Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster.

ABSTRACT: High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also anal

SUBMITTER: Eckstein N 

PROVIDER: S-EPMC11106717 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature