Using Multilayer Perceptron Computation to Discover Ideal Insect Olfactory Receptor Combinations in the Mosquito and Fruit Fly for an Efficient Electronic Nose

Neural Computation - Tập 27 Số 1 - Trang 171-201 - 2015
Luqman R. Bachtiar1, Charles P. Unsworth1, Richard D. Newcomb2
1Department of Engineering Science, University of Auckland, Auckland 1142, New Zealand
2New Zealand Institute for Plant and Food Research Limited, Auckland 1025, and School of Biological Sciences, University of Auckland, Auckland 1142, New Zealand

Tóm tắt

The model organism, Drosophila melanogaster, and the mosquito Anopheles gambiae use 60 and 79 odorant receptors, respectively, to sense their olfactory world. However, a commercial “electronic nose” in the form of an insect olfactory biosensor demands very low numbers of receptors at its front end of detection due to the difficulties of receptor/sensor integration and functionalization. In this letter, we demonstrate how computation via artificial neural networks (ANNs), in the form of multilayer perceptrons (MLPs), can be successfully incorporated as the signal processing back end of the biosensor to drastically reduce the number of receptors to three while still retaining 100% performance of odorant detection to that of a full complement of receptors. In addition, we provide a detailed performance comparison between D. melanogaster and A. gambiae odorant receptors and demonstrate that A. gambiae receptors provide superior olfaction detection performance over D. melanogaster for very low receptor numbers. The results from this study present the possibility of using the computation of MLPs to discover ideal biological olfactory receptors for an olfactory biosensor device to provide maximum classification performance of unknown odorants.

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