Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification. (November 2015)

• Record Type: Journal Article
• Authors: Sarkar, Sankho Turjo; Bhondekar, Amol P.; Macaš, Martin; Kumar, Ritesh; Kaur, Rishemjit; Sharma, Anupma; Gulati, Ashu; Kumar, Amod
• Is Part Of: Neural networks. Volume 71(2015:Nov.)
• Page Start: 142
• Page End: 149
• Publication Date: 2015-11
• Subjects: Electronic nose -- McNemar's test -- Spiking neural network -- Tea -- Spike latency coding -- Dynamically evolving spiking neural networks; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2015.07.014
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: 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.