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Dataset results
16 results for “synaptic connection”
Heterogeneity of synaptic connectivity in the fly visual system
<p>Source data of the paper Cornean, Molina-Obando et al. 2024, Nature Communications. This work contains an analysis of synaptic connectivity in the Drosophila system, focusing on the presynaptic circuitry of three medulla interneurons, Tm9, Tm1, and Tm2.<br>Synaptic connectivity was analyzed using the FAFB dataset (Zheng et al. 2018 Cell) and the Flywire connectome (Schlegel et al. 2023 bioRxiv, Dorkenwald et al. 2023 bioRxiv), as well as expansion microscopy. This analysis is supplement by some functional analysis using in vivo 2-photon calcium imaging. <br><br>Connectomics data used for this study are provided as .xlsx and .text files containing raw and processed data. <br>Expansion microscopy are uploaded as .tiff files containing raw data, as well as .nrrd and .csv files containing processed data.<br>Calcium imaging data are provided at .mat files containing both raw and processed data, as well as .xml files with information about the experimental protocol.</p><p>Please find all relevant information to use the code in the README files.</p><p>The code to analyze the data, either written in Matlab or Python, is found at https://github.com/silieslab/Cornean_Molina-Obando_etal_2024.git</p>
Dataset of "Synaptic Response of Fluidic Nanopores: The Connection of Potentiation with Hysteresis"
<p>This dataset supports the article published in <em>ChemPhysChem</em>.</p> <p>"Synaptic Response of Fluidic Nanopores: The Connection of Potentiation with Hysteresis"</p> <p> </p> <p>Raw data for the article "Synaptic Response of Fluidic Nanopores: The Connection of Potentiation with Hysteresis". For further details see the readme.txt file.</p>
Input data and analyzed data of "Topology of synaptic connectivity constrains neuronal stimulus representation (...)"
<p>This dataset contains the input data, as well as the analyzed data that our <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">preprint</a></p> <p><em><strong>Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies</strong></em></p> <p>to be found on <a href="https://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">bioRxiv</a> is based on. The input data (<em>input_data.zip</em>) contains everything that is needed to run the full <a href="https://github.com/BlueBrain/topological_sampling/">analysis pipeline</a> from start to the generation of the figures found in the manuscript. However, some of the analysis steps can be computationally heavy, so we also provide the output of these expensive steps, that can be simply used in conjunction with jupyter notebooks (<em>notebooks.zip)</em> to generate the figures.</p> <p><strong>Overview</strong></p> <p>An overview image can be found <a href="https://raw.githubusercontent.com/BlueBrain/topological_sampling/master/toposampling_pipeline_overview.png"><strong>here</strong></a></p> <p>Blue squares denote input / output files (that are part of this dataset). Grey circles denote steps of the analysis pipeline (that are implemented in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>). Red rectangles denote configuration files (that are part of this dataset and also in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>).</p> <p>This Dataset can also be browsed, downloaded and accessed as linked open data from the <a href="https://bbp.epfl.ch/nexus/web/studios/public/topological-sampling/studios/data:a7cc7e9f-53c5-4940-929c-95f4c4f57728?workspaceId=data:165e54c5-e8f6-4d85-ac94-53bc3dfe5cd4">BBP knowledge Graph based Data studios</a>.</p> <p><strong>Contained file types and their structure</strong></p> <p>Here, we provide four types of files. Configuration files specify analysis parameters and define the expected locations of the data files. Input files are the inputs into the analysis pipeline. Analyzed files are the outputs of said pipeline. Finally, we provide a number of jupyter notebooks that use the analyzed files to generate the manuscript figures. If you want to re-run the entire analysis pipeline, you need the code and configuration files from the <a href="https://github.com/BlueBrain/topological_sampling/">repository</a>, the input files and notebooks; the analyzed files will be generated as you run the pipeline. For information how to run this, refer to the <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a>. If you only want to generate the figures, you still need the code and configuration files from the repository, as it contains a package related to reading the result files; further, you need the analyzed files in addition to the input files. Of course, you can also run parts of the analysis pipeline and download the outputs for the rest.</p> <p>To run everything smoothly, the files have to be placed into the expected file structure. You can look up and configure the file structure in the configuration files. Below, we describe the default layout, which is very simple (<em>root</em> is where you placed the code from our <a href="https://github.com/BlueBrain/topological_sampling/">repository</a> and can be any location on your file system):</p> <ul> <li>Configuration files<em>: </em>Part of the <a href="https://github.com/BlueBrain/topological_sampling/">repository.</a> Placed into <em>root/working_dir/configs</em></li> <li>Input data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>input_data.zip</em> -- Input data. Contains details on the model used in the manuscript and the output (spike times) of the simulation described in the manuscript. Within the file: <ul> <li>For details, see <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a></li> </ul> </li> </ul> </li> <li>Analyzed data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>classifier_features_results.zip </em>-- Output of the "classifier" step. Results of stimulus classification on the data in <em>features.zip</em></li> <li><em>classifier_manifold_result</em>s.zip -- Output of the "classifier" step. Results of stimulus classification on the data in <em>extracted_components.zip</em></li> <li><em>community_database.zip</em> -- Output of "gen_topo_db". Various topological parameters related to the close neighborhood of neurons in the model</li> <li><em>extracted_components.zip </em>-- Output of "manifold_analysis". Results of factor analysis on the spike times in the <em>input_data</em></li> <li><em>features.zip</em> -- Output of "topological_featurization". A new dimensionality reduction method we introduce in the <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">manuscript</a></li> <li><em>split_spike_trains.zip -- </em>Output of "split_time_windows". The spike trains, split into time windows that are the responses to individual stimuli injected in the simulation</li> <li><em>structural_parameters.zip</em><em> -- </em>Output of "Structural tribe analysis". Values for the topological parameters in <em>community_database.zip</em> associated with the neuron samples specified in <em>tribes.zip</em></li> <li><em>structural_parameters_vol.zip</em> -- Output of "Structural tribe analysis". Same as above, but for volumetric neuron samples.</li> <li><em>triads.zip</em> -- Output of "Triad-counts". Over- and under-expression of triad motifs in the samples in <em>tribes.zip</em>.</li> <li><em>tribes.zip</em><em> -- </em>Output of "sample_tribes". Specific neuron samples that are then analyzed further.</li> </ul> </li> <li>Notebooks: Place into <em>root/notebooks</em> and unzip in place <ul> <li><em>notebooks.zip</em><em> -- </em>Jupyter notebooks. Run them to generate the figures in the manuscript.</li> </ul> </li> </ul> <p> </p> <p><strong>Updates:</strong></p> <p>v1.1.0 (2020/12/11): Added some additional control cases to the results for figure 7. These results will probably not be updated on bioRxiv, but go into the submission to a journal.</p> <p>v1.2.0 (2021/10/05): Updated the notebooks.zip with changes we made in response to reviewers' feedback.</p>
Data from: Directed and acyclic synaptic connectivity in the human layer 2-3 cortical microcircuit
<p>The computational capabilities of neuronal networks are fundamentally constrained by their specific connectivity. Previous studies of cortical connectivity have been mostly carried out in rodents; however, whether the principles also apply to the evolutionary expanded human cortex is unclear. Here we studied network properties within the human temporal cortex using samples obtained from brain surgery. We analyzed multi-neuron patch-clamp recordings in layer 2-3 pyramidal neurons and identified substantial differences compared to rodents. Reciprocity showed random distribution, synaptic strength was independent from connection probability and connectivity of the supragranular temporal cortex followed a directed and mostly acyclic graph topology. Application of these principles in neuronal models increased the dimensionality of network dynamics suggesting a critical role for cortical computation.</p>
Target cell-specific synaptic dynamics of excitatory to inhibitory neuron connections in supragranular layers of human neocortex
<p>Rodent studies have demonstrated that synaptic dynamics from excitatory to inhibitory neuron types are often dependent on the target cell type. However, these target cell-specific properties have not been well investigated in human cortex, where there are major technical challenges in reliably obtaining healthy tissue, conducting multiple patch-clamp recordings on inhibitory cell types, and identifying those cell types. Here, we take advantage of newly developed methods for human neurosurgical tissue analysis with multiple patch-clamp recordings, <em>post-hoc</em> fluorescent <em>in situ</em> hybridization (FISH), machine learning-based cell type classification, and prospective GABAergic AAV-based labeling to investigate synaptic properties between pyramidal neurons and PVALB- vs. SST-positive interneurons. We find that there are robust molecular differences in synapse-associated genes between these neuron types, and that individual presynaptic pyramidal neurons evoke postsynaptic responses with heterogeneous synaptic dynamics in different postsynaptic cell types. Using molecular identification with FISH and classifiers based on transcriptomically identified PVALB neurons analyzed by Patch-seq, we find that PVALB neurons typically show depressing synaptic characteristics, whereas other interneuron types including SST-positive neurons show facilitating characteristics. Together, these data support the existence of target cell-specific synaptic properties in human cortex that are similar to rodents, thereby indicating evolutionary conservation of local circuit connectivity motifs from excitatory to inhibitory neurons and their synaptic dynamics.</p>
Data from: Combinatorial expression of gamma-protocadherins regulates synaptic connectivity in the mouse neocortex
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Target cell-specific synaptic dynamics of excitatory to inhibitory neuron connections in supragranular layers of human neocortex
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Data from: Directed and acyclic synaptic connectivity in the human layer 2-3 cortical microcircuit
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Synaptic connectivity and neuronal network activity changes after extracellular matrix depletion
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Ascertaining cells' synaptic connections and RNA expression simultaneously with barcoded rabies virus libraries
GEO Series GSE214386. Mus musculus. 156 samples. Type: Expression profiling by high throughput sequencing.
Promoter Decommissioning by the NuRD Chromatin Remodeling Complex Triggers Synaptic Connectivity in the Mammalian Brain
GEO Series GSE57758. Mus musculus. 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Regulation of synaptic connectivity in schizophrenia by mutual neuron-microglia interaction
GEO Series GSE213232. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Homeodomain proteins hierarchically specify neuronal diversity and synaptic connectivity
GEO Series GSE246726. Drosophila melanogaster. 3 samples. Type: Other.
Synaptic Injury and Functional Connectivity in Alzheimer's Disease
ClinicalTrials.gov study NCT03300726. IPD Sharing: NO. Countries: 1. Publications: 0.
Drosophila Fezf functions as a transcriptional repressor to direct layer specific synaptic connectivity in the fly visual system
GEO Series GSE163311. Drosophila melanogaster. 9 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Alterations in Retrotransposition, Synaptic Connectivity, and Myelination Implicated by Transcriptomic Changes Following Maternal Immune Activation in Nonhuman Primates
GEO Series GSE197605. Macaca mulatta. 52 samples. Type: Expression profiling by high throughput sequencing.
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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)
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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.