Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification
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-c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/10/2756 |
| _version_ | 1797568221354655744 |
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| author | Anup Vanarse Josafath Israel Espinosa-Ramos Adam Osseiran Alexander Rassau Nikola Kasabov |
| author_facet | Anup Vanarse Josafath Israel Espinosa-Ramos Adam Osseiran Alexander Rassau Nikola Kasabov |
| author_sort | Anup Vanarse |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:53:21Z |
| format | Article |
| id | doaj.art-3a8fa28e519c4ddfb37e942e8311b65d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:53:21Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3a8fa28e519c4ddfb37e942e8311b65d2023-11-20T00:10:43ZengMDPI AGSensors1424-82202020-05-012010275610.3390/s20102756Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data ClassificationAnup Vanarse0Josafath Israel Espinosa-Ramos1Adam Osseiran2Alexander Rassau3Nikola Kasabov4School of Engineering, Edith Cowan University, Perth 6027, AustraliaKnowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New ZealandSchool of Engineering, Edith Cowan University, Perth 6027, AustraliaSchool of Engineering, Edith Cowan University, Perth 6027, AustraliaKnowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New ZealandExisting 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.https://www.mdpi.com/1424-8220/20/10/2756biomimetic pattern-recognitionneuromorphic olfactionelectronic nose systemsspiking neural networks (SNNs)SNN-based classification |
| spellingShingle | Anup Vanarse Josafath Israel Espinosa-Ramos Adam Osseiran Alexander Rassau Nikola Kasabov Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification Sensors biomimetic pattern-recognition neuromorphic olfaction electronic nose systems spiking neural networks (SNNs) SNN-based classification |
| title | Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification |
| title_full | Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification |
| title_fullStr | Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification |
| title_full_unstemmed | Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification |
| title_short | Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification |
| title_sort | application of a brain inspired spiking neural network architecture to odor data classification |
| topic | biomimetic pattern-recognition neuromorphic olfaction electronic nose systems spiking neural networks (SNNs) SNN-based classification |
| url | https://www.mdpi.com/1424-8220/20/10/2756 |
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