Neural Encoding of Rapidly Fluctuating Odors

Olfactory processing in the insect antennal lobe is a highly dynamic process, yet it has been studied primarily with static step stimuli. To approximate the rapid odor fluctuations encountered in nature, we presented flickering "white-noise" odor stimuli to the antenna of the locust and recorded spike trains from antennal lobe projection neurons (PNs). The responses varied greatly across PNs and across odors for the same PN. Surprisingly, this diversity across the population was highly constrained, and most responses were captured by a quantitative model with just 3 parameters. Individual PNs were found to communicate odor information at rates up to similar to 4 bits/s. A small group of PNs was sufficient to provide an accurate representation of the dynamic odor time course, whose quality was maximal for fluctuations of frequency similar to 0.8 Hz. We develop a simple model for the encoding of dynamic odor stimuli that accounts for many prior observations on the population response.