A Spiking Neural Network for Gas Discrimination using a Tin Oxide Sensor Array

Maxime Ambard 1 Bin Guo 2 Dominique Martinez 1 Amine Bermak 2
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 HKUST - Hong Kong University of Science and Technology
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 from gas sensor arrays.
Keywords : Tin Oxide Gas Sensor Array Spike Timing Computation Supervised Learning
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Communication dans un congrès
4th IEEE International Symposium on Electronic Design, Test & Applications - DELTA 2008, Jan 2008, Hong-Kong, Hong Kong SAR China. 2008
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Maxime Ambard, Bin Guo, Dominique Martinez, Amine Bermak. A Spiking Neural Network for Gas Discrimination using a Tin Oxide Sensor Array. 4th IEEE International Symposium on Electronic Design, Test & Applications - DELTA 2008, Jan 2008, Hong-Kong, Hong Kong SAR China. 2008. 〈inria-00401777〉

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