My research program is centered around two major projects:

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. However, most models are dominated by a single time scale, whereas a range of time scales are likely to influence information processing in neural circuits. A second focus of the lab is to use theory and modeling to hone our intuitions about temporal coding. One aspect of this work focuses on understanding the temporal response properties of simplified integrate-and-fire neurons. More general investigation will explore the possibility that neural circuits use interacting encoding schemes operating on different time scales to multiplex information.