Todd W. Troyer, Ph.D.
Phone: (210) 458-5487
Areas of Specialization
» Behavioral and neural dynamics
» Computational neuroscience
» Noise and sychnronization in neural circuits
» Songbird learning
Ph.D. in Math; University of California, Berkeley
B.A. in Math and Physics; Washington University, St. Louis
Behavioral analysis and computational modeling of vocal development in songbirds
The combination of a learned, stereotyped behavior and specialized anatomy make birdsong an ideal system in which to study the neural basis of complex behavior. However, much work will be required to bridge the gap between the functional anatomy of the song system and song behavior. A crucial component in building this bridge will be to gain a better understanding of the acoustic signals produced during vocal learning. Therefore, one goal of the lab is to collect and analyze a large database of song collected from juvenile birds. A second goal is to work on building the bridge directly, by constructing computational models of song learning.
Neural encoding and dynamical processing in models of neural circuits
Computational models serve as important analogies for thinking about how biological mechanisms give rise to complex behavior. A second focus of the lab is to use theory and modeling to hone our intuitions about temporal temporal processing in neurons. This work includes efforts to understand the temporal response properties of simplified integrate-and-fire neurons, understand the relationship between the phase response curve (PRC) in the presence of noise, and to understand the mechanisms of subthreshold resonances in neurons of the mammalian striatum.
Click here for a list of publications.