Contrast enhancement of gas sensor array patterns with a neurodynamics model of the olfactory bulb
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We propose a biologically inspired signal processing model capable of enhancing the discrimination of multivariate patterns from gas sensor arrays. The model captures two functions in the early olfactory pathway: chemotopic convergence of sensory neurons onto the olfactory bulb, and center on-off surround lateral interactions. Sensor features are first topologically projected onto a two-dimensional lattice according to their selectivity profile, leading to odor-specific spatial patterning. The resulting patterns serve as inputs to a network of mitral cells with center on-off surround lateral inhibition, which enhances the initial contrast among odors and decouples odor identity from intensity. The model is validated using experimental data from an array of temperature-modulated metal-oxide sensors. Our results indicate that the model is able to improve the separability between odor patterns that is available at the inputs. 2006 Elsevier B.V. All rights reserved.