Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification
Language English Country United States Media print-electronic
Document type Journal Article
PubMed
26356597
DOI
10.1016/j.neunet.2015.07.014
PII: S0893-6080(15)00151-3
Knihovny.cz E-resources
- Keywords
- Dynamically evolving spiking neural networks, Electronic nose, McNemar’s test, Spike latency coding, Spiking neural network, Tea,
- MeSH
- Algorithms MeSH
- Biomimetics MeSH
- Tea * MeSH
- Olfactory Perception MeSH
- Equipment Design MeSH
- Electronic Nose * MeSH
- Neural Networks, Computer * MeSH
- Normal Distribution MeSH
- Nose MeSH
- Odorants * MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Tea * MeSH
The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis.
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