Front-end Weber-Fechner gain control enhances the fidelity of combinatorial odor coding

Odor identity is encoded by spatiotemporal patterns of activity in olfactory receptor neurons (ORNs). In natural environments, the intensity and timescales of odor signals can span several orders of magnitude, and odors can mix with one another, potentially scrambling the combinatorial code mapping neural activity to odor identity. Recent studies have shown that in Drosophila melanogaster the ORNs that express the olfactory co-receptor Orco scale their gain inversely with mean odor concentration according to the Weber-Fechner Law of psychophysics. Here we use a minimal biophysical model of signal transduction, ORN firing, and signal decoding to investigate the implications of this front-end scaling law for the neural representations of odor identity. We find that Weber-Fechner scaling enhances coding capacity and promotes the reconstruction of odor identity from dynamic odor signals, even in the presence of confounding background odors and rapid intensity fluctuations. We show that these enhancements are further aided by downstream transformations in the antennal lobe and mushroom body. Thus, despite the broad overlap between individual ORN tuning curves, a mechanism of front-end adaptation, when endowed with Weber-Fechner scaling, may play a vital role in preserving representations of odor identity in naturalistic odor landscapes.