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Date of Award
Doctor of Philosophy (PhD)
Accurate detection and identification of gases pose a number of challenges for chemical sensory systems. The stimulus space is enormous; volatile compounds vary in size, charge, functional groups, and isomerization among others. Furthermore, variability arises from intrinsic (poisoning of the sensors or degradation due to aging) and extrinsic (environmental: humidity, temperature, flow patterns) sources. Nonetheless, biological olfactory systems have been refined over time to overcome these challenges. The main objective of this work is to understand how the biological olfactory system deals with these challenges, and translate them to artificial olfaction to achieve comparable capabilities. In particular, this thesis focuses on the design and computing mechanisms that allow a relatively simple invertebrate olfactory system to robustly recognize odorants even though the sensory neurons inputs may vary due to the identified intrinsic, or extrinsic factors.
In biological olfaction, signal processing in the central circuits is largely shielded from the variations in the periphery arising from the constant replacement of older olfactory sensory neurons with newer ones. Inspired by this design principle, we developed an analytical method where the operation of a temperature programmed chemiresistor is treated akin to a mathematical input/output (I/O) transform. Results show that the I/O transform is unique for each analyte-transducer combination, robust with respect to sensor aging, and is highly reproducible across sensors of equal manufacture. This enables decoupling of the signal processing algorithms from the chemical transducer, and thereby allows seamless replacement of sensor array, while the signal processing approach was kept a constant. This is a key advance necessary for achieving long-term, non-invasive chemical sensing.
Next, we explored how the biological system maintains invariance while environmental conditions, particularly with respect to changes in humidity levels. At the sensory level, odor-evoked responses to odorants did not vary with changes in humidity levels, however, the spontaneous activity varied significantly. Nevertheless, in the central antennal lobe circuits, ensembles of projection neurons robustly encoded information about odorant identity and intensity irrespective of the humidity levels. Interestingly, variations in humidity levels led to variable compression of intensity information which was carried forward to behavior. Taken together, these results indicate how the influence of humidity is diminished by central neural circuits in the biological olfactory system.
Finally, we explored a potential biomedical application where a robust chemical sensing approach will be immensely useful: non-invasive assay for malaria diagnosis based on exhaled breath analysis. We developed a method to screen gas chromatography/mass spectroscopy (GC/MS) traces of human breath and identified 6 compounds that have abundance changes in malaria infected patients and can potentially serve as biomarkers in exhaled breath for their diagnosis. We will conclude with a discussion of on-going efforts to develop a non-invasive solution for diagnosing malaria based on breath volatiles.
In sum, this work seeks to understand the basis for robust odor recognition in biological olfaction and proposes bioinspired and statistical solutions for achieving the same abilities in artificial chemical sensing systems.
Parag Banerjee, Jianmin Cui, Timothy Holy, Daniel Moran,
Available for download on Friday, April 19, 2019