Emi M. Wheelock

BioE Ph.D. Proposal Presentation

Date and Time: Friday, May 15th, 2026, at 1:00 PM (EDT)

Location: EBB Krone Conference Room 3029

https://gatech.zoom.us/j/97249014901

Advisor: Hang Lu, Ph.D. (Chemical and Biomolecular Engineering, Georgia Institute of Technology)

Committee:

Gordon Berman, Ph.D. (Biology, Emory University)
Matthieu Bloch, Ph.D. (Electrical and Computer Engineering, Georgia Institute of Technology)
Anqi Wu, Ph.D. (Computational Science and Engineering, Georgia Institute of Technology)
Patrick McGrath, Ph.D. (Biological Sciences, Georgia Institute of Technology)

Experience-Dependent Reweighting of Conserved Neural Circuit Dynamics in C. elegans

Learning allows animals to use past experience to change how sensory cues guide future behavior, but it remains unclear how this change is implemented in brain-wide neural activity. One possibility is that learning creates a new neural response pattern; another is that learning changes how sensory cues recruit pre-existing patterns already present in the circuit. Distinguishing these possibilities requires treating neural activity as a time-evolving population process, rather than only asking which neurons increase or decrease their activity. C. elegans is well suited for this problem because controlled sensory stimulation, whole-brain calcium imaging, quantitative behavior, and genetic perturbation can be combined in the same animal. This thesis investigates how aversive olfactory learning changes brain-wide neural dynamics in C. elegans, using odors from standard bacterial food (E. coli OP50), pathogenic bacteria (Pseudomonas aeruginosa PA14), and buffer controls. Aim 1 will identify reproducible odor-evoked dynamical features across animals using latent dynamical modeling and invariant summaries. Aim 2 will test whether pathogen training shifts pathogen-evoked dynamics toward food-associated structure or produces a distinct trained-state response. Aim 3 will determine whether learning-sensitive dynamical readouts predict turning behavior and reveal how targeted genetic perturbations disrupt learned sensorimotor computation.