Odor recognition in an artificial nose by spatio-temporal processing using an olfactory neuronal network

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We have developed an analytical approach for an artificial chemosensing system that is based directly on olfactory neural circuits. An array of chemosensors, with response properties similar to olfactory sensory neurons , provides time-varying inputs to a computer simulation of the olfactory bulb (OB). The OB simulation produces spatio-temporal patterns of spiking that vary with odor type. These patterns are then recognized by a delay line neural network (DLNN). After OB–DLNN processing, odor identity is encoded by activity across DLNN units, and odor intensity is encoded by response latency, enabling discrimination among organic vapors over a range of concentrations.