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3 Janelia Publications

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    11/10/21 | Circuits for integrating learned and innate valences in the insect brain.
    Eschbach C, Fushiki A, Winding M, Afonso B, Andrade IV, Cocanougher BT, Eichler K, Gepner R, Si G, Valdes-Aleman J, Fetter RD, Gershow M, Jefferis GS, Samuel AD, Truman JW, Cardona A, Zlatic M
    eLife. 2021 Nov 10;10:. doi: 10.7554/eLife.62567

    Animal behavior is shaped both by evolution and by individual experience. Parallel brain pathways encode innate and learned valences of cues, but the way in which they are integrated during action-selection is not well understood. We used electron microscopy to comprehensively map with synaptic resolution all neurons downstream of all Mushroom Body output neurons (encoding learned valences) and characterized their patterns of interaction with Lateral Horn neurons (encoding innate valences) in larva. The connectome revealed multiple types that receive convergent Mushroom Body and Lateral Horn inputs. A subset of these receives excitatory input from positive-valence MB and LH pathways and inhibitory input from negative-valence MB pathways. We confirmed functional connectivity from LH and MB pathways and behavioral roles of two of these neurons. These neurons encode integrated odor value and bidirectionally regulate turning. Based on this we speculate that learning could potentially skew the balance of excitation and inhibition onto these neurons and thereby modulate turning. Together, our study provides insights into the circuits that integrate learned and innate valences to modify behavior.

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    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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    07/26/17 | Recent progress in the 3D reconstruction of Drosophila neural circuits.
    Shinomiya K, Ito M
    Decoding Neural Circuit Structure and Function:63-89. doi: 10.1007/978-3-319-57363-2_3

    The brain of fruit fly Drosophila melanogaster has been used as a model system for functional analysis of neuronal circuits, including connectomics research, due to its modest size (~700 μm) and availability of abundant molecular genetics tools for visualizing neurons. Three-dimensional (3D) reconstruction of high-resolution images of neurons or circuits visualized with appropriate methods is a critical step for obtaining information such as morphology and connectivity patterns of neuronal circuits. In this chapter, we introduce methods for generating 3D reconstructed images with data acquired from confocal laser scanning microscopy (CLSM) or electron microscopy (EM) to analyze neuronal circuits found in the central nervous system (CNS) of the fruit fly. Comparisons of different algorithms and strategies for reconstructing neuronal circuits, using actual studies as references, will be discussed within this chapter.

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