Frontiers in Computational Neuroscience Computational Neuroscience

Abstract

INTRODUCTION Honeybees (Apis mellifera) are social insects that use odors as communication signals. These odors are of simple structure, typically composed of a single chemical compound. On the contrary, naturally occurring odorants that become relevant for the bee during foraging are generally complex blends of a large number of volatile compounds (Pichersky and Gershenzon, 2002) with time-varying composition and concentrations. Psychophysical studies in bees (Chandra and Smith, 1998; Deisig et al., 2003), catfi sh (Valenticic et al., 2000), and humans (Livermore and Laing, 1998) have shown that the neural representation of an odor mixture can be synthetic or elemental whereby the mixture either acquires a novel perceptual quality or it resembles its elemental compounds. In vertebrates, odor mixture interactions were observed at different levels of the olfactory system, in the periphery (Duchamp-Viret et al., 2003), in the olfactory bulb (Davison and Katz, 2007; Giraudet et al., 2002; Lin et al., 2006; Tabor et al., 2004) and in the olfactory cortex (Lei et al., 2006; Zou and Buck, 2006). Studies of mixture interaction in the honeybee antennal lobe using Ca2+-imaging of glomerular activity revealed suppressive interactions in which the response to an odor mixture can be much lower than the response to the single compounds (Deisig et al., 2006; Joerges et al., 1997; Silbering and Galizia, 2007). Such a suppression effect was not observed in the moth Spodoptera littoralis where glomerular Ca responses to mixtures always demonstrated hypoadditive interactions resembling the response to the strongest compounds within the mixture (Carlsson et al., 2007). Rapid odor processing in the honeybee antennal lobe network