Open AccessCompensatory variability in network parameters enhances memory performance in the Drosophila mushroom body
Author(s) -
Nada Y. Abdelrahman,
Eleni Vasilaki,
Andrew C. Lin
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2102158118
Subject(s) - mushroom bodies , neuroscience , biology , memory formation , biological neural network , psychology , drosophila melanogaster , hippocampus , gene , genetics
Significance How does variability between neurons affect neural circuit function? How might neurons behave similarly despite having different underlying features? We addressed these questions in neurons called Kenyon cells, which store olfactory memories in flies. Kenyon cells differ among themselves in key features that affect how active they are, and in a model of the fly’s memory circuit, adding this interneuronal variability made the model fly worse at learning the values of multiple odors. However, memory performance was rescued if compensation between the variable underlying features allowed Kenyon cells to be equally active on average, and we found the hypothesized compensatory variability in real Kenyon cells’ anatomy. This work reveals the existence and computational benefits of compensatory variability in neural networks.