The University of Washington’s Computational Neuroscience Center – Decoding Intelligence
The CNC is a hub for research in mathematical and computational neuroscience, connecting researchers at the University of Washington across campus and to the extended neuroscience community in the Pacific Northwest. Research topics span the full spectrum of scales, mechanisms, and functions of the brain — from the cellular biophysics of computation to brainwide models of neural processing and cognition to next-generation brain/computer interfaces. The Center, which also houses the Swartz Center for Theoretical Neuroscience, is the campus home for undergraduate, graduate and postdoctoral training and research programs linking theoretical and experimental neuroscientists to advance understanding of the principles of neural computation.
Participating faculty members’ research includes theory, computation and data analysis and members interact extensively with colleagues in quantitative experimentation and imaging. Our faculty hold positions in departments across campus including Physiology and Biophysics, Biological Structure, Applied Mathematics, Statistics, Computer Science and Engineering, Biology, Psychology, Bioengineering, and Electrical and Computer Engineering — giving students the chance to find their natural disciplinary home — but are closely connected through a dense web of interdisciplinary, cross-departmental collaboration.
The CNC has a lively calendar of activities and interactions in mathematical and computational approaches to neuroscience, including workshops, an annual retreat, a seminar series, student symposia, a weekly journal club, speaker lunches and public lectures. To participate, join one of the training programs or simply keep in touch with all of our open opportunities via our mailing list!
- Coding Principles in Adapation Alison Weber, Kamesh Krishnamurthy, Adrienne Fairhall
- Population adaptation in efficient balanced networks. Gabrielle Gutierrez, Sophie Denève
- Feedback through graph motifs relates structure and function in complex networks. Yu Hu, Steven L Brunton, Nicholas Cain, Stefan Mihalas, J. Nathan Kutz, Eric Shea-Brown