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9 results for “SSCX”
Connectivity matrix of the internal connectivity of a SSCX model
<p>This is a simplified view of the <i>internal</i> synaptic connectivity of the model deposited under <a href="https://zenodo.org/records/8026353">https://zenodo.org/records/8026353.</a></p><p>While in this view a lot of data is lost compared to the detailed representation linked above (such as the pre- and postsynaptic locations of synaptic contacts and their physiology), it can still be used for structural connectomics analyses.</p><p>The data is in a format that can be loaded using our <a href="https://github.com/BlueBrain/ConnectomeUtilities"><i>Connectome_Utilities</i></a> package. We have converted the internal connectivity of the <a href="https://www.microns-explorer.org/">MICrONS</a> dataset into the same format (<a href="https://zenodo.org/records/8364070">found here</a>), allowing researchers to easily run the same analysis scripts on both datasets.</p>
Rewired connectome of an SSCX model with inhibitory targeting based on trends found in MICrONS data
<p>This is a rewired connectome of the internal synaptic connectivity of the model deposited under <a href="../records/7930275" target="_blank" rel="noopener">https://zenodo.org/records/7930275</a>.</p> <p>In the original model, local connectivity is based on axo-dendritic overlaps combined with a pruning rule, which together are known to be able to recreate biological trends in excitatory subnetworks. By comparison with the <a href="https://www.microns-explorer.org/cortical-mm3" target="_blank" rel="noopener">MICrONS</a> dataset we found that the inhibitory trends characterized by <a href="https://www.biorxiv.org/content/10.1101/2023.01.23.525290v3" target="_blank" rel="noopener">Schneider-Mizell et al. (2023)</a> can be reproduced with some additional, relatively simple pruning rules (see accompanying <a href="https://www.biorxiv.org/content/10.1101/2022.08.11.503144" target="_blank" rel="noopener">anatomy publication</a>), and created a rewired version of the connectome in <a href="https://github.com/AllenInstitute/sonata" target="_blank" rel="noopener">SONATA</a> format (edges.h5) that reproduces these trends. We refer to this as the Schneider-Mizell compatible SM-connectome in the accompanying <a href="https://www.biorxiv.org/content/10.1101/2023.05.17.541168" target="_blank" rel="noopener">physiology publication</a>.</p> <p>Note that for technical reasons, all <em>afferent_segment_...</em> and <em>efferent_...</em> synapse properties (which are not required for running simulations) were set to zero in the rewired pathways.</p> <p><strong>[Update 08/05/2024 - v3]:</strong> Conductances of individual synapses were recalibrated to preserve pathway-specific reference PSP amplitudes. The <em>afferent_section_type</em> and <em>efferent_section_type</em> synapse properties were previously off by 1 and were fixed. Missing synaptic input compensation was recalibrated to obtain target firing rates and added to this release.</p> <p><strong>[Update 28/02/2024 - v2]:</strong> Source m-type L1_NGC-SA included</p>
Rewired SSCx network model with 2nd-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 2nd-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 3rd-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 3rd-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 1st-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 1st-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 4th-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 4th-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
SSCx toy circuit with 6k neurons in SONATA format
<p>This dataset contains a toy circuit model of the rat somatosensory cortex (SSCx) in <a href="https://doi.org/10.1371/journal.pcbi.1007696">SONATA</a> format with 5,924 neurons and 568,717 (structural: 10,071,815) synapses. The toy circuit represents all six layers of the shoulder regions (S1Sh) of the primary somatosensory cortex (S1), but built with a reduced neuron density of only 7% of its biological density.</p> <p>Its intended use cases are testing, benchmarking, and tutorials of connectome manipulation and simulation experiments, such as the ones outlined under <code>/examples</code> in the <a href="https://doi.org/10.1101/2024.05.24.593860" target="_blank" rel="noopener">Connectome-Manipulator</a> software repository: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Three circuit configs are provided:</p> <ul> <li><code>circuit_config.json</code> ... Circuit with one nodes population "All" containing all 5,924 neurons and one edges population "default" containing all 568,717 synapses</li> <li><code>circuit_config__no_conn.json</code> ... Circuit with one nodes population "All" containing all 5,924 neurons but no edges population, i.e., no connectivity</li> <li><code>struct_circuit_config.json</code> ... Circuit with one nodes population "All" containing all 5,924 neurons and one edges population "default" containing all 10,071,815 structural synapses</li> </ul> <p>ℹ️ For running network simulations, follow the simulation instructions of a larger model of SSCx in <a href="https://doi.org/10.1371/journal.pcbi.1007696">SONATA</a> format released under DOI <a href="https://doi.org/10.5281/zenodo.7930275">10.5281/zenodo.7930275</a>.</p> <p><strong>[Update v2 - 10/12/2024]:</strong> Structural connectome (i.e., all synapses w/o pruning) added under <code>ToyCircuit-S1-6k-struct.xz</code></p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 5th-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 5th-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Common neighbor bias in the different regions of the Blue Brain rat SSCX model
<p>The "common neighbor bias" refers to the tendency of neurons with many graph-theoretical common neighbors to be more likely to be connected to each other. This concept can be split into a bias for afferent and efferent common neighbors.</p> <p>We quantify it (for a given neuron) as the mean number of common neighbors with connected neurons, divided by the mean number of common neighbors with all other neurons. Further distinction can be made by limiting this analysis to pairs of given neuron types.</p> <p>In this dataset, we report samples of such common neighbor biases in different regions of the rat SSCX model of Blue Brain. We report biases separately for afferent and efferent common neighbors, and for different pre- and postsynaptic neuron types. Results are lists, where each entry in a list is a result for a single neuron of the indicated pre-synaptic neuron type, quantifying how it affects it connectivity to neurons of the indicated post-synaptic type. A value of 1.0 indicates no bias.</p>
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International Brain Laboratory public data
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OpenNeuro
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