Invariances in a Combinatorial Olfactory Receptor Code

ORAL

Abstract

Animals can identify an odorant type across a wide range of concentrations, as well as detect changes in concentration for individual odorant. How olfactory representations are structured to support these functions remains poorly understood. Here, we studied how a full complement of olfactory receptor neurons (ORNs) in the Drosophila larva encodes a broad input space of odorant types and concentrations. We find that dose-response relationships across odorants and ORNs follow the Hill function with shared cooperativity but different activation thresholds. These activation thresholds are drawn from a power law distribution. A fixed activation function and power law distribution of activation thresholds underlie invariances in the encoding of odorant identity and intensity. Moreover, we find similar temporal response filters of ORNs across odorant types and concentrations. Such uniformity in the temporal filter may allow identity invariant coding in fluctuating odor environments. Common patterns in ligand-receptor binding and sensory transduction across olfactory receptors may give rise to these observed invariances in the olfactory combinatorial code. Invariant patterns in the activity responses of individual ORNs and the ORN ensemble may simplify decoding by downstream circuits.

Presenters

  • Guangwei Si

    Physcis, Harvard University

Authors

  • Guangwei Si

    Physcis, Harvard University

  • Jessleen Kanwal

    Program in Neuroscience, Harvard University

  • Yu Hu

    Center for Brain Science, Harvard University

  • Christopher Tabone

    Physcis, Harvard University

  • Jacob Baron

    Physcis, Harvard University

  • Matthew Berck

    Physcis, Harvard University

  • Gaetan Vignoud

    Physcis, Harvard University

  • Aravinthan Samuel

    Physcis, Harvard University