Abdelrahman, N.Y., Vasilaki, E. and Lin, A.C. orcid.org/0000-0001-6310-9765 (2021) Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body. Proceedings of the National Academy of Sciences, 118 (49). e2102158118. ISSN 0027-8424
Abstract
Neural circuits use homeostatic compensation to achieve consistent behavior despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual and what computational benefits might result from such compensation. We address these questions in the Drosophila mushroom body, the fly’s olfactory memory center. In a computational model, we show that under sparse coding conditions, memory performance is degraded when the mushroom body’s principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model’s compensatory mechanisms appear in the Drosophila hemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) - see: https://creativecommons.org/licenses/by/4.0/. |
Keywords: | Drosophila; associative memory; homeostatic plasticity; mushroom body |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Dec 2021 08:27 |
Last Modified: | 10 Feb 2023 14:12 |
Status: | Published |
Publisher: | National Academy of Sciences |
Refereed: | Yes |
Identification Number: | 10.1073/pnas.2102158118 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:181348 |
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