Robust Odorant Recognition in Biological and Artificial Olfaction

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.