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229 results for “synapse”
Cortical slice labelled with anti GFP and VAMP2 antibodies - sample image for software testing of "Contacting synapse" protocol
<p><strong>Image 1.tif is a Brain slice</strong>. This 16 bits confocal stack of pictures ((801x711 pixels x33 z slices - pixel size 78.17 nm) of a brain slice has been taken at 93x (LeicaHC PL APO CS2 93x/1.30 GLYC) in sequential mode with two channels : one dedicated to the GFP detection, and the other one to synpatic boutons labelled with VAMP2 protein. VAMP2 protein are expressed at glutamatergic presynaptic sites and is usually found apposed to Post Synaptic Density. This is a good sample to test "contacting synapse" software. Here GFP cells were electroporated with various plasmid. The aim of the software is to identify if expression of those plasmid within the GFP labelled cell, influence the density of synapse contacting this GFP cells. Here presynaptic contact are identified through the use of antibodies to VAMP2 proteins.</p>
Dataset of "Introduction to neuromorphic functions of memristors: The inductive nature of synapse potentiation"
<p>This dataset supports the article "Introduction to Neuromorphic Functions of Memristors: The Inductive Nature of Synapse Potentiation," published in the Journal of Applied Physics.</p> <p>Raw data for the article "Introduction to neuromorphic functions of memristors: The inductive nature of synapse potentiation". For further details see the Readme.txt file.</p>
cgiotis/palimpsest_memories: Dataset and Code Material for "Palimpsest Memories Stored in Memristive Synapses" Manuscript."
<p>Dataset and code material for the "Palimpsest Memories Stored in Memristive Synapses" manuscript, currently available on ArXiv. This repository currently contains instructions and the necessary dataset to recreate Figures 1 and 2 from the paper, as well as parametric code to recreate the simulation results and generate the relevant plots (Figures 3 and 4).</p>
Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
<p>Dataset linked to the article with the same title</p> <p>The model itself is very similar to its non-plastic counterpart under the following DOI: <a href="../record/7930275">10.5281/zenodo.7930275</a>, i.e. a 1.5 mm diameter cortical tissue comprising 211,712 neurons and their connectivity in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas. It's formatted in the open <a href="https://github.com/AllenInstitute/sonata">SONATA</a> standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity (with all their anatomical and physiological parameters). The main difference from the non-plastic version is the addition of plasticity related parameters to <em>O1/S1nonbarrel_neurons__S1nonbarrel_neurons__chemical/edges.h5. </em>Extrinsic synaptic connections from the thalamus are included in this release, but for inputs from neurons in the remainder of non-barrel somatosensory cortex please see the non-plastic version of the circuit.</p> <p><strong>Analyzing the model</strong></p> <p>The model can be analyzed in terms of its anatomy, physiology and connectivity using the packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a>, <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a> and <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>. (see first Jupyter notebook)</p> <p><strong>Simulating the model</strong></p> <p>To simulate the model we'd recommend using out using our open-source simulator <a href="https://github.com/BlueBrain/neurodamus">Neurodamus</a>. The reference version is the branch <em>nbS1-2023</em>, which is archived under the following DOI: <a href="http://doi.org/10.5281/zenodo.8075202">10.5281/zenodo.8075202</a>. Instructions on how to use the simulator are provided on the GitHub page linked above. Briefly, you'll first have to <a href="https://github.com/BlueBrain/neurodamus#install-neurodamus">install Neurodamus</a>. Next, build a <em>"special"</em> executable that include compiled versions of ion channel and synapse models. To do that, follow <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files">these instructions</a>, where <em>mod-files-from-released-circuit </em>is replaced by the location of <em>O1/mods</em> on your system. Finally, <a href="https://github.com/BlueBrain/neurodamus#examples">run a simulation</a>. The specific simulation conditions and stimuli are specified in simulation configuration files. An exemplary simulation configuration is included in this release (<em>simulation_config.zip</em>).</p> <p><strong>Analyzing simulation results</strong></p> <p>Simulation results can be analyzed with <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a>, <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>, and <a href="https://github.com/BlueBrain/assemblyfire">assemblyfire</a>. Notebooks 2-5 go though these analysis and recreate some of the panels from our article. In most cases the notebooks can be run with the shared HDF5 files and don't require running any simulations.</p> <p><strong>Version 2</strong></p> <p>Bug fix in simulation_config.json and therefore new version of results (and corresponding notebooks). The underlying circuit model (O1.xz) did not change from v1.</p> <p>--</p> <p><em>The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.</em></p>
synapse
<p>This dataset uses the CK OO metrics.</p> <p>More information at http://openscience.us/repo/defect/ck/synapse.html</p>
All-Optical Data Processing with Photon-Avalanching Nanocrystalline Photonic Synapse
<h2>Abstract</h2><p>Data processing and storage in electronic devices are typically performed as a sequence of elementary binary operations. Alternative approaches, such as neuromorphic or reservoir computing, are rapidly gaining interest where data processing is relatively slow, but can be performed in a more comprehensive way or massively in parallel, like in neuronal circuits. Here, time-domain all-optical information processing capabilities of photon-avalanching (PA) nanoparticles at room temperature are discovered. Demonstrated functionality resembles properties found in neuronal synapses, such as: paired-pulse facilitation and short-term internal memory, in situ plasticity, multiple inputs processing, and all-or-nothing threshold response. The PA-memory-like behavior shows capability of machine-learning-algorithm-free feature extraction and further recognition of 2D patterns with simple 2 input artificial neural network. Additionally, high nonlinearity of luminescence intensity in response to photoexcitation mimics and enhances spike-timing-dependent plasticity that is coherent in nature with the way a sound source is localized in animal neuronal circuits. Not only are yet unexplored fundamental properties of photon-avalanche luminescence kinetics studied, but this approach, combined with recent achievements in photonics, light confinement and guiding, promises all-optical data processing, control, adaptive responsivity, and storage on photonic chips.</p>
Image quantification data for: Activity-dependent mitochondrial ROS signaling regulates recruitment of glutamate receptors to synapses
<p>Our understanding of mitochondrial signaling in the nervous system has been limited by the technical challenge of analyzing mitochondrial function <em>in vivo</em>. In the transparent genetic model <em>Caenorhabditis elegans, </em>we were able to manipulate and measure mitochondrial ROS (reactive oxygen species) signaling of individual mitochondria as well as neuronal activity of single neurons <em>in vivo</em>. Using this approach, we provide evidence supporting a novel role for mitochondrial ROS signaling in dendrites of excitatory glutamatergic <em>C. elegans</em> interneurons. Specifically, we show that following neuronal activity, dendritic mitochondria take up calcium (Ca<sup>2+</sup>) via the mitochondrial Ca<sup>2+</sup> uniporter MCU-1 which results in an upregulation of mitochondrial ROS production. We also observed that mitochondria are positioned in close proximity to synaptic clusters of GLR-1, the <em>C. elegans</em> ortholog of the AMPA subtype of glutamate receptors that mediate neuronal excitation. We show that synaptic recruitment of GLR-1 is upregulated when MCU-1 function is pharmacologically or genetically impaired but is downregulated by mitoROS signaling. Thus, signaling from postsynaptic mitochondria may regulate excitatory synapse function to maintain neuronal homeostasis by preventing excitotoxicity and energy depletion.</p>
Vesicular release probability sets the strength of individual Schaffer collateral synapses
<p>This repository contains the data used to generate the figures of the manuscript entitled 'Vesicular release probability sets the strength of individual Schaffer collateral synapses' and all the data used for the quantal analysis.</p>
Raw data accompanying the manuscript "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse". The data files are 3D image stacks of a custom-built wide field deconvolution fluorescence microscope (.tif) and super-resolution structured illumination microscopy data (.dv) of Jurkat T cells transferring HIV-1 virus particles to previously uninfected primary T cells.</p>
Single-synapse analyses of Alzheimer's disease implicate pathologic tau, DJ1, CD47, and ApoE
<p>Synaptic molecular characterization is limited for Alzheimer's disease (AD). Our newly invented mass cytometry-based method, Synaptometry by Time of Flight (SynTOF), was used to measure 38 antibody probes in approximately 17 million single-synapse events from human brains without pathologic change or with pure AD or Lewy body disease (LBD), non-human primates (NHP), and PS/APP mice. Synaptic molecular integrity in humans and NHP was similar. Although not detected in human synapses, Aβ was in PS/APP mice single-synapse events. Clustering and pattern identification of human synapses showed expected disease-specific differences, like increased hippocampal pathologic tau in AD and reduced caudate dopamine transporter in LBD, and revealed novel findings including increased hippocampal CD47 and lowered DJ1 in AD and higher ApoE in AD with dementia. Our results were independently supported by multiplex ion beam imaging of intact tissue. This highlights the higher depth and breadth of insight on neurodegenerative diseases obtainable through SynTOF.</p>
Mechanisms of simultaneous linear and nonlinear computations at the mammalian cone photoreceptor synapse
<p>Neurons enhance their computational power by combining linear and nonlinear transformations in extended dendritic trees. Rich, spatially distributed processing is rarely associated with individual synapses, but the cone photoreceptor synapse may be an exception. Graded voltages temporally modulate vesicle fusion at a cone's ~20 ribbon active zones. The transmitter then flows into a common, glia-free volume where bipolar cell dendrites are organized by type in successive tiers. Using super-resolution microscopy and tracking vesicle fusion and postsynaptic response at the quantal level in the thirteen-lined ground squirrel, <em>Ictidomys</em> <em>tridecemlineatus</em>, we show that certain bipolar cell types respond to individual fusion events in the stream while other types respond to degrees of locally coincident events, creating a gradient across tiers that are increasingly nonlinear. Nonlinearities emerge from a combination of factors specific to each bipolar cell type including diffusion distance, contact number, receptor affinity, and proximity to transporters. Complex computations related to feature detection begin within the first visual synapse.</p>
Activity-dependent mitochondrial ROS signaling regulates recruitment of glutamate receptors to synapses
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Data from: Presynaptic Nrxn3 is essential for ribbon-synapse maturation in hair cells
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Mechanisms of simultaneous linear and nonlinear computations at the mammalian cone photoreceptor synapse
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Data and code from: Distinct transmission sites within a synapse for strengthening and homeostasis
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An imaging flow cytometry dataset for profiling the immunological synapse of therapeutic antibodies
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Data from: Kif1a and intact microtubules maintain synaptic-vesicle populations at ribbon synapses in zebrafish hair cells
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Sleep deprivation drives brain-wide changes in cholinergic pre-synapse abundance in Drosophila melanogaster
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Single-synapse analyses of Alzheimer’s disease implicate pathologic tau, DJ1, CD47, and ApoE
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Dataset of Axonal Synapses, Acquired using a Two-photon Microscope in the Live Mouse Cortex
<p>This Dataset consists of TIFF 100 images, split in 20 test and 80 training images, of axons with their synapses (boutons) labelled. The labels are in form of ground-truth binary images of the same size, in which the corresponding synapses have been labelled as boxes.</p> <p>This data was collected in the live mouse cortex, using a two-photon microscope, with a 40x objective, at zoom 4, with a resolution of 512 x 512 x 0.147 microns per pixel, and a Point Spread Function characterised by a Full Width at Half Maximum (FWHM) values of 0.45 x 0.45 x 2.5 microns (x, y, z). </p> <p> </p> <p><strong>Please cite the following paper when using this dataset:</strong></p> <p>Bass C, Helkkula P, De Paola V, Clopath C, Bharath AA. Detection of axonal synapses in 3D two-photon images. Giniger E, ed. <em>PLoS ONE</em>. 2017;12(9):e0183309. doi:10.1371/journal.pone.0183309.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.