Exploring a Mathematical Model of Gain Control via Lateral Inhibition in the Antennal Lobe.

Bioinspired Neural Networks have in many instances paved the way for significant discoveries in Statistical and Machine Learning. Among the many mechanisms employed by biological systems to implement learning, gain control is a ubiquitous and essential component that guarantees standard representation of patterns for improved performance in pattern recognition tasks. Gain control is particularly important for the identification of different odor molecules, regardless of their concentration. In this paper, we explore the functional impact of a biologically plausible model of the gain control on classification performance by representing the olfactory system of insects with a Single Hidden Layer Network (SHLN). Common to all insects, the primary olfactory pathway starts at the Antennal Lobes (ALs) and, then, odor identity is computed at the output of the Mushroom Bodies (MBs). We show that gain-control based on lateral inhibition in the Antennal Lobe robustly solves the classification of highly-concentrated odors. Furthermore, the proposed mechanism does not depend on learning at the AL level, in agreement with biological literature. Due to its simplicity, this bioinspired mechanism may not only be present in other neural systems but can also be further explored for applications, for instance, involving electronic noses.