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135 Publications

Showing 31-40 of 135 results
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    Looger LabJayaraman LabSvoboda LabSchreiter Lab
    06/17/19 | High-performance calcium sensors for imaging activity in neuronal populations and microcompartments.
    Dana H, Sun Y, Mohar B, Hulse BK, Kerlin AM, Hasseman JP, Tsegaye G, Tsang A, Wong A, Patel R, Macklin JJ, Chen Y, Konnerth A, Jayaraman V, Looger LL, Schreiter ER, Svoboda K, Kim DS
    Nature Methods. 2019 Jun 17;16(7):649-57. doi: 10.1038/s41592-019-0435-6

    Calcium imaging with genetically encoded calcium indicators (GECIs) is routinely used to measure neural activity in intact nervous systems. GECIs are frequently used in one of two different modes: to track activity in large populations of neuronal cell bodies, or to follow dynamics in subcellular compartments such as axons, dendrites and individual synaptic compartments. Despite major advances, calcium imaging is still limited by the biophysical properties of existing GECIs, including affinity, signal-to-noise ratio, rise and decay kinetics and dynamic range. Using structure-guided mutagenesis and neuron-based screening, we optimized the green fluorescent protein-based GECI GCaMP6 for different modes of in vivo imaging. The resulting jGCaMP7 sensors provide improved detection of individual spikes (jGCaMP7s,f), imaging in neurites and neuropil (jGCaMP7b), and may allow tracking larger populations of neurons using two-photon (jGCaMP7s,f) or wide-field (jGCaMP7c) imaging.

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    Svoboda LabMouseLight
    03/12/19 | Single-neuron axonal reconstruction: The search for a wiring diagram of the brain.
    Economo MN, Winnubst J, Bas E, Ferreira TA, Chandrashekar J
    The Journal of Comparative Neurology. 2019 Mar 12:. doi: 10.1002/cne.24674

    Reconstruction of the axonal projection patterns of single neurons has been an important tool for understanding both the diversity of cell types in the brain and the logic of information flow between brain regions. Innovative approaches now enable the complete reconstruction of axonal projection patterns of individual neurons with vastly increased throughput. Here we review how advances in genetic, imaging, and computational techniques have been exploited for axonal reconstruction. We also discuss how new innovations could enable the integration of genetic and physiological information with axonal morphology for producing a census of cell types in the mammalian brain at scale. This article is protected by copyright. All rights reserved.

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    Svoboda Lab
    05/15/19 | Prediction of choice from competing mechanosensory and choice-memory cues during active tactile decision making.
    Campagner D, Evans MH, Chlebikova K, Colins-Rodriguez A, Loft MS, Fox S, Pettifer D, Humphries MD, Svoboda K, Petersen RS
    The Journal of Neuroscience : the official journal of the Society for Neuroscience. 2019 May 15;39(20):3921-33. doi: 10.1523/JNEUROSCI.2217-18.2019

    Perceptual decision making is an active process where animals move their sense organs to extract task-relevant information. To investigate how the brain translates sensory input into decisions during active sensation, we developed a mouse active touch task where the mechanosensory input can be precisely measured and that challenges animals to use multiple mechanosensory cues. Male mice were trained to localise a pole using a single whisker and to report their decision by selecting one of three choices. Using high-speed imaging and machine vision we estimated whisker-object mechanical forces at millisecond resolution. Mice solved the task by a sensory-motor strategy where both the strength and direction of whisker bending were informative cues to pole location. We found competing influences of immediate sensory input and choice memory on mouse choice. On correct trials, choice could be predicted from the direction and strength of whisker bending, but not from previous choice. In contrast, on error trials, choice could be predicted from previous choice but not from whisker bending. This study shows that animal choices during active tactile decision making can be predicted from mechanosenory and choice-memory signals; and provides a new task, well-suited for future study of the neural basis of active perceptual decisions.Due to the difficulty of measuring the sensory input to moving sense organs, active perceptual decision making remains poorly understood. The whisker system provides a way forward since it is now possible to measure the mechanical forces due to whisker-object contact during behaviour. Here we train mice in a novel behavioural task that challenges them to use rich mechanosensory cues, but can be performed using one whisker and enables task-relevant mechanical forces to be precisely estimated. This approach enables rigorous study of how sensory cues translate into action during active, perceptual decision making. Our findings provide new insight into active touch and how sensory/internal signals interact to determine behavioural choices.

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    Svoboda Lab
    02/21/19 | Principles governing the dynamics of GABAergic interneurons in the barrel cortex.
    Yu J, Hu H, Agmon A, Svoboda K
    bioRxiv. 2019 Feb 21:. doi: 10.1101/554949

    Information processing in the neocortex is performed by GABAergic interneurons that are integrated with excitatory neurons into precisely structured circuits. To reveal how each neuron type shapes sensory representations, we measured spikes and membrane potential of specific types of neurons in the barrel cortex while mice performed an active, whisker-dependent object localization task. Whiskers were tracked with millisecond precision. Fast-spiking (FS) neurons were activated by touch with short latency and by whisking. FS neurons track thalamic input and provide feedforward inhibition. Somatostatin (SOM)-expressing neurons were also excited by touch, but with a delay (5 ms) compared to excitatory (E) and FS neurons. SOM neurons monitor local excitation and provide feedback inhibition. Vasoactive intestinal polypeptide (VIP)-expressing neurons were not driven by touch but elevated their spike rate during whisking, disinhibiting E and FS neurons. Our data reveal rules of recruitment for specific interneuron types, providing foundations for understanding cortical computations.

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    Romani LabSvoboda Lab
    02/06/19 | Discrete attractor dynamics underlies persistent activity in the frontal cortex.
    Inagaki HK, Fontolan L, Romani S, Svoboda K
    Nature. 2019 Feb 06;566(7743):212-7. doi: 10.1038/s41586-019-0919-7

    Short-term memories link events separated in time, such as past sensation and future actions. Short-term memories are correlated with slow neural dynamics, including selective persistent activity, which can be maintained over seconds. In a delayed response task that requires short-term memory, neurons in the mouse anterior lateral motor cortex (ALM) show persistent activity that instructs future actions. To determine the principles that underlie this persistent activity, here we combined intracellular and extracellular electrophysiology with optogenetic perturbations and network modelling. We show that during the delay epoch, the activity of ALM neurons moved towards discrete end points that correspond to specific movement directions. These end points were robust to transient shifts in ALM activity caused by optogenetic perturbations. Perturbations occasionally switched the population dynamics to the other end point, followed by incorrect actions. Our results show that discrete attractor dynamics underlie short-term memory related to motor planning.

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    Svoboda Lab
    01/17/19 | NWB:N 2.0: An accessible data standard for neurophysiology.
    Rubel O, Tritt A, Dichter B, Braun T, Cain N, Clack NG, Davidson TJ, Dougherty M, Fillion-Rubin J, Graddis N, Grauer M, Kiggins JT, Niu L, Ozturk D, Schroeder W, Soltesz I, Sommer FT, Svoboda K, Ng L, et al
    bioRxiv. 2019 Jan 17:. doi: 10.1101/523035

    Neurodata Without Borders: Neurophysiology (NWB:N) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data. With NWB:N version 2.0 (NWB:N 2.0) we made significant advances towards creating a usable standard, software ecosystem, and vibrant community for standardizing neurophysiology data. In this manuscript we focus in particular on the NWB:N data standard schema and present advances towards creating an accessible data standard for neurophysiology.

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    Svoboda LabDruckmann LabScientific Computing Software
    01/15/19 | An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability.
    Wei Z, Inagaki H, Li N, Svoboda K, Druckmann S
    Nature Communications. 2019 Jan 15;10(1):216. doi: 10.1038/s41467-018-08141-6

    Animals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is to understand the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population activity must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g., in decision making), heterogeneity across neurons and limited sampling of the relevant neural population. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics is able to reveal previously unrecognized structure in the organization of population activity. This structure is similar on error and correct trials, suggesting dynamics that may be constrained by the underlying circuitry, is able to predict multiple aspects of behavioral variability and reveals long time-scale modulation of population activity.

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    Svoboda Lab
    11/23/18 | Paring down to the essentials.
    Wang T
    Science (New York, N.Y.). 2018 Nov 23;362(6417):904. doi: 10.1126/science.aav6872
    Looger LabSvoboda LabMouseLightQuantitative Genomics
    10/31/18 | Distinct descending motor cortex pathways and their roles in movement.
    Economo MN, Viswanathan S, Tasic B, Bas E, Winnubst J, Menon V, Graybuck LT, Nguyen TN, Smith KA, Yao Z, Wang L, Gerfen CR, Chandrashekar J, Zeng H, Looger LL, Svoboda K
    Nature. 2018 Nov;563(7729):79-84. doi: 10.1038/s41586-018-0642-9

    Activity in the motor cortex predicts movements, seconds before they are initiated. This preparatory activity has been observed across cortical layers, including in descending pyramidal tract neurons in layer 5. A key question is how preparatory activity is maintained without causing movement, and is ultimately converted to a motor command to trigger appropriate movements. Here, using single-cell transcriptional profiling and axonal reconstructions, we identify two types of pyramidal tract neuron. Both types project to several targets in the basal ganglia and brainstem. One type projects to thalamic regions that connect back to motor cortex; populations of these neurons produced early preparatory activity that persisted until the movement was initiated. The second type projects to motor centres in the medulla and mainly produced late preparatory activity and motor commands. These results indicate that two types of motor cortex output neurons have specialized roles in motor control.

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    Looger LabSvoboda Lab
    10/31/18 | Shared and distinct transcriptomic cell types across neocortical areas.
    Tasic B, Yao Z, Graybuck LT, Smith KA, Nguyen TN, Bertagnolli D, Goldy J, Garren E, Economo MN, Viswanathan S, Penn O, Bakken T, Menon V, Miller J, Fong O, Hirokawa KE, Lathia K, Rimorin C, Tieu M, Larsen R, Casper T, Barkan E, Kroll M, Parry S, Shapovalova NV, Hirschstein D, Pendergraft J, Sullivan HA, Kim TK, Szafer A, Dee N, Groblewski P, Wickersham I, Cetin A, Harris JA, Levi BP, Sunkin SM, Madisen L, Daigle TL, Looger L, Bernard A, Phillips J, Lein E, Hawrylycz M, Svoboda K, Jones AR, Koch C, Zeng H
    Nature. 2018 Nov;563(7729):72-78. doi: 10.1038/s41586-018-0654-5

    The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.

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