Abstract
The hemibrain connectome provides large-scale connectivity and morphology information for the majority of the central brain of Drosophila melanogaster. Using this data set, we provide a complete description of the Drosophila olfactory system, covering all first, second and lateral horn-associated third-order neurons. We develop a generally applicable strategy to extract information flow and layered organisation from connectome graphs, mapping olfactory input to descending interneurons. This identifies a range of motifs including highly lateralised circuits in the antennal lobe and patterns of convergence downstream of the mushroom body and lateral horn. Leveraging a second data set we provide a first quantitative assessment of inter- versus intra-individual stereotypy. Comparing neurons across two brains (three hemispheres) reveals striking similarity in neuronal morphology across brains. Connectivity correlates with morphology and neurons of the same morphological type show similar connection variability within the same brain as across two brains.
Highlights
By providing a full account of neurons and networks at synaptic resolution, connectomics can form and inform testable hypotheses for nervous system function
The Janelia hemibrain data set comprises most of the right hemisphere of the central brain of an adult female fly and contains ~25,000 largely complete neurons; neurons were automatically segmented and proofread by humans recovering on average ~39% of their synaptic connectivity (Scheffer et al, 2020)
We find that compared to ORNs TRN/HRNs spend more of their output budget on connections to antennal lobe projection neurons (ALPNs) (41% vs. 34%), and this difference seems to be mostly accounted for by the very low level of axo- axonic TRN/HRN to as follows: first-o rder receptor neurons (ALRNs) connectivity (Figure 3D)
Summary
By providing a full account of neurons and networks at synaptic resolution, connectomics can form and inform testable hypotheses for nervous system function. This approach is most powerful when applied at a whole-b rain scale. Until very recently, the handful of whole-b rain connectomics data sets have either been restricted to complete nervous systems of a few hundred neurons (i.e. nematode worm; White et al, 1986) and Ciona tadpole (Ryan et al, 2016) or to the sparse tracing of specific circuits, as in larval and adult Drosophila (Zheng et al, 2018; Ohyama et al, 2015). We develop new software, analytical tools and integration strategies, and apply them to annotate and analyse a full sensory connectome
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