T.C. Pearce, M. Elshaw, School of Engineering, University of Derby, England.
While the original research programme for a ``model" or ``electronic" nose was begun in the early eighties as an attempt to produce a facsimile of the biological olfactory system, this has largely given way to attempts to engineer practical chemical sensing technology. Without doubt, impressive advances have been made over this period in this endevour, particularly in terms of improvements in sensor technology, odour delivery and control, and also pattern recognition algorithms. However, current systems have been developed with almost no consideration to the biological olfactory system.
In view of the recent advances in the understanding of information processing in neural system, and in particular olfaction, many opportunities exist for incorporating this into its analogue - the ``electronic nose". While the biological olfactory system may not be a unique solution to combining exceptional molecular specificity with high sensitivity, there are key processing principles that give rise to its performance.
In this paper, we investigate the application of computational neuroscience models of the olfactory pathway to machine-odour sensing. By combining a working model of the olfactory bulb with an array of nonspecific, broadly-tuned, chemical sensors, we hope to develop a system for discriminating learnt odour patterns. Through the development of such a combined system, it is our aim, to demonstrate improved performance with respect to sensitivity, sensor drift, and background odours.