Caitlyn Parmelee
Caitlyn joined the Math Department at ºÚÁϸ£ÀûÉç in 2016 after receiving her PhD in Mathematics from the University of Nebraska - Lincoln. She is an applied mathematician working in mathematical neuroscience. She works with students at ºÚÁϸ£ÀûÉç and collaborators at Brown University and the University of Northern Colorado studying how the structure of neural networks affects network behavior.
Current Research Students: Madeleine Forhan ('25), Genevieve Steenhoek (‘25), Olivia Kaminske ('26), and maybe you!
Former Research Students: Cameron Spiess (‘20), Abigail McKinley ('24)
Publications: C. Parmelee, S. Moore, K. Morrison, C. Curto. Core motifs predict dynamic attractors in combinatorial threshold-linear networks. PLoS ONE 17(3): e0264456, 2022.
C. Parmelee, J. Londono Alvarez, C. Curto, and K. Morrison. Sequential Attractors in Combinatorial Threshold-Linear Networks. SIAM Journal on Applied Dynamical Systems, 2022 21:2, 1597-1630, 2022. or freely available on arXiv
M. Adams, S. Hatch, E. Winsor, and C. Parmelee (2022) "Development of a Standard Push-up Scale for College-Aged Females," International Journal of Exercise Science, 15:4, 820 - 833, 2022.
W.B. Thoreson, M.J. Van Hook, C.M. Parmelee, C. Curto. Modeling and measurement of vesicle pools at the cone ribbon synapse: Changes in release probability are solely responsible for voltage-dependent changes in release. Synapse 70(1):1-14, 2016.
M.J. Van Hook, C.M. Parmelee, M. Chen, K.M. Cork, C. Curto, W.B. Thoreson. Calmodulin enhances ribbon replenishment and shapes filtering of synaptic transmission by cone photoreceptors. Journal of General Physiology, 144:357-378, 2014.