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28 Janelia Publications

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    11/18/20 | Spatial readout of visual looming in the central brain of Drosophila.
    Morimoto MM, Nern A, Zhao A, Rogers EM, Wong A, Isaacson MD, Bock D, Rubin GM, Reiser MB
    eLife. 2020 Nov 18;9:. doi: 10.7554/eLife.57685

    Visual systems can exploit spatial correlations in the visual scene by using retinotopy. However, retinotopy is often lost, such as when visual pathways are integrated with other sensory modalities. How is spatial information processed outside of strictly visual brain areas? Here, we focused on visual looming responsive LC6 cells in , a population whose dendrites collectively cover the visual field, but whose axons form a single glomerulus-a structure without obvious retinotopic organization-in the central brain. We identified multiple cell types downstream of LC6 in the glomerulus and found that they more strongly respond to looming in different portions of the visual field, unexpectedly preserving spatial information. Through EM reconstruction of all LC6 synaptic inputs to the glomerulus, we found that LC6 and downstream cell types form circuits within the glomerulus that enable spatial readout of visual features and contralateral suppression-mechanisms that transform visual information for behavioral control.

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    01/15/20 | A genetic, genomic, and computational resource for exploring neural circuit function.
    Davis FP, Nern A, Picard S, Reiser MB, Rubin GM, Eddy SR, Henry GL
    eLife. 2020 Jan 15;9:. doi: 10.7554/eLife.50901

    The anatomy of many neural circuits is being characterized with increasing resolution, but their molecular properties remain mostly unknown. Here, we characterize gene expression patterns in distinct neural cell types of the visual system using genetic lines to access individual cell types, the TAPIN-seq method to measure their transcriptomes, and a probabilistic method to interpret these measurements. We used these tools to build a resource of high-resolution transcriptomes for 100 driver lines covering 67 cell types, available at Combining these transcriptomes with recently reported connectomes helps characterize how information is transmitted and processed across a range of scales, from individual synapses to circuit pathways. We describe examples that include identifying neurotransmitters, including cases of apparent co-release, generating functional hypotheses based on receptor expression, as well as identifying strong commonalities between different cell types.

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    12/11/19 | The computation of directional selectivity in the OFF motion pathway.
    Gruntman E, Romani S, Reiser MB
    eLife. 2019 Dec 11;8:. doi: 10.7554/eLife.50706

    In flies, the direction of moving ON and OFF features is computed separately. T4 (ON) and T5 (OFF) are the first neurons in their respective pathways to extract a directionally selective response from their non-selective inputs. Our recent study of T4 found that the integration of offset depolarizing and hyperpolarizing inputs is critical for the generation of directional selectivity. However, T5s lack small-field inhibitory inputs, suggesting they may use a different mechanism. Here we used whole-cell recordings of T5 neurons and found a similar receptive field structure: fast depolarization and persistent, spatially offset hyperpolarization. By assaying pairwise interactions of local stimulation across the receptive field, we found no amplifying responses, only suppressive responses to the non-preferred motion direction. We then evaluated passive, biophysical models and found that a model using direct inhibition, but not the removal of excitation, can accurately predict T5 responses to a range of moving stimuli.

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    01/09/19 | Nuclear transcriptomes of the seven neuronal cell types that constitute the mushroom bodies.
    Shih MM, Davis FP, Henry GL, Dubnau J
    G3 (Bethesda, Md.). 2019 Jan 09;9(1):81-94. doi: 10.1534/g3.118.200726

    The insect mushroom body (MB) is a conserved brain structure that plays key roles in a diverse array of behaviors. The MB is the primary invertebrate model of neural circuits related to memory formation and storage, and its development, morphology, wiring, and function has been extensively studied. MBs consist of intrinsic Kenyon Cells that are divided into three major neuron classes (γ, α'/β' and α/β) and 7 cell subtypes (γd, γm, α'/β'ap, α'/β'm, α/βp, α/βs and α/βc) based on their birth order, morphology, and connectivity. These subtypes play distinct roles in memory processing, however the underlying transcriptional differences are unknown. Here, we used RNA sequencing (RNA-seq) to profile the nuclear transcriptomes of each MB neuronal cell subtypes. We identified 350 MB class- or subtype-specific genes, including the widely used α/β class marker and the α'/β' class marker Immunostaining corroborates the RNA-seq measurements at the protein level for several cases. Importantly, our data provide a full accounting of the neurotransmitter receptors, transporters, neurotransmitter biosynthetic enzymes, neuropeptides, and neuropeptide receptors expressed within each of these cell types. This high-quality, cell type-level transcriptome catalog for the MB provides a valuable resource for the fly neuroscience community.

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    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.

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    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.

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    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.

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    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.

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    Reiser LabRubin LabFly Functional Connectome
    12/18/17 | Behavioral state modulates the ON visual motion pathway of Drosophila.
    Strother JA, Wu S, Rogers EM, Eliason JL, Wong AM, Nern A, Reiser MB
    Proceedings of the National Academy of Sciences of the United States of America. 2017 Dec 18;115(1):E102-11. doi: 10.1073/pnas.1703090115

    The behavioral state of an animal can dynamically modulate visual processing. In flies, the behavioral state is known to alter the temporal tuning of neurons that carry visual motion information into the central brain. However, where this modulation occurs and how it tunes the properties of this neural circuit are not well understood. Here, we show that the behavioral state alters the baseline activity levels and the temporal tuning of the first directionally selective neuron in the ON motion pathway (T4) as well as its primary input neurons (Mi1, Tm3, Mi4, Mi9). These effects are especially prominent in the inhibitory neuron Mi4, and we show that central octopaminergic neurons provide input to Mi4 and increase its excitability. We further show that octopamine neurons are required for sustained behavioral responses to fast-moving, but not slow-moving, visual stimuli in walking flies. These results indicate that behavioral-state modulation acts directly on the inputs to the directionally selective neurons and supports efficient neural coding of motion stimuli.

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    11/08/17 | Ultra-selective looming detection from radial motion opponency.
    Klapoetke NC, Nern A, Peek MY, Rogers EM, Breads P, Rubin GM, Reiser MB, Card GM
    Nature. 2017 Nov 08;551(7679):237-241. doi: 10.1038/nature24626

    Nervous systems combine lower-level sensory signals to detect higher-order stimulus features critical to survival, such as the visual looming motion created by an imminent collision or approaching predator. Looming-sensitive neurons have been identified in diverse animal species. Different large-scale visual features such as looming often share local cues, which means loom-detecting neurons face the challenge of rejecting confounding stimuli. Here we report the discovery of an ultra-selective looming detecting neuron, lobula plate/lobula columnar, type II (LPLC2) in Drosophila, and show how its selectivity is established by radial motion opponency. In the fly visual system, directionally selective small-field neurons called T4 and T5 form a spatial map in the lobula plate, where they each terminate in one of four retinotopic layers, such that each layer responds to motion in a different cardinal direction. Single-cell anatomical analysis reveals that each arm of the LPLC2 cross-shaped primary dendrites ramifies in one of these layers and extends along that layer's preferred motion direction. In vivo calcium imaging demonstrates that, as their shape predicts, individual LPLC2 neurons respond strongly to outward motion emanating from the centre of the neuron's receptive field. Each dendritic arm also receives local inhibitory inputs directionally selective for inward motion opposing the excitation. This radial motion opponency generates a balance of excitation and inhibition that makes LPLC2 non-responsive to related patterns of motion such as contraction, wide-field rotation or luminance change. As a population, LPLC2 neurons densely cover visual space and terminate onto the giant fibre descending neurons, which drive the jump muscle motor neuron to trigger an escape take off. Our findings provide a mechanistic description of the selective feature detection that flies use to discern and escape looming threats.

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