journal.pcbi.1009583.pdf (6.6 MB)
Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies
Version 2 2023-06-12, 08:10
Version 1 2023-06-10, 01:37
journal contribution
posted on 2023-06-12, 08:10 authored by Mario Pannunzi, Thomas NowotnyThomas NowotnyWhen flies explore their environment, they encounter odors in complex, highly intermittent plumes. To navigate a plume and, for example, find food, they must solve several challenges, including reliably identifying mixtures of odorants and their intensities, and discriminating odorant mixtures emanating from a single source from odorants emitted from separate sources and just mixing in the air. Lateral inhibition in the antennal lobe is commonly understood to help solving these challenges. With a computational model of the Drosophila olfactory system, we analyze the utility of an alternative mechanism for solving them: Non-synaptic (“ephaptic”) interactions (NSIs) between olfactory receptor neurons that are stereotypically co-housed in the same sensilla. We find that NSIs improve mixture ratio detection and plume structure sensing and do so more efficiently than the traditionally considered mechanism of lateral inhibition in the antennal lobe. The best performance is achieved when both mechanisms work in synergy. However, we also found that NSIs decrease the dynamic range of co-housed ORNs, especially when they have similar sensitivity to an odorant. These results shed light, from a functional perspective, on the role of NSIs, which are normally avoided between neurons, for instance by myelination.
Funding
Odor-background segregation and source localization using fast olfactory processing; G1652; HUMAN FRONTIER SCIENCE PROGRAM (HFSP); RGP0053/2015
Human Brain Project Specific Grant Agreement 2 — HBP SGA2; G2410; EUROPEAN UNION; 785907
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- Published
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- Published version
Journal
PLoS Computational BiologyISSN
1553-734XPublisher
Public Library of ScienceExternal DOI
Issue
12Volume
17Page range
1-34Article number
a1009583Department affiliated with
- Informatics Publications
Research groups affiliated with
- Centre for Computational Neuroscience and Robotics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2021-11-03First Open Access (FOA) Date
2021-12-14First Compliant Deposit (FCD) Date
2021-11-03Usage metrics
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