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dc.contributor.authorDiamond, A
dc.contributor.authorSchmuker, M
dc.contributor.authorBerna, A Z
dc.contributor.authorTrowell, S
dc.contributor.authorNowotny, Thomas
dc.date.accessioned2016-11-30T18:14:05Z
dc.date.available2016-11-30T18:14:05Z
dc.date.issued2016-02-18
dc.identifier.citationDiamond , A , Schmuker , M , Berna , A Z , Trowell , S & Nowotny , T 2016 , ' Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system ' , Bioinspiration & Biomimetics , vol. 11 , no. 2 , pp. 026002 . https://doi.org/10.1088/1748-3190/11/2/026002
dc.identifier.issn1748-3182
dc.identifier.otherPURE: 10469132
dc.identifier.otherPURE UUID: 95d3cac8-9e4f-4442-a07f-959809105002
dc.identifier.otherPubMed: 26891474
dc.identifier.otherScopus: 84963522612
dc.identifier.urihttp://hdl.handle.net/2299/17373
dc.description© 2016 IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence https://creativecommons.org/licenses/by/3.0/ Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
dc.description.abstractIn 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.en
dc.language.isoeng
dc.relation.ispartofBioinspiration & Biomimetics
dc.rights/dk/atira/pure/core/openaccesspermission/open
dc.subjectelectronic noses
dc.subjectbioinspired neural networks
dc.subjectclassification
dc.subjectinsect olfatation
dc.titleClassifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory systemen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Computer Science
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2016-01-21
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1088/1748-3190/11/2/026002
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeopenAccess


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