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• A.K.Vidybida. Selectivity Gain in Olfactory Projection Neurons at Low Odor Concentrations. Submitted to the IEEE SENSORS 2020 Conference.
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  Abstract. It is known that selectivity of an olfactory projection neuron is better than that of receptor ones converging on it. Under high odor concentration, the selectivity is improved due to lateral inhibition mechanism in the olfactory bulb. This mechanism does not work at low concentrations. We propose an original mechanism which could improve selectivity at low concentration of odors, which is based on the stochastic nature of stimuli obtained by a projection neuron from the receptor ones. The mechanism operates at the level of communication from receptor neurons to a projection neuron, and does not require involvement of other bulbar neurons. As a projection neuron model we use one described by the Korolyuk, Kostyuk, Pjatigoskii, Tkachenko. In this model, the membrane electrical leakage is modeled by spontaneous random decay of each input impulse, which is kept unchanged until the decay. We analyze the neuron’s triggering process due to stochastic stimulation from receptor neurons by exactly calculating the mean interspike interval for the projection neuron. This allows to compare selectivity of projection neuron with that of receptor neurons converging on it. Exact mathematical expression is obtained for the selectivity gain in projection neurons as compared to that in the receptor ones. A possibility of high gain at low odor concentration is predicted based on the expression obtained. The stochastic nature of communication from receptor to projection neurons causes selectivity improvement in the projection ones at low odor concentration, when the lateral inhibition mechanism in the olfactory bulb does not work. Keywords: odor ensors, selectivity, olfactory neurons, selectivity, stochastic process.