In this letter, we use the firing rates from an array of olfactory sensory neurons (OSNs) of the fruit fly, Drosophila melanogaster, to train an artificial neural network (ANN) to distinguish different chemical classes of volatile odorants. Bootstrapping is implemented for the optimized networks, providing an accurate estimate of a network's predicted values. Initially a simple linear predictor was used to assess the complexity of the data and was found to provide low prediction performance. A nonlinear ANN in the form of a single multilayer perceptron (MLP) was also used, providing a significant increase in prediction performance. The effect of the number of hidden layers and hidden neurons of the MLP was investigated and found to be effective in enhancing network performance with both a single and a double hidden layer investigated separately. A hybrid array of MLPs was investigated and compared against the single MLP architecture. The hybrid MLPs were found to classify all vectors of the validation set, presenting the highest degree of prediction accuracy. Adjustment of the number of hidden neurons was investigated, providing further performance gain. In addition, noise injection was investigated, proving successful for certain network designs. It was found that the best-performing MLP was that of the double-hidden-layer hybrid MLP network without the use of noise injection. Furthermore, the level of performance was examined when different numbers of OSNs used were varied from the maximum of 24 to only 5 OSNs. Finally, the ideal OSNs were identified that optimized network performance. The results obtained from this study provide strong evidence of the usefulness of ANNs in the field of olfaction for the future realization of a signal processing back end for an artificial olfactory biosensor.

The olfactory system detects volatile chemical compounds, known as odour molecules or odorants. Such odorants have a diverse chemical structure which in turn interact with the receptors of the olfactory system. The insect olfactory system provides a unique opportunity to directly measure the firing rates that are generated by the individual olfactory sensory neurons (OSNs) which have been stimulated by odorants in order to use this data to inform their classification. In this work, we demonstrate that it is possible to use the firing rates from an array of OSNs of the vinegar fly, Drosophila melanogaster, to train an Artificial Neural Network (ANN), as a series of a Multi-Layer Perceptrons (MLPs), to differentiate between eight distinct chemical classes. We demonstrate that the MLPs when trained on 108 odorants, for both clean and 10% noise injected data, can reliably identify 87% of an unseen validation set of chemicals using noise injection. In addition, the noise injected MLPs provide a more accurate level of identification. This demonstrates that a 10% noise injected series of MLPs provides a robust method for classifying chemicals from the firing rates of OSNs and paves the way to a future realisation of an artificial olfactory biosensor.

Chemical descriptors are a way to define information concerning the physical, chemical and biological properties of a chemical compound. Machine learning methods such as the Artificial Neural Network (ANN) can be used to learn and predict such compounds by training on the compounds chemical descriptors. The motivation of our work is to predict odorant molecules for the development of an artificial biosensor. In this work, we demonstrate using a set of 32 optimized odorant descriptors how an assembly of MultiLayer Perceptrons (MLPs) can be successfully trained to differentiate among eight different chemical classes of odorant. In this communication, we demonstrate how it is possible to predict all 15/15 vectors from an unseen validation set with a high average prediction accuracy of 88.5% for the validation vectors. Furthermore, an introduction of a 10% noise injection level to the training set, increased the learning rate significantly as well as improve the average prediction accuracy of the MLPs to 92% for the validating vectors. Thus, this work indicates the promise of using odorant descriptor values to accurately predict chemical class and so move us forward to the realisation of an artificial odorant biosensor.

Various odorants such as carbon dioxide (CO2) and 1-octen-3-ol, underlie the host-seeking behaviors of the major malaria vector Anopheles Gambiae. Highlighted by the olfactory processing strength of the mosquito, such a powerful olfactory sense could serve as the sensors of an artificial olfactory biosensor. In this work, we use the firing rates of the A. Gambiae mosquito Olfactory Receptor Neurons (ORNs), to train an Artificial Neural Network (ANN) for the classification of volatile odorants into their known chemical classes and assess their suitability for an olfactory biosensor. With the implementation of bootstrapping, a more representative result was obtained wherein we demonstrate the training of a hybrid ANN consisting of an array of Multi-Layer Perceptrons (MLPs) with optimal number of hidden neurons. The ANN system was able to correctly class 90.1% of the previously unseen odorants, thus demonstrating very strong evidence for the use of A. Gambiae olfactory receptors coupled with an ANN as an olfactory biosensor.

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.