A simple task like walking to one’s favorite coffee shop involves computation across several timescales. On a short time scale (< 1s), one has to move one’s legs on an uneven surface and maintain balance. On a medium time-scale (~ few seconds) one has to walk relatively straight on a sidewalk. On a longer time-scale (~ minutes), one has to follow the street signs or use one’s memory to navigate. On an even longer time-scale are decisions such as whether or not to drink coffee. My lab is interested in defining the behavioral algorithms at each of these different time scales, identifying the neural circuits which execute them and understanding the neural computations underlying behavior at these different timescales.
We will pursue this question in the context of the tiny fruit fly’s olfactory system. Humans rely chiefly on vision for their description of the world around them. But for many organisms, the world is dominated by their sense of smell, in which every day activities like finding food depends on the ability to modulate behavior based on olfactory cues. Using the fly olfactory system as model, we seek to understand computations which underlie olfactory behavior at each step from the olfactory receptor neuron which initially bind to the odor to the muscles which ultimately determine movement.