TY - JOUR T1 - Connectivity patterns shape sensory representation in a cerebellum-like network JF - bioRxiv DO - 10.1101/2021.02.10.430647 SP - 2021.02.10.430647 AU - Daniel Zavitz AU - Elom A. Amematsro AU - Alla Borisyuk AU - Sophie J.C. Caron Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/02/10/2021.02.10.430647.abstract N2 - Cerebellum-like structures are found in many brains and share a basic fan-out–fan-in network architecture. How the specific structural features of these networks give rise to their learning function remains largely unknown. To investigate this structure–function relationship, we developed a realistic computational model of an empirically very well-characterized cerebellum-like structure, the Drosophila melanogaster mushroom body. We show how well-defined connectivity patterns between the Kenyon cells, the constituent neurons of the mushroom body, and their input projection neurons enable different functions. First, biases in the likelihoods at which individual projection neurons connect to Kenyon cells allow the mushroom body to prioritize the learning of particular, ethologically meaningful odors. Second, groups of projection neurons connecting preferentially to the same Kenyon cells facilitate the mushroom body generalizing across similar odors. Altogether, our results demonstrate how different connectivity patterns shape the representation space of a cerebellum-like network and impact its learning outcomes.Competing Interest StatementThe authors have declared no competing interest. ER -