Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system

摘要

In many application domains, conventional e-noses are frequently outperformed in both speed and accuracy by their biological counterparts. Exploring potential bio-inspired improvements, we note a number of neuronal network models have demonstrated some success in classifying static datasets by abstracting the insect olfactory system. However, these designs remain largely unproven in practicaludsettings, where sensor data is real-time, continuous, potentially noisy, lacks a precise onset signal andudaccurate classification requires the inclusion of temporal aspects into the feature set. This investigationudtherefore seeks to inform and develop the potential and suitability of biomimetic classifiers for use with typical real-world sensor data. Taking a generic classifier design inspired by the inhibition andudcompetition in the insect antennal lobe, we apply it to identifying 20 individual chemical odours fromudthe timeseries of responses of metal oxide sensors. We show that four out of twelve available sensorsudand the first 30 s(10%) of the sensors’ continuous response are sufficient to deliver 92% accurateudclassification without access to an odour onset signal. In contrast to previous approaches, onceudtraining is complete, sensor signals can be fed continuously into the classifier without requiringuddiscretization. We conclude that for continuous data there may be a conceptual advantage in usingudspiking networks, in particular where time is an essential component of computation. Classificationudwas achieved in real time using a GPU-accelerated spiking neural network simulator developed in ourudgroup.