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The brain is a remarkably complex network of neurons, and many functions and dysfunctions of the mind cannot be localized to any particular part of the brain. Further, network activity changes in time at every spatial scale, from molecular dynamics of single synapses to coordinated oscillations across brain areas to circadian rhythms. Distilling spatial-temporal coherent patterns from large scale, noisy measurements is vital to understanding how networks of neurons give rise to behavior.
I am inspired by system-level questions in neuroscience: How can we describe the multi-scale connective topology of brain areas? What functional metrics differentiate a neuronal network before and after learning? To tackle these and related questions, I leverage recent mathematical advances in the fields of dimensionality reduction and compressive sensing.
Bing Brunton is an Associate Professor in the Department of Biology. She joined the faculty in 2014 as part of the Provost’s Initiative in Data-Intensive Discovery to build an interdisciplinary research program at the intersection of biology and data science. She also holds appointments in the Paul G. Allen School of Computer Science & Engineering and the Department of Applied Mathematics. Her training spans biology, biophysics, molecular biology, neuroscience, and applied mathematics (B.S. in Biology from Caltech in 2006, Ph.D. in Neuroscience from Princeton in 2012). Her group develops data-driven analytic methods that are applied to, and are inspired by, neuroscience questions. The common thread in this work is the development of methods that leverage the escalating scale and complexity of neural and behavioral data to find interpretable patterns. She has received the Alfred P. Sloan Research Fellowship in Neuroscience (2016), the UW Innovation Award (2017), and the AFOSR Young Investigator Program award (2018) for her work on sparse sensing with wing mechanosensory neurons.