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Selectivity Gain in Olfactory Projection Neurons at Low Odor Concentrations.
Submitted to the IEEE SENSORS 2020 Conference.
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
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.