Figure 2.

Biological principles that have inspired parallel approaches in artificial olfaction. (a) Odor molecules traversing the aqueous, protein-filled interface between the environment and the sensory neurons (ORNs) are differentially transported, helping different odorants to evoke different spatiotemporal patterns of activation. (b) Left: The response of the population of receptor neurons to an odor approximates a Gaussian distribution, arranged here by the receptors’ selectivity for Odors A and B.³³ Increasing odorant concentrations recruit additional responses from neurons less selective for the odorant, broadening this distribution. Right: Vector representations of odor identity and intensity coding across the population of sensory neurons allow ready comparisons of response characteristics. (c) Integrating redundant information from multiple copies of a receptor allows the olfactory system to reduce uncorrelated noise. Left: Firing rates of two receptor neurons over time. Right: The baseline fluctuations observed in the two independent channels are reduced when the channels are integrated, improving the signal-to-noise ratio. (Reprinted with permission from ref (98)). (d) Lateral interactions between projection neurons (PNs) nonlinearly transform responses originating in sensory neurons and restructure odor patterns to become more uniformly distributed and distinct. (Reprinted with permission from ref (99). Copyright 2008 Elsevier). (e) The olfactory system refines odor representation over time such that features common across a set of chemicals are extracted first (odor class information) and finer features required for precise identification are extracted subsequently. Thus a single system, over time, resolves conflicting demands posed by the problems of odor classification and recognition.