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Learning with naturalistic odor representations in a dynamic model of the Drosophila olfactory system

Kennedy, Ann (2019) Learning with naturalistic odor representations in a dynamic model of the Drosophila olfactory system. . (Unpublished)

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Many odor receptors in the insect olfactory system are broadly tuned, yet insects can form associative memories that are odor-specific. The key site of associative olfactory learning in insects, the mushroom body, contains a population of Kenyon Cells (KCs) that form sparse representations of odor identity and enable associative learning of odors by mushroom body output neurons (MBONs). This architecture is well suited to odor-specific associative learning if KC responses to odors are uncorrelated with each other, however it is unclear whether this hold for actual KC representations of natural odors. We introduce a dynamic model of the Drosophila olfactory system that predicts the responses of KCs to a panel of 110 natural and monomolecular odors, and examine the generalization properties of associative learning in model MBONs. While model KC representations of odors are often quite correlated, we identify mechanisms by which odor-specific associative learning is still possible.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper ItemCode for simulating and analyzing the spiking mushroom body model
Kennedy, Ann0000-0002-3782-0518
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license. The author is grateful to L.F. Abbott, Richard Axel, Daisuke Hattori, Peter Wang, and Glenn C. Turner for many helpful conversations during the development of this model, and Elizabeth J. Hong and Vanessa Ruta for their comments and feedback during the preparation of this manuscript. The author was supported by postdoctoral fellowships from the Swartz Foundation and Helen Hay Whitney Foundation. Code Availability: Code for building and simulating all versions of the model and code for learning/generalization investigations is provided with documentation at Competing interests: None declared.
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Swartz FoundationUNSPECIFIED
Helen Hay Whitney FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20191001-104800394
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Official Citation:Learning with naturalistic odor representations in a dynamic model of the Drosophila olfactory system Ann Kennedy bioRxiv 783191; doi:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98983
Deposited By: George Porter
Deposited On:01 Oct 2019 20:49
Last Modified:16 Nov 2021 17:43

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