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2 Janelia Publications

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    Truman LabCardona LabZlatic LabFlyLightFly Facility
    03/23/20 | Recurrent architecture for adaptive regulation of learning in the insect brain.
    Eschbach C, Fushiki A, Winding M, Schneider-Mizell CM, Shao M, Arruda R, Eichler K, Valdes-Aleman J, Ohyama T, Thum AS, Gerber B, Fetter RD, Truman JW, Litwin-Kumar A, Cardona A, Zlatic M, Cardona A, Zlatic M
    Nature Neuroscience. 2020 Mar 23;23(4):544-55. doi: 10.1038/s41593-020-0607-9

    Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. We provide a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). We combine this with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. We find that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. Our study provides the most detailed view to date of biological circuit motifs that support associative learning.

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    Cardona LabFly Facility
    02/14/20 | Identifying neural substrates of competitive interactions and sequence transitions during mechanosensory responses in Drosophila.
    Masson J, Laurent F, Cardona A, Barre C, Skatchkovsky N, Zlatic M, Jovanic T
    PLoS Genetics. 2020 Feb 14;16(2):e1008589. doi: 10.1371/journal.pgen.1008589

    Nervous systems have the ability to select appropriate actions and action sequences in response to sensory cues. The circuit mechanisms by which nervous systems achieve choice, stability and transitions between behaviors are still incompletely understood. To identify neurons and brain areas involved in controlling these processes, we combined a large-scale neuronal inactivation screen with automated action detection in response to a mechanosensory cue in Drosophila larva. We analyzed behaviors from 2.9x105 larvae and identified 66 candidate lines for mechanosensory responses out of which 25 for competitive interactions between actions. We further characterize in detail the neurons in these lines and analyzed their connectivity using electron microscopy. We found the neurons in the mechanosensory network are located in different regions of the nervous system consistent with a distributed model of sensorimotor decision-making. These findings provide the basis for understanding how selection and transition between behaviors are controlled by the nervous system.

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