Increasing the separability of chemosensor array patterns with Hebbian/anti-Hebbian learning

The olfactory bulb is able to enhance the contrast between odor representations through a combination of excitatory and inhibitory circuits. Inspired by this mechanism, we propose a new Hebbian/anti-Hebbian learning rule to increase the separability of sensor-array patterns in a neurodynamics model of the olfactory system: the KIII. In the proposed learning rule, a Hebbian term is used to build associations within odors and an anti-Hebbian term is used to reduce correlated activity across odors. The KIII model with the new learning rule is characterized on synthetic data and validated on experimental data from an array of temperature-modulated metal-oxide sensors. Our results show that the performance of the model is comparable to that obtained with Linear Discriminant Analysis (LDA). Furthermore, the model is able to increase pattern separability for different concentrations of three odorants: allyl-alcohol, tert-butanol, and benzene, even though it is only trained with the gas sensor response to the highest concentration.