Neuromorphic Processing for Optical Microbead Arrays: Dimensionality Reduction and Contrast Enhancement

• Published in: IEEE sensors journal 2007-04, Vol.7 (4), p.506-514
• Main Authors: Raman, B., Kotseroglou, T., Clark, L., Lebl, M., Gutierrez-Osuna, R.
• Format: Article
• Language: English
• Subjects: Arrays; Beads; Biomedical optical imaging; Biosensors; Chemical and biological sensors; Chemical technology; Circuits; Lateral inhibition; machine olfaction; Microorganisms; neuromorphic computation; Neuromorphics; Odors; Olfactory; olfactory bulb; Optical arrays; optical microbead sensors; Optical signal processing; Reduction; Sensor arrays; Sensors; sensory convergence; Studies
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2007.891935

This paper presents a neuromorphic approach for sensor-based machine olfaction that combines a portable chemical detection system based on microbead array technology with a biologically inspired model of signal processing in the olfactory bulb. The sensor array contains hundreds of microbeads coated with solvatochromic dyes adsorbed in, or covalently attached on, the matrix of various microspheres. When exposed to odors, each bead sensor responds with corresponding intensity changes, spectral shifts, and time-dependent variations associated with the fluorescent sensors. The bead array responses are subsequently processed using a model of olfactory circuits that capture the following two functions: chemotopic convergence of receptor neurons and center on-off surround lateral interactions. The first circuit performs dimensionality reduction, transforming the high-dimensional microbead array response into an organized spatial pattern (i.e., an odor image). The second circuit enhances the contrast of these spatial patterns, improving the separability of odors. The model is validated on an experimental dataset containing the responses of a large array of microbead sensors to five different analytes. Our results indicate that the model is able to significantly improve the separability between odor patterns, compared to that available from the raw sensor response