Adaptation of olfactory receptor abundances for efficient coding

TL;DR

This paper presents a model explaining the heterogeneous distribution of olfactory receptors as an adaptation for efficient coding of odor information, predicting receptor abundance changes based on environmental odor statistics.

Contribution

It introduces a theoretical framework linking receptor abundance adaptation to efficient coding principles and provides algorithms to predict receptor changes after environmental odor exposure.

Findings

Receptor abundances adapt to odor statistics in a way consistent with efficient coding.

Different receptor responses to odor exposure are explained by sensor correlations.

Proposed dynamical rules could underlie neural receptor turnover processes.

Abstract

Olfactory receptor usage is highly heterogeneous, with some receptor types being orders of magnitude more abundant than others. We propose an explanation for this striking fact: the receptor distribution is tuned to maximally represent information about the olfactory environment in a regime of efficient coding that is sensitive to the global context of correlated sensor responses. This model predicts that in mammals, where olfactory sensory neurons are replaced regularly, receptor abundances should continuously adapt to odor statistics. Experimentally, increased exposure to odorants leads variously, but reproducibly, to increased, decreased, or unchanged abundances of different activated receptors. We demonstrate that this diversity of effects is required for efficient coding when sensors are broadly correlated, and provide an algorithm for predicting which olfactory receptors should increase or decrease in abundance following specific environmental changes. Finally, we give simple dynamical rules for neural birth and death processes that might underlie this adaptation.