@inproceedings{2fb44a981abb49258ff861b4094c90a3,
title = "A spiking neural network for gas discrimination using a tin oxide sensor array",
abstract = "We propose a bio-inspired signal processing method for odor discrimination. A spiking neural network is trained with a supervised learning rule so as to classify the analog outputs from a monolithic 4×4 tin oxide gas sensor array implemented in our in-house 5 μm process. This scheme has been sucessfully tested on a discrimination task between 4 gases (hydrogen, ethanol, carbon monoxide, methane). Performance compares favorably to the one obtained with a common statistical classifier. Moreover, the simplicity of our method makes it well suited for building dedicated hardware for processing data fromg gas sensor arrays.",
keywords = "Gas sensor array, Spike timing computation, Supervised learning, Tin oxide",
author = "Maxime Ambard and Bin Guo and Dominique Martinez and Amine Bermak",
year = "2008",
doi = "10.1109/DELTA.2008.116",
language = "English",
isbn = "0769531105",
series = "Proceedings - 4th IEEE International Symposium on Electronic Design, Test and Applications, DELTA 2008",
pages = "394--397",
booktitle = "Proceedings - 4th IEEE International Symposium on Electronic Design, Test and Applications, DELTA 2008",
note = "4th IEEE International Symposium on Electronic Design, Test and Applications, DELTA 2008 ; Conference date: 23-01-2008 Through 25-01-2008",
}