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.

Recent advances in DNA sequencing technology and DNA storage systems have rekindled the interest in deletion channels. Multiple recent works have looked at variants of sequence reconstruction over a single and over multiple deletion channels, a notoriously difficult problem due to its highly combinatorial nature. Although works in theoretical computer science have provided algorithms which guarantee perfect reconstruction with multiple independent observations from the deletion channel, they are only applicable in the large blocklength regime and more restrictively, when the number of observations is also large. Indeed, with only a few observations, perfect reconstruction of the input sequence may not even be possible in most cases. In such situations, maximum likelihood (ML) and maximum aposteriori (MAP) estimates for the deletion channels are natural questions that arise and these have remained open to the best of our knowledge. In this work, we take steps to answer the two aforementioned questions. Specifically: 1. We show that solving for the ML estimate over the single deletion channel (which can be cast as a discrete optimization problem) is equivalent to solving its relaxation, a continuous optimization problem; 2. We exactly compute the symbolwise posterior distributions (under some assumptions on the priors) for both the single as well as multiple deletion channels. As part of our contributions, we also introduce tools to visualize and analyze error events, which we believe could be useful in other related problems concerning deletion channels.

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