Elizabeth A. De Laittre, PhD, graduated in Winter 2025 from the Computational Neuroscience program and received the Program Award for Outstanding Performance in the General Field of Computational Neuroscience. Her dissertation, The Natural Variability of a Dexterous Motor Skill Is Stably Encoded in the Cortex of Freely Behaving Mice, conducted under the mentorship of Dr. Jason MacLean, explored how the brain encodes variability in skilled movement. Using cutting-edge techniques to record and analyze neural activity in freely moving mice, Elizabeth demonstrated that even subtle behavioral differences are stably represented in cortical circuits, offering new insight into how the brain supports flexible motor control.
At the University of Chicago, her research bridged experimental neuroscience with data-driven analysis, contributing to our understanding of motor learning, variability, and neural representation. Her work has advanced the field’s understanding of how fine-grained aspects of behavior are neurally encoded, with implications for movement disorders, rehabilitation, and the design of brain-machine interfaces.