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 use 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 (PNs) in the antennal lobe innervate Kenyon cells (KCs) of the mushroom body. In locust, each KC receives inputs from ;50% of 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 suboptimal. Using a computational model of the olfactory system, we show that 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.

Hybrid Neural Networks of the Olfactory Learning Center in theDrosophilaBrain

“…4A). For the assessment of coding capacity, we examined the KC hamming code dimensionality, using binary representations to describe activated and inactivated states ( 45-47 ). Notably, through PCA, we found that γ KCs exhibited significantly higher coding capacity compared to α/β and α’/β’ KCs (Fig.…”

Plasticity in inhibitory networks improves pattern separation in early olfactory processing

“…Modeling studies (Rajagopalan and Assisi, 2020) have also found that the categorization of odors in the MB depends on changes in PN responses caused by conditioning. Our study suggests a potential mechanism for this change in KC responses.…”

“…In contrast, odor presentations without any reinforcement weaken their representation in KCs [61]. Modeling studies [62] have also indicated that the categorization of odors in the MB depends on changes in PN responses caused by conditioning. Our study suggests a potential mechanism for this change in KC responses.…”

Hacking brain development to test models of sensory coding

“…Beginning with Marr-Albus theory of the cerebellum in the 1970’s, expansion coding has been hypothesized to perform pattern separation. The ratio between sensory channels and expansion layer neurons, and the number of sensory inputs that individual expansion layer neurons receive, are theorized to be key parameters in balancing perception, discrimination, and generalization (Albus, 1971; Babadi and Sompolinsky, 2014; Cayco-Gajic and Silver, 2019; Hiratani and Latham, 2022; Ito, 1972; Jefferis et al, 2007; Jortner et al, 2007; Litwin-Kumar et al, 2017; Luo et al, 2010; Marr, 1969; Modi et al, 2020; Rajagopalan and Assisi, 2020). However, the perceptual and behavioral effects of altering hard-wired quantitative relationships have not been experimentally tested.…”