Normalized neural representations of natural odors

TL;DR

This paper models how global inhibition in the olfactory system creates concentration-invariant, sparse odor representations, revealing how inhibition strength affects odor discrimination and the impact of receptor heterogeneity.

Contribution

It introduces a simple statistical model showing how global inhibition shapes normalized, sparse odor representations and their dependence on inhibition strength and receptor diversity.

Findings

Inhibition strength can be tuned for optimal odor discrimination.

Odors with many molecular species are harder to discriminate.

Heterogeneous receptor sensitivities impair odor representation quality.

Abstract

The olfactory system removes correlations in natural odors using a network of inhibitory neurons in the olfactory bulb. It has been proposed that this network integrates the response from all olfactory receptors and inhibits them equally. However, how such global inhibition influences the neural representations of odors is unclear. Here, we study a simple statistical model of this situation, which leads to concentration-invariant, sparse representations of the odor composition. We show that the inhibition strength can be tuned to obtain sparse representations that are still useful to discriminate odors that vary in relative concentration, size, and composition. The model reveals two generic consequences of global inhibition: (i) odors with many molecular species are more difficult to discriminate and (ii) receptor arrays with heterogeneous sensitivities perform badly. Our work can thus help to understand how global inhibition shapes normalized odor representations for further processing in the brain.