ArticleProceedings of the National Academy of Sciences of the United States of America2021
Compensatory variability in network parameters enhances memory performance in the
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Conflicting adaptations in an inhibitory feedback circuit.The Journal of physiology · 2026Article
- Ectopic sodium channel expression decreases excitability of Drosophila Kenyon cells.The Journal of physiology · 2025Article
- Sensory encoding and memory in the mushroom body: signals, noise, and variability.Learning & memory (Cold Spring Harbor, N.Y.) · 2024Review
- Effects of stochastic coding on olfactory discrimination in flies and mice.PLoS biology · 2023Article
- Article
- The cellular architecture of memory modules ineLife · 2023Article
- Article
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3 authors.
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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.
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