FB2024_03 , released June 25, 2024
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Citation
Tsai, K.T., Hu, C.K., Li, K.W., Hwang, W.L., Chou, Y.H. (2018). Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity.  Sci. Rep. 8(1): 8027.
FlyBase ID
FBrf0239016
Publication Type
Research paper
Abstract
Local interneurons (LNs) in the Drosophila olfactory system exhibit neuronal diversity and variability, yet it is still unknown how these features impact information encoding capacity and reliability in a complex LN network. We employed two strategies to construct a diverse excitatory-inhibitory neural network beginning with a ring network structure and then introduced distinct types of inhibitory interneurons and circuit variability to the simulated network. The continuity of activity within the node ensemble (oscillation pattern) was used as a readout to describe the temporal dynamics of network activity. We found that inhibitory interneurons enhance the encoding capacity by protecting the network from extremely short activation periods when the network wiring complexity is very high. In addition, distinct types of interneurons have differential effects on encoding capacity and reliability. Circuit variability may enhance the encoding reliability, with or without compromising encoding capacity. Therefore, we have described how circuit variability of interneurons may interact with excitatory-inhibitory diversity to enhance the encoding capacity and distinguishability of neural networks. In this work, we evaluate the effects of different types and degrees of connection diversity on a ring model, which may simulate interneuron networks in the Drosophila olfactory system or other biological systems.
PubMed ID
PubMed Central ID
PMC5966413 (PMC) (EuropePMC)
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    Language of Publication
    English
    Additional Languages of Abstract
    Parent Publication
    Publication Type
    Journal
    Abbreviation
    Sci. Rep.
    Title
    Scientific reports
    ISBN/ISSN
    2045-2322
    Data From Reference
    Alleles (4)
    Genes (2)
    Insertions (4)