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A multilayer circuit architecture for the generation of distinct locomotor behaviors in Drosophila

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Animals generate diverse motor behaviors, yet how the same motor neurons (MNs) generate two distinct or antagonistic behaviors remains an open question. Here, we characterize Drosophila larval muscle activity patterns and premotor/motor circuits to understand how they generate forward and backward locomotion. We show that all body wall MNs are activated during both behaviors, but a subset of MNs change recruitment timing for each behavior. We used TEM to reconstruct a full segment of all 60 MNs and 236 premotor neurons (PMNs), including differentially-recruited MNs. Analysis of this comprehensive connectome identified PMN-MN ‘labeled line’ connectivity; PMN-MN combinatorial connectivity; asymmetric neuronal morphology; and PMN-MN circuit motifs that could all contribute to generating distinct behaviors. We generated a recurrent network model that reproduced the observed behaviors, and used functional optogenetics to validate selected model predictions. This PMN-MN connectome will provide a foundation for analyzing the full suite of larval behaviors.
Title: A multilayer circuit architecture for the generation of distinct locomotor behaviors in Drosophila
Description:
Animals generate diverse motor behaviors, yet how the same motor neurons (MNs) generate two distinct or antagonistic behaviors remains an open question.
Here, we characterize Drosophila larval muscle activity patterns and premotor/motor circuits to understand how they generate forward and backward locomotion.
We show that all body wall MNs are activated during both behaviors, but a subset of MNs change recruitment timing for each behavior.
We used TEM to reconstruct a full segment of all 60 MNs and 236 premotor neurons (PMNs), including differentially-recruited MNs.
Analysis of this comprehensive connectome identified PMN-MN ‘labeled line’ connectivity; PMN-MN combinatorial connectivity; asymmetric neuronal morphology; and PMN-MN circuit motifs that could all contribute to generating distinct behaviors.
We generated a recurrent network model that reproduced the observed behaviors, and used functional optogenetics to validate selected model predictions.
This PMN-MN connectome will provide a foundation for analyzing the full suite of larval behaviors.

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