Local synaptic inputs support opposing, network-specific odor representations in a widely projecting modulatory neuron
Abstract
Serotonin plays different roles across networks within the same sensory modality. Previously, we used whole-cell electrophysiology in Drosophila to show that serotonergic neurons innervating the first olfactory relay are inhibited by odorants (Zhang and Gaudry, 2016). Here we show that network-spanning serotonergic neurons segregate information about stimulus features, odor intensity and identity, by using opposing coding schemes in different olfactory neuropil. A pair of serotonergic neurons (the CSDns) innervate the antennal lobe and lateral horn, which are first and second order neuropils. CSDn processes in the antennal lobe are inhibited by odors in an identity independent manner. In the lateral horn, CSDn processes are excited in an odor identity dependent manner. Using functional imaging, modeling, and EM reconstruction, we demonstrate that antennal lobe derived inhibition arises from local GABAergic inputs and acts as a means of gain control on branch specific inputs that the CSDns receive within the lateral horn.
Data availability
All data is publicly available at https://drive.google.com/drive/folders/1KVh6aIQ6S_MWyuY7JOwXZ0efgrReFTPv?usp=sharing
Article and author information
Author details
Funding
Whitehall Foundation
- Quentin Gaudry
National Institute on Deafness and Other Communication Disorders (R21 DC015873-02)
- Quentin Gaudry
National Institute on Deafness and Other Communication Disorders (RO1 DC016293)
- Andrew M Dacks
- Quentin Gaudry
Georgia Gwinnett College VPASA Seed Fund
- Cengiz Günay
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Mani Ramaswami, Trinity College Dublin, Ireland
Version history
- Received: March 14, 2019
- Accepted: July 1, 2019
- Accepted Manuscript published: July 2, 2019 (version 1)
- Version of Record published: July 26, 2019 (version 2)
Copyright
© 2019, Zhang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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