Author Identifier

Anup Varnase

ORCID: https://orcid.org/0000-0002-0082-4283

Adam Osseiran

ORCID: https://orcid.org/0000-0001-9611-9345

Alexander Rassau

ORCID: https://orcid.org/0000-0002-8295-5681

Document Type

Journal Article

Publication Title

Sensors

Publisher

MDPI

School

School of Engineering

RAS ID

31875

Funders

Edith Cowan University - Open Access Support Scheme

Comments

Vanarse, A., Espinosa-Ramos, J.I., Osseiran, A., Rassau, A., & Kasabov, N. (2020). Application of a brain-inspired spiking neural network architecture to odor data classification. Sensors, 20(10), Article 2756. https://doi.org/10.3390/s20102756

Abstract

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.

DOI

10.3390/s20102756

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Included in

Engineering Commons

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