My lab is interested in understanding the design and computing principles of biological sensory systems and translating this knowledge into bio-inspired intelligent systems and machine learning algorithms. Research in my lab involves two main themes:
Biological Olfaction

How does the nervous system convert sensory stimuli into neural representations? Odorants are detected by a large population of olfactory receptor neurons (ORNs), which convert chemical stimuli into an electrical signal that is relayed downstream for further processing in the olfactory bulb (OB, vertebrates) or the antennal lobe (AL, insects). In the AL/OB, interactions between ensembles of excitatory principal neurons and inhibitory local neurons reshape the ORN input into complex, slow spatio-temporal patterns that are superimposed on a faster oscillatory field potential activity. The patterned AL/OB responses contain information about odor identity and intensity and form the only odor representation available to the organism. Hence they are considered to be the ‘odor code’. The generated odor code is then transmitted to learning and memory centers.

Related Publications:
Artificial Olfaction

An electronic nose is an instrument that combines an array of cross-selective chemical sensors and a pattern recognition engine to recognize chemical species. We employ statistical and bio-inspired signal approaches to design & operate MEMS-based chemiresistive microsensor arrays and tune them for specific chemical sensing application

Target applications of electronic nose include:

Related Publications: