Main Menu (Mobile)- Block

Main Menu - Block

janelia7_blocks-janelia7_secondary_menu | block
janelia7_blocks-janelia7_fake_breadcrumb | block
Reiser Lab / Publications
custom | custom

Filter

facetapi-Q2b17qCsTdECvJIqZJgYMaGsr8vANl1n | block
facetapi-PV5lg7xuz68EAY8eakJzrcmwtdGEnxR0 | block
facetapi-021SKYQnqXW6ODq5W5dPAFEDBaEJubhN | block

Type of Publication

general_search_page-panel_pane_1 | views_panes

4 Publications

Showing 1-4 of 4 results
Your Criteria:
    11/06/18 | A GAL80 collection to inhibit GAL4 transgenes in olfactory sensory neurons.
    Eliason J, Afify A, Potter C, Matsumura L
    G3 (Bethesda, Md.). 2018 Nov 06;8(11):3661-3668. doi: 10.1534/g3.118.200569

    Fruit flies recognize hundreds of ecologically relevant odors and respond appropriately to them. The complexity, redundancy and interconnectedness of the olfactory machinery complicate efforts to pinpoint the functional contributions of any component neuron or receptor to behavior. Some contributions can only be elucidated in flies that carry multiple mutations and transgenes, but the production of such flies is currently labor-intensive and time-consuming. Here, we describe a set of transgenic flies that express the GAL80 in specific olfactory sensory neurons (). The GAL80s effectively and specifically subtract the activities of GAL4-driven transgenes that impart anatomical and physiological phenotypes. can allow researchers to efficiently activate only one or a few types of functional neurons in an otherwise nonfunctional olfactory background. Such experiments will improve our understanding of the mechanistic connections between odorant inputs and behavioral outputs at the resolution of only a few functional neurons.

    View Publication Page
    06/12/18 | A connectome based hexagonal lattice convolutional network model of the Drosophila visual system.
    Tschopp FD, Reiser MB, Turaga SC
    arXiv. 2018 Jun 12:1806.04793

    What can we learn from a connectome? We constructed a simplified model of the first two stages of the fly visual system, the lamina and medulla. The resulting hexagonal lattice convolutional network was trained using backpropagation through time to perform object tracking in natural scene videos. Networks initialized with weights from connectome reconstructions automatically discovered well-known orientation and direction selectivity properties in T4 neurons and their inputs, while networks initialized at random did not. Our work is the first demonstration, that knowledge of the connectome can enable in silico predictions of the functional properties of individual neurons in a circuit, leading to an understanding of circuit function from structure alone.

    View Publication Page
    04/02/18 | Colour vision: A fresh view of lateral inhibition in Drosophila.
    Longden KD
    Current Biology : CB. 2018 Apr 02;28(7):R308-R311. doi: 10.1016/j.cub.2018.02.052

    A recent study reports a novel form of lateral inhibition between photoreceptors supporting colour vision in the vinegar fly, Drosophila melanogaster.

    View Publication Page
    01/08/18 | Simple integration of fast excitation and offset, delayed inhibition computes directional selectivity in Drosophila.
    Gruntman E, Romani S, Reiser MB
    Nature Neuroscience. 2018 Jan 08;21(2):250-7. doi: 10.1038/s41593-017-0046-4

    A neuron that extracts directionally selective motion information from upstream signals lacking this selectivity must compare visual responses from spatially offset inputs. Distinguishing among prevailing algorithmic models for this computation requires measuring fast neuronal activity and inhibition. In the Drosophila melanogaster visual system, a fourth-order neuron-T4-is the first cell type in the ON pathway to exhibit directionally selective signals. Here we use in vivo whole-cell recordings of T4 to show that directional selectivity originates from simple integration of spatially offset fast excitatory and slow inhibitory inputs, resulting in a suppression of responses to the nonpreferred motion direction. We constructed a passive, conductance-based model of a T4 cell that accurately predicts the neuron's response to moving stimuli. These results connect the known circuit anatomy of the motion pathway to the algorithmic mechanism by which the direction of motion is computed.

    View Publication Page