Author ORCID Identifier
https://orcid.org/0000-0003-3181-3648
https://orcid.org/0000-0003-2592-8908
https://orcid.org/0000-0002-6805-4211
https://orcid.org/0000-0001-7586-571X
N/A
https://orcid.org/0000-0002-6658-9175
N/A
Document Type
Article
Publication Date
2019
Department/Program/Center
Biology
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.
Digital Commons Citation
Zhang, Xiaonan; Coates, Kaylynn; Dacks, Andrew; Günay, Cengiz; Lauritzen, J. Scott; Li, Feng; Calle-Schuler, Steven A.; Bock, Davi; and Gaudry, Quentin, "Local Synaptic Inputs Support Opposing, Network-Specific Odor Representations in a Widely Projecting Modulatory Neuron" (2019). Faculty & Staff Scholarship. 1963.
https://researchrepository.wvu.edu/faculty_publications/1963
Source Citation
Zhang, X., Coates, K., Dacks, A., Günay, C., Lauritzen, J. S., Li, F., Calle-Schuler, S. A., Bock, D., & Gaudry, Q. (2019). Local synaptic inputs support opposing, network-specific odor representations in a widely projecting modulatory neuron. eLife, 8. https://doi.org/10.7554/elife.46839
Comments
© 2019, Zhang et al.
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