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The 2010 International Joint Conference on Neural Networks (IJCNN)

DOI: 10.1109/ijcnn.2010.5596551

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Testing olfactory models with an artificial experimental platform

This paper is available in a repository.
This paper is available in a repository.

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Abstract

Artificial olfaction systems have been investigated since more than two decades on alleged property of similarities between the receptive field of natural receptors and artificial sensors. Due to the limited number of sensors embedded in these systems the complexity of electronic noses is generally too low to allow the application of olfaction processing models. Actually the literature there are several models attempting to describe some olfaction functionalities, and the availability of an artificial platform to test models could be of great benefit for these studies. Recently, the use of optical image sensors has been demonstrated as a simple method to obtain large sensor arrays. Furthermore, an elegant and simple method to cluster individual sensors in classes allows for the definition of epithelium and glomerular layers. This system enables the application of a complex olfaction model, and these properties are here illustrated applying a glomerular compartmentalization model to the data generated by the exposure of such an artificial system to pure and mixed gases.