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

Diamond, A, Schmuker, M, Berna, A Z, Trowell, S and Nowotny, Thomas (2016) Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system. Bioinspiration & Biomimetics, 11 (2). ISSN 1748-3190

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Abstract

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 practical
settings, where sensor data is real-time, continuous, potentially noisy, lacks a precise onset signal and
accurate classification requires the inclusion of temporal aspects into the feature set. This investigation
therefore 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 and
competition in the insect antennal lobe, we apply it to identifying 20 individual chemical odours from
the timeseries of responses of metal oxide sensors. We show that four out of twelve available sensors
and the first 30 s(10%) of the sensors’ continuous response are sufficient to deliver 92% accurate
classification without access to an odour onset signal. In contrast to previous approaches, once
training is complete, sensor signals can be fed continuously into the classifier without requiring
discretization. We conclude that for continuous data there may be a conceptual advantage in using
spiking networks, in particular where time is an essential component of computation. Classification
was achieved in real time using a GPU-accelerated spiking neural network simulator developed in our
group.

Item Type: Article
Keywords: Electronic noses, Bioinspired neural networks, Classification, Insect olfaction
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: Alan Diamond
Date Deposited: 25 Feb 2016 12:32
Last Modified: 07 Mar 2017 10:35
URI: http://sro.sussex.ac.uk/id/eprint/59755

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Project NameSussex Project NumberFunderFunder Ref
Fast electronic noses through spiking neuromorphic networksG1208EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCILEFXD13024
Human Brain Project: Neuromorphic Implementations of Multivariate Classification Inspired by the Olfactory SystemG1359EUROPEAN UNION604102 HBP NEUROCLASSIOS
Biomachinelearning: Bio-inspired Machine Learning for Chemical Sensing (fellow: Michael Schmuker)G1382EUROPEAN UNIONPIEF-GA-2012-331892