Contrast enhancement of gas sensor array patterns with a neurodynamics model of the olfactory bulb

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.