of reducing the dimensionality of spatial-temporal data, was a useful method for analyzing large datasets of neural recordings. She validated this approach by using it to analyze ECoG, or electrocorticography, data, which is collected from an electrode array implanted in the human brain for medical reasons.
Now as a professor at the University of Washington, Bing tries to follow the mentorship example of the many supportive and kind mentors she had in her scientific career. She focuses on providing an environment where it is recognized that trainees are people as well as scientists. Bing describes trying to build an environment that trainees can use as a jumping-off point; her goal as a PhD advisor and post-doc mentor is to help people move on and succeed in their next steps. Bing remarks that the softer skills required for these career moves are one area of science she was less knowledgeable about as a trainee. She hopes to ensure that her trainees have the experience and knowledge required to navigate the soft skills of the academic world and beyond. She describes lab meetings focused on this ambition, such as meetings in which everyone presents their CVs and the lab critiques them.
Scientifically, her lab is entirely theoretical, meaning that no one is limited by equipment or resources to one particular topic, and it is possible to combine across many levels or modalities of data in a way that is often impractical in experimental labs. As a theorist, she views her role in science as an accelerator; by modeling different experiments that may be too expensive or time consuming, she can isolate which hypotheses are most likely correct. This can help narrow the field of hypotheses and the scale of experiments. As an overarching theme, Bing focuses on modeling the connection between the brain and naturalistic behavior. Bing says she has “shiny object syndrome,” constantly jumping between projects and topics based on what seems like an interesting question. Describing her scientific lab meeting environment as a bit of “intellectual whiplash,” Bing says she and her lab enjoy the diversity of topics covered. Her projects span from understanding how insect flight works with the intent of informing airplane design, to decoding human neural recordings to predict future movements.
Outside of mentorship in the lab, Bing is also focused on teaching computational neuroscience skills to a larger audience. She is involved in Neuromatch Academy, a program intended to teach computational neuroscience to thousands of students each summer. Bing also places her lectures and course content online, as she believes this multiplies her impact as a teacher and makes it easier for everyone to access and develop some level of data fluency. At her core, Bing is focused on increasing accessibility in computational neuroscience. As a woman in a male-dominated field, her mentors, although kind and supportive, were never female, and thus had experiences and lives different from her own. She feels that this representation is important, and she sees hope in the newer generation of scientists’ awareness and insistence on change in this realm. Bing’s steadfast commitment to making science more accessible through many different avenues will undoubtably have immesurable impact on budding computational neuroscientists globally.
Find out more about Bing and her lab’s research heres://www.bingbrunton.com/.
Listen to Meenakshi’s full interview with Bing on January 27th, 2023 below!
