{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T22:49:34Z","timestamp":1776725374217,"version":"3.51.2"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,12]],"date-time":"2020-05-12T00:00:00Z","timestamp":1589241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor data, classification techniques based on the bio-computations of the higher brain areas, which process the spiking data for identification of odor, remain largely unexplored. This paper argues that brain-inspired spiking neural networks constitute a promising approach for the next generation of machine intelligence for odor data processing. Inspired by principles of brain information processing, here we propose the first spiking neural network method and associated deep machine learning system for classification of odor data. The paper demonstrates that the proposed approach has several advantages when compared to the current state-of-the-art methods. Based on results obtained using a benchmark dataset, the model achieved a high classification accuracy for a large number of odors and has the capacity for incremental learning on new data. The paper explores different spike encoding algorithms and finds that the most suitable for the task is the step-wise encoding function. Further directions in the brain-inspired study of odor machine classification include investigation of more biologically plausible algorithms for mapping, learning, and interpretation of odor data along with the realization of these algorithms on some highly parallel and low power consuming neuromorphic hardware devices for real-world applications.<\/jats:p>","DOI":"10.3390\/s20102756","type":"journal-article","created":{"date-parts":[[2020,5,12]],"date-time":"2020-05-12T10:53:55Z","timestamp":1589280835000},"page":"2756","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0082-4283","authenticated-orcid":false,"given":"Anup","family":"Vanarse","sequence":"first","affiliation":[{"name":"School of Engineering, Edith Cowan University, Perth 6027, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5128-0610","authenticated-orcid":false,"given":"Josafath Israel","family":"Espinosa-Ramos","sequence":"additional","affiliation":[{"name":"Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9611-9345","authenticated-orcid":false,"given":"Adam","family":"Osseiran","sequence":"additional","affiliation":[{"name":"School of Engineering, Edith Cowan University, Perth 6027, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8295-5681","authenticated-orcid":false,"given":"Alexander","family":"Rassau","sequence":"additional","affiliation":[{"name":"School of Engineering, Edith Cowan University, Perth 6027, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikola","family":"Kasabov","sequence":"additional","affiliation":[{"name":"Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand"},{"name":"Intelligent Systems Research Centre, Ulster University, Magee Campus, Londonderry BT48 7JL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.conb.2010.03.007","article-title":"Neuromorphic sensory systems","volume":"20","author":"Liu","year":"2010","journal-title":"Curr. Opin. Neurobiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1038\/299352a0","article-title":"Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose","volume":"299","author":"Persaud","year":"1982","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Vanarse, A., Osseiran, A., and Rassau, A. (2017). An investigation into spike-based neuromorphic approaches for artificial olfactory systems. Sensors, 17.","DOI":"10.3390\/s17112591"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MIM.2019.8674627","article-title":"Neuromorphic engineering\u2014A paradigm shift for future im technologies","volume":"22","author":"Vanarse","year":"2019","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"774","DOI":"10.3389\/fnins.2018.00774","article-title":"Deep learning with spiking neurons: Opportunities and challenges","volume":"12","author":"Pfeiffer","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3389\/fnins.2016.00115","article-title":"A review of current neuromorphic approaches for vision, auditory, and olfactory sensors","volume":"10","author":"Vanarse","year":"2016","journal-title":"Front. Neurosci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/TCSI.2006.888677","article-title":"Analog vlsi circuit implementation of an adaptive neuromorphic olfaction chip","volume":"54","author":"Koickal","year":"2007","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_8","first-page":"18","article-title":"Glomerular latency coding in artificial olfaction","volume":"4","author":"Yamani","year":"2011","journal-title":"Front. Neuroeng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3389\/fnins.2013.00119","article-title":"Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex","volume":"7","author":"Pearce","year":"2013","journal-title":"Front. Neurosci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lepora, N.F., Mura, A., Krapp, H.G., Verschure, P.F.M.J., and Prescott, T.J. (2013). Robust ratiometric infochemical communication in a neuromorphic \u201csynthetic moth\u201d. Biomimetic and Biohybrid Systems: Second International Conference, Living Machines 2013, London, UK, 29 July\u20132 August, 2013 Proceedings, Springer.","DOI":"10.1007\/978-3-642-39802-5"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kasap, B., and Schmuker, M. (2013, January 6\u20138). Improving Odor Classification through Self-Organized Lateral Inhibition in a Spiking Olfaction-Inspired Network. Proceedings of the 2013 6th International IEEE\/EMBS Conference on Neural Engineering (NER), San Diego, CA, USA.","DOI":"10.1109\/NER.2013.6695911"},{"key":"ref_12","unstructured":"Yamani, J.H.J.A., Boussaid, F., Bermak, A., and Martinez, D. (2012, January 20\u201323). Bio-inspired gas recognition based on the organization of the olfactory pathway. Proceedings of the 2012 IEEE International Symposium on Circuits and Systems, Seoul, South Korea."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"83","DOI":"10.3389\/fnins.2012.00083","article-title":"Implementation of olfactory bulb glomerular-layer computations in a digital neurosynaptic core","volume":"6","author":"Imam","year":"2012","journal-title":"Front. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1109\/TNNLS.2012.2195329","article-title":"Vlsi implementation of a bio-inspired olfactory spiking neural network","volume":"23","author":"Hsieh","year":"2012","journal-title":"IEEE Trans. Neural. Netw. Learn. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1021\/cn200027r","article-title":"Mimicking biological design and computing principles in artificial olfaction","volume":"2","author":"Raman","year":"2011","journal-title":"ACS Chem. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s00542-013-2020-8","article-title":"A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation","volume":"20","author":"Marco","year":"2013","journal-title":"Microsyst. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"491","DOI":"10.3389\/fnins.2015.00491","article-title":"Comparing neuromorphic solutions in action: Implementing a bio-inspired solution to a benchmark classification task on three parallel-computing platforms","volume":"9","author":"Diamond","year":"2016","journal-title":"Front. Neurosci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Vanarse, A., Osseiran, A., Rassau, A., and van der Made, P. (2019). A hardware-deployable neuromorphic solution for encoding and classification of electronic nose data. Sensors, 19.","DOI":"10.3390\/s19224831"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"026002","DOI":"10.1088\/1748-3190\/11\/2\/026002","article-title":"Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system","volume":"11","author":"Diamond","year":"2016","journal-title":"Bioinspir. Biomim."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Vanarse, A., Osseiran, A., and Rassau, A. (2019). Real-time classification of multivariate olfaction data using spiking neural networks. Sensors, 19.","DOI":"10.3390\/s19081841"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"015105","DOI":"10.1088\/1361-6501\/28\/1\/015105","article-title":"Signal processing inspired from the olfactory bulb for electronic noses","volume":"28","author":"Jing","year":"2017","journal-title":"Meas. Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCSI.2017.2697945","article-title":"A bio-inspired analog gas sensing front end","volume":"64","author":"Huang","year":"2017","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.bios.2016.08.080","article-title":"Bioelectronic nose: Current status and perspectives","volume":"87","author":"Wasilewski","year":"2017","journal-title":"Biosens. Bioelectron."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wilson, D.A., and Rennaker, R.L. (2010). Cortical activity evoked by odors. The Neurobiology of Olfaction, Taylor & Francis.","DOI":"10.1201\/9781420071993-c14"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.gde.2011.07.006","article-title":"Olfactory networks: From sensation to perception","volume":"21","author":"Leinwand","year":"2011","journal-title":"Curr. Opin. Genet. Dev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1016\/S0893-6080(01)00083-1","article-title":"Spike-based strategies for rapid processing","volume":"14","author":"Thorpe","year":"2001","journal-title":"Neural Netw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2015.09.011","article-title":"Evolving spatio-temporal data machines based on the neucube neuromorphic framework: Design methodology and selected applications","volume":"78","author":"Kasabov","year":"2016","journal-title":"Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neunet.2014.01.006","article-title":"Neucube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data","volume":"52","author":"Kasabov","year":"2014","journal-title":"Neural Netw."},{"key":"ref_29","unstructured":"Kasabov, N.K. (2007). Evolving Connectionist Systems: The Knowledge Engineering Approach, Springer Science & Business Media."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kasabov, N.K. (2019). Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-662-57715-8"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.neunet.2012.11.014","article-title":"Dynamic evolving spiking neural networks for on-line spatio-and spectro-temporal pattern recognition","volume":"41","author":"Kasabov","year":"2013","journal-title":"Neural Netw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"026014","DOI":"10.1088\/1741-2552\/aafabc","article-title":"Classification and regression of spatio-temporal signals using neucube and its realization on spinnaker neuromorphic hardware","volume":"16","author":"Behrenbeck","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_33","unstructured":"CSIRO, Berna, A., and Stephen, T. (2015). Electronic Nose (fox) Recording of 20 Chemicals, CSIRO Data Collection."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.snb.2013.01.088","article-title":"Optimal feature selection for classifying a large set of chemicals using metal oxide sensors","volume":"187","author":"Nowotny","year":"2013","journal-title":"Sens. Actuators B Chem."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"J46","DOI":"10.1149\/1.3065436","article-title":"Zeolite-modified discriminating gas sensors","volume":"156","author":"Binions","year":"2009","journal-title":"J. Electrochem. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1109\/JSEN.2010.2084079","article-title":"Discrimination effects in zeolite modified metal oxide semiconductor gas sensors","volume":"11","author":"Binions","year":"2010","journal-title":"IEEE Sens. J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1109\/TNNLS.2019.2906158","article-title":"Selection and optimization of temporal spike encoding methods for spiking neural networks","volume":"31","author":"Petro","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.snb.2008.10.065","article-title":"Acceleration of chemo-sensory information processing using transient features","volume":"137","author":"Muezzinoglu","year":"2009","journal-title":"Sens. Actuators B Chem."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/TNNLS.2016.2536742","article-title":"Mapping temporal variables into the neucube for improved pattern recognition, predictive modeling, and understanding of stream data","volume":"28","author":"Tu","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gerstner, W., and Kistler, W.M. (2002). Spiking Neuron Models: Single Neurons, Populations, Plasticity, Cambridge University Press.","DOI":"10.1017\/CBO9780511815706"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hu, J., Hou, Z.-G., Chen, Y.-X., Kasabov, N., and Scott, N. (2014, January 12\u201315). Eeg-based classification of upper-limb adl using snn for active robotic rehabilitation. Proceedings of the 5th IEEE RAS\/EMBS International Conference on Biomedical Robotics and Biomechatronics, Sao Paulo, Brazil.","DOI":"10.1109\/BIOROB.2014.6913811"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2014.2304638","article-title":"The spinnaker project","volume":"102","author":"Furber","year":"2014","journal-title":"Proc. IEEE"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2756\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:28:01Z","timestamp":1760174881000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2756"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,12]]},"references-count":43,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20102756"],"URL":"https:\/\/doi.org\/10.3390\/s20102756","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,12]]}}}