Towards a cognitive understanding of odor discrimination: Combining experimental and theoretical approaches

In response to changes in odorous environmental conditions, most species (ranging from lower invertebrates to mammals), demonstrate high adaptive behavioral performances. Complex natural chemical signals (i.e. odorous blends involved in food search), are particularly unstable and fluctuating, in quality, space and time. Nevertheless, adapted behavioral responses related to meaningful odor signals can be observed even in complex natural odorous environments, demonstrating that the underlying olfactory neural network is a very dynamic pattern recognition device. In the honeybee, a large amount of experimental data have been collected at different levels of observation within the olfactory system, from signal processing to behavior, including cellular and molecular properties. However, no set of data considered by itself can give insight into the mechanisms underlying odor discrimination and pattern recognition. Here, by concentrating on deciphering the neural mechanisms underlying encoding and decoding of the olfactory signal in the two first layers of the neural network, we illustrate how a theoretical approach helps us to integrate the different experimental data and to extract relevant parameters (features) which might be selected and used to store an odor representation in a behavioral context.