Computational Neuroscience

The University of Oregon's Computational Neuroscience research is highly collaborative, and trainees often receive shared supervision between computational and experimental labs on joint projects. Our group aims to understand how the collective activity of large networks of neurons leads to the emergence of cognitive function and behavior, how information processing in the brain arises through learning and plasticity, and how it is modulated by context and behavioral states. To model information processing in the brain, we employ a combination of mathematical approaches from theoretical physics and applied mathematics, together with brain-inspired artificial intelligence models used to emulate the complex ways in which neural circuits learn and represent information. We also develop and deploy new machine learning tools to analyze large neural datasets recorded from behaving animals. In collaboration with experimental labs at the University of Oregon and other institutions around the world, we design new experiments to test models and develop theories of brain function, cognition and learning.



Name Department Research Interest
Felix Deku Knight Campus for Accelerating Scientific Impact Microelectrodes development and clinical translation
Tim Gardner Knight Campus for Accelerating Scientific Impact Sensory motor learning and neural interface development
Shawn Lockery Biology Department Neurogenetic basis of decision making
Luca Mazzucato Biology Department Computational neuroscience, Artificial Intelligence
David McCormick Biology Department Cortical neural circuits of behavior and attention
James Murray Biology Department Theoretical and computational neuroscience
Cristopher Niell Biology Department Neural circuits for natural vision
Michael Wehr Psychology Department Neural computation in auditory circuits