2021
DOI: 10.1073/pnas.2102158118
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Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body

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, … Show more

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Cited by 9 publications
(9 citation statements)
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“…Such a model is supported by a recent computational study demonstrating that variability in parameters controlling neuronal excitability of individual KCs negatively affects associative memory performance. The authors provide evidence that compensatory variation mechanisms exist that ensure similar activity levels between all odor-encoding KC sets to maintain efficient memory performance ( Abdelrahman et al, 2021 ).…”
Section: Discussionmentioning
confidence: 99%
“…Such a model is supported by a recent computational study demonstrating that variability in parameters controlling neuronal excitability of individual KCs negatively affects associative memory performance. The authors provide evidence that compensatory variation mechanisms exist that ensure similar activity levels between all odor-encoding KC sets to maintain efficient memory performance ( Abdelrahman et al, 2021 ).…”
Section: Discussionmentioning
confidence: 99%
“…The same question is waiting to be addressed for other neuromodulators as well, such as DPM-derived serotonin that may extend dopamine’s functional plasticity window within the KC axon ( Zeng et al, 2023 ). Such investigation will also be of value for future modeling studies, which to this point must assume simple spiking of KCs and equal or random weights to KC-to-MBON synapses ( Smith et al, 2008 ; Huerta and Nowotny, 2009 ; Peng and Chittka, 2017 ; Abdelrahman et al, 2021 ; Hafez et al, 2023 ). The intersection of these approaches will be especially important for understanding the subcellular mechanisms of learning-related processes, particularly as they appear to be distributed across functional units, whether they be compartmentalized axon bundles or boutons along an individual axon.…”
Section: Discussionmentioning
confidence: 99%
“…Importantly, by comparing the CS+ and CS- responses to those of untrained odors, we ascribed plasticity to potentiation or depression (accounting for any non-associative olfactory adaptation) within each compartment. This is relevant for modeling efforts, where it has been unclear whether to include potentiation (along with depression) in the learning rule(s) at KC-MBON synapses ( Abdelrahman et al, 2021 ; Bennett et al, 2021 ; Jiang and Litwin-Kumar, 2021 ; Springer and Nawrot, 2021 ). In addition, the experiments revealed an additional layer of spatial regulation in the γ1–γ3 compartments: a gradient of CS+ potentiation to CS- depression following appetitive conditioning.…”
Section: Discussionmentioning
confidence: 99%