Neural Comput - Multilayer perceptron classification of unknown volatile chemicals from the firing rates of insect olfactory sensory neurons and its application to biosensor design.

Tópicos

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Resumo

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.

Resumo Limpo

letter use fire rate array olfactori sensori neuron osn fruit fli drosophila melanogast train artifici neural network ann distinguish differ chemic class volatil odor bootstrap implement optim network provid accur estim network predict valu initi simpl linear predictor use assess complex data found provid low predict perform nonlinear ann form singl multilay perceptron mlp also use provid signific increas predict perform effect number hidden layer hidden neuron mlp investig found effect enhanc network perform singl doubl hidden layer investig separ hybrid array mlps investig compar singl mlp architectur hybrid mlps found classifi vector valid set present highest degre predict accuraci adjust number hidden neuron investig provid perform gain addit nois inject investig prove success certain network design found bestperform mlp doublehiddenlay hybrid mlp network without use nois inject furthermor level perform examin differ number osn use vari maximum osn final ideal osn identifi optim network perform result obtain studi provid strong evid use ann field olfact futur realiz signal process back end artifici olfactori biosensor

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