| The ∼ 830 projection neurons (PNs) of the locust antennal lobe respond to odors with dense, odor-specific spatio-temporal activity patterns that are mapped via intrinsic and circuit properties into a sparse representation by the Kenyon cells of the mushroom body, which are in turn read out by the beta-lobe neurons (bLNs). In this thesis we present several analyses of this system. First, we describe metrics for quantifying the geometric properties of PN population responses in the full response space that verify the structures revealed by locally linear embedding. Second, we analyze the mixture responses of single PNs and find that in many cases the mixture response can be explained using one of the component responses. Grouping PNs by their single component preferences reveals a potentially simple substrate for olfactory computations. Third, we look for evidence of cycle-by-cycle decoding of PNs by KCs. We show that much of the variance in single KC responses can be explained using small numbers of PNs, and conversely, that PN odor response trajectories can be reconstructed using KC responses. Finally, in a theoretical/computational analysis, we assemble some of the basic biological facts about the locust olfactory system into an architecture for the online learning of arbitrary mappings from odors to valences. |
