Effect of circuit structure on odor representation in the insect olfactory system

Abstract

In Neuroscience, the structure of a circuit has often been used to intuit function – an inversion of Louis Kahn’s famous dictum, `Form follows function’ (Kristan and Katz 2006). However, different brain networks may utilize different network architectures to solve the same problem. The olfactory circuits of two insects, the Locust, Schistocerca americana, and the fruit fly, Drosophila melanogaster, serve the same function – to identify and discriminate odors. The neural circuitry that achieves this shows marked structural differences. Projection neurons (PN) in the antennal lobe (AL) innervate Kenyon cells (KC) of the mushroom body (MB). In locust, each KC receives inputs from ∼50% PNs, a scheme that maximizes the difference between inputs to any two of ∼50,000 KCs. In contrast, in drosophila, this number is only 5% and appears sub-optimal. Using a computational model of the olfactory system, we show the activity of KCs is sufficiently high-dimensional that it can separate similar odors regardless of the divergence of PN-KC connections. However, when temporal patterning encodes odor attributes, dense connectivity outperforms sparse connections.Increased separability comes at the cost of reliability. The disadvantage of sparse connectivity can be mitigated by incorporating other aspects of circuit architecture seen in drosophila. Our simulations predict that drosophila and locust circuits lie at different ends of a continuum where the drosophila gives up on the ability to resolve similar odors to generalize across varying environments, while the locust separates odor representations but risks misclassifying noisy variants of the same odor. Significance Statement How does the structure of a network affect its function? We address this question in the context of two olfactory systems that serve the same function, to distinguish the attributes of different odorants, but do so using markedly distinct architectures. In the locust, the probability of connections between projection neurons and Kenyon cells - a layer downstream - is nearly 50%. In contrast, this number is merely 5% in drosophila. We developed computational models of these networks to understand the relative advantages of each connectivity. Our analysis reveals that the two systems exist along a continuum of possibilities that balance two conflicting goals – separating the representations of similar odors while grouping together noisy variants of the same odor.