Spatio-temporal information in an artificial olfactory mucosa
EntityUAM. Departamento de Ingeniería Informática
PublisherThe Royal Society of Chemistry
10.1098/rspa.2007.0140Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464 (2008): 1057–1077
ISSN1471-2946 (online); 1364-5021 (print)
Funded byWe also acknowledge the support of the Royal Society via a Joint European Research Project (to T.C.P. and M.A.S.-M.) and Engineering and Physical Sciences Research Council (to T.C.P. and J.W.G.). M.A.S.-M. was also supported by MEC (grant BFU2006-07902/BFI) and CAM (PRICIT 5-SEM-0255-2006).
ProjectComunidad de Madrid. S2006/SEM-0255/OLFACTOSENSE
SubjectsArtificial olfactory mucosa; Information theory; Machine olfaction; Informática
NoteFirst publication of the article by the Royal Society of Chemistry.
Rights© Royal Society of Chemistry 2008
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
Deploying chemosensor arrays in close proximity to stationary phases imposes stimulus-dependent spatio-temporal dynamics on their response and leads to improvements in complex odour discrimination. These spatio-temporal dynamics need to be taken into account explicitly when considering the detection performance of this new odour sensing technology, termed an artificial olfactory mucosa. For this purpose, we develop here a new measure of spatio-temporal information that combined with an analytical model of the artificial mucosa, chemosensor and noise dynamics completely characterizes the discrimination capability of the system. This spatio-temporal information measure allows us to quantify the contribution of both space and time to discrimination performance and may be used as part of optimization studies or calculated directly from an artificial mucosa output. Our formal analysis shows that exploiting both space and time in the mucosa response always outperforms the use of space alone and is further demonstrated by comparing the spatial versus spatio-temporal information content of mucosa experimental data. Together, the combination of the spatio-temporal information measure and the analytical model can be applied to extract the general principles of the artificial mucosa design as well as to optimize the physical and operating parameters that determine discrimination performance.
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