Abstract
distribution with the experimentally observed mean and standard deviation. We then used these response sets to classify the chemicals (odors) within the two sets all against all using a standard linear support vector machine classifier [3]. To address our original question of which combination of receptors promises the best odor recognition in the two example applications, we used a so-called wrapper approach: We formed all possible combinations of subsets of receptors and evaluated their performance in odor recognition using the linear SVM in 10-fold
Highlights
Animals detect volatiles in the environment with an animal-specific set of olfactory receptor molecules
To address this problem we collected a large number of in vivo recordings from individual Drosophila olfactory receptor neurons in response to two sets of chemicals: A set of 36 chemicals related to wine making (“wine set”) [2] and a set of 35 chemicals related to security applications (“risk set”) [1]
We characterized the responses of olfactory receptor neurons, each expressing one of 20 considered olfactory receptors (ORs) types, by their mean firing rate and the standard deviation of the mean in repeated experiments
Summary
Animals detect volatiles in the environment with an animal-specific set of olfactory receptor molecules. To address this problem we collected a large number of in vivo recordings from individual Drosophila olfactory receptor neurons in response to two sets of chemicals: A set of 36 chemicals related to wine making (“wine set”) [2] and a set of 35 chemicals related to security applications (“risk set”) [1]. We characterized the responses of olfactory receptor neurons, each expressing one of 20 considered OR types, by their mean firing rate and the standard deviation of the mean in repeated experiments.
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