Friday, February 4th, 2022, the NeuroSchool PhD Program will invite Adrienne Fairhall (University of Washington, Seattle, USA) for a seminar on variability, learning and robustness in birdsong.
The songbird zebra finch is an exemplary model system in which to study trial-and-error learning, as the bird learns its single song gradually through the production of many noisy renditions. It is also a sound system to study the maintenance of motor skills, as the adult bird actively maintains its song and retains some residual plasticity. Motor learning occurs by association with timing within the music, represented by sparse firing in nucleus HVC, with motor output driven by nucleus RA. Here we show through modeling that the trim level of the observed variability in HVC can result in a network that is more easily able to adapt to change and is most robust to cell damage or death than an unperturbed network. In collaboration with Carlos Lois’ lab, we also consider the effect of directly perturbing HVC through viral injection of toxins that affect the firing of projection neurons. The song is profoundly affected following these perturbations but can almost perfectly recover. We characterize the changes in song acoustics and syntax and propose models for HVC architecture and plasticity to account for some of the observed effects. Finally, we suggest a potential role for inputs from nucleus Uva in helping to control timing precision in HVC.
- HVC (High vocal center) : is part of the premotor pathway necessary for song production and is also a primary source of input to the anterior forebrain pathway (AFP), a basal ganglia-related circuit essential for vocal learning.
- RA (the robust nucleus of the arcopallium) : HVC project to RA and, in the adult, fire in a rapid burst exactly once during the song.
- Uva (Nucleus Uvaeformis of the thalamus) : sends input to two forebrain nuclei (NIf and HVC) but has not been thought to be important for song production.
February 4th, 2022, online on zoom / on site INT
- Discussion among students – For Ph.D students only – location ➡️ pending decision, contact the chairman for participation.
- 2:30 pm – Seminar – Open to all – on-site: Henri Gastaut room, ground floor of the INT / online on zoom
- 3:30 pm – Discussion with the speaker – For Ph.D. students only – on-site: Laurent Vinay room, first floor of the INT / online on zoom
📢 Ph.D. Students, register on AMETICE for your hours to be counted. Attendants get 3 hours, chairpersons 4 hours.
Register and receive the zoom link
Adrienne Fairhall is a professor in the Department of Physiology and Biophysics and an adjunct in Physics and Applied Mathematics at the University of Washington (UW). She obtained her honors degree in theoretical physics from the Australian National University and a Ph.D. in statistical physics from the Weizmann Institute of Science. She joined the UW faculty in 2004 after postdoctoral studies at Princeton University. She is the co-director of the UW Computational Neuroscience Center and its educational programs. She has directed the Marine Biological Laboratories course, Methods in Computational Neuroscience, and the UW/Allen Institute Summer Workshop for the Dynamic Brain. She has held Burroughs-Wellcome, McKnight, and Sloan fellowships and is currently Tocqueville-Fulbright Distinguished Chair. Her work focuses on neural coding and computation across various systems and species, with a particular interest in the interplay between cellular and circuit dynamics and coding.