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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; Nowotny, Thomas.

In: Bioinspiration & Biomimetics, Vol. 11, No. 2, 18.02.2016, p. 026002.

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@article{95d3cac89e4f4442a07f959809105002,
title = "Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system",
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.",
keywords = "electronic noses, bioinspired neural networks, classification, insect olfatation",
author = "A Diamond and M Schmuker and Berna, {A Z} and S Trowell and Thomas Nowotny",
note = "{\textcopyright} 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. ",
year = "2016",
month = feb,
day = "18",
doi = "10.1088/1748-3190/11/2/026002",
language = "English",
volume = "11",
pages = "026002",
journal = "Bioinspiration & Biomimetics",
issn = "1748-3182",
publisher = "IOP Publishing Ltd.",
number = "2",

}

RIS

TY - JOUR

T1 - Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system

AU - Diamond, A

AU - Schmuker, M

AU - Berna, A Z

AU - Trowell, S

AU - Nowotny, Thomas

N1 - © 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.

PY - 2016/2/18

Y1 - 2016/2/18

N2 - 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.

AB - 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.

KW - electronic noses

KW - bioinspired neural networks

KW - classification

KW - insect olfatation

U2 - 10.1088/1748-3190/11/2/026002

DO - 10.1088/1748-3190/11/2/026002

M3 - Article

C2 - 26891474

VL - 11

SP - 026002

JO - Bioinspiration & Biomimetics

JF - Bioinspiration & Biomimetics

SN - 1748-3182

IS - 2

ER -