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Bio-Inspired Strategies for Improving the Selectivity and Sensitivity of Artificial Noses: A Review
Open AccessArticle

Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification

1
School of Engineering, Edith Cowan University, Perth 6027, Australia
2
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand
3
Intelligent Systems Research Centre, Ulster University, Magee Campus, Londonderry BT48 7JL, UK
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(10), 2756; https://doi.org/10.3390/s20102756
Received: 3 April 2020 / Revised: 3 May 2020 / Accepted: 7 May 2020 / Published: 12 May 2020
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. View Full-Text
Keywords: biomimetic pattern-recognition; neuromorphic olfaction; electronic nose systems; spiking neural networks (SNNs); SNN-based classification biomimetic pattern-recognition; neuromorphic olfaction; electronic nose systems; spiking neural networks (SNNs); SNN-based classification
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MDPI and ACS Style

Vanarse, A.; Espinosa-Ramos, J.I.; Osseiran, A.; Rassau, A.; Kasabov, N. Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification. Sensors 2020, 20, 2756. https://doi.org/10.3390/s20102756

AMA Style

Vanarse A, Espinosa-Ramos JI, Osseiran A, Rassau A, Kasabov N. Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification. Sensors. 2020; 20(10):2756. https://doi.org/10.3390/s20102756

Chicago/Turabian Style

Vanarse, Anup; Espinosa-Ramos, Josafath I.; Osseiran, Adam; Rassau, Alexander; Kasabov, Nikola. 2020. "Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification" Sensors 20, no. 10: 2756. https://doi.org/10.3390/s20102756

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