Generation of Sparse, Combinatorial Wiring for Sensory Coding
Creators: name: Ahmed, Maria
Publisher: name: My University
Year: 2023
Subjects: subject: neural circuits
subject: sparse coding
subject: mushroom body
subject: learning and memory
subject: Drosophila melanogaster
subject: olfaction
subject: Molecular, Cellular and Developmental Biology
subject: Science

description: We experience countless sensory cues on a daily basis. How is the brain wired to deal with this wide and unpredictable array of stimuli with only a limited set of neurons? To do this, principal neurons of sensory perception and associative learning regions receive information about cues in combinations. In regions including the cerebellum, there is an expansion of inputs from a small set of neurons onto a much larger set of postsynaptic cells, and each postsynaptic cell receives a few inputs (&lt;10). Additionally, the combination of inputs to each neuron are variable and unstructured, allowing us to perceive novel, unfamiliar stimuli. Such sparse, distributed innervation is also observed in the fruit fly mushroom body, which is primarily involved in olfactory perception and associative learning. Despite this circuit architecture being commonly used, we do not understand (1) how sparse, combinatorial connectivity relates to our sensory capabilities, and (2) what mechanisms underlie emergence of this connectivity pattern. In my dissertation work, I sought to answer these questions using the Drosophila melanogaster mushroom body as a model system. ~2000 Kenyon cells (KCs) within the mushroom body each integrate inputs from a combination of 3-10 projection neurons (PNs), and each Kenyon cell receives a different set of inputs. Theoretically, the sparse density of inputs, together with the variability in inputs, prepares the olfactory system for evolutionarily novel environments. The first part of my dissertation (Chapter 2) tests in vivo the relation between sparse, combinatorial connectivity and olfactory representations in the brain. To do this, I made developmental perturbations to the mushroom body circuit to independently vary Kenyon cell number, and Kenyon cell input density. I then tested the functional and behavioral consequences. Using in vivo calcium imaging, I found that input density tunes sparsity of odor responses; Kenyon cells become less odor-selective as their input number grows. Moreover, odor responses were robust to changes in Kenyon cell numbers. Next, we examined the effect of these circuit alterations on behavior. Flies with reduced Kenyon cell numbers learned simple olfactory associations similar to controls, while those with increased Kenyon cells showed improved learning. Flies with more inputs per KC became worse at discriminating similar odors. Together, these results demonstrate for the first time the significance of input number in tuning sensory perception in vivo, and provide a novel method for testing the “purpose” of observed learning circuit architectures. The second part of my dissertation (Chapter 3) explores the mechanism underlying variability in inputs to Kenyon cells. I hypothesized that individual Kenyon cells must either express variable cell surface molecules themselves, allowing them to partner with diverse PNs, or they must downregulate expression of these molecules to “ignore” the molecular heterogeneity of olfactory PNs. To discriminate between these models, I performed single-cell RNA-sequencing on Kenyon cells in development. I found that Kenyon cells were largely depleted of cell surface and secreted molecules, and Kenyon cells within the same class were transcriptionally homogenous in expression of these molecules. Therefore, I propose that by downregulating cell surface molecules, Kenyon cells can “ignore” the deterministic molecular diversity of incoming olfactory PNs, ending up with promiscuous inputs. Together, these findings illuminate the developmental and functional principles underlying sparse, unstructured combinatorial connectivity, and provide a rich resource of circuit alterations for future behavioral investigations of expansion layer circuit parameters in cognition.