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High-resolution electron microscopy (EM) of nervous systems enables the reconstruction of neural circuits at the level of individual synaptic connections. However, for invertebrates, such as Drosophila melanogaster, it has so far been unclear whether the phenotype of neurons or synapses alone is sufficient to predict specific functional properties such as neurotransmitter identity. Here, we show that in Drosophila melanogaster artificial convolutional neural networks can confidently predict the type of neurotransmitter released at a synaptic site from EM images alone. The network successfully discriminates between six types of neurotransmitters (GABA, glutamate, acetylcholine, serotonin, dopamine, and octopamine) with an average accuracy of 87% for individual synapses and 94% for entire neurons, assuming each neuron expresses only one neurotransmitter. This result is surprising as there are often no obvious cues in the EM images that human observers can use to predict neurotransmitter identity. We apply the proposed method to quantify whether, similar to the ventral nervous system (VNS), all hemilineages in the Drosophila melanogaster brain express only one fast acting transmitter within their neurons. To test this principle, we predict the neurotransmitter identity of all identified synapses in 89 hemilineages in the Drosophila melanogaster adult brain. While the majority of our predictions show homogeneity of fast-acting neurotransmitter identity within a single hemilineage, we identify a set of hemilineages that express two fast-acting neurotransmitters with high statistical significance. As a result, our predictions are inconsistent with the hypothesis that all neurons within a hemilineage express the same fast-acting neurotransmitter in the brain of Drosophila melanogaster.
bioRxiv PrePrint https://doi.org/10.1101/2020.06.12.148775