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5,448 results for “Neuron”
Data from: Mirrored STDP implements autoencoder learning in a network of spiking neurons
The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.
Dataset for PointTree: Automatic and accurate reconstruction of long-range axonal projections of single-neuron
<p dir="auto">If you find our work useful in your research, please consider citing our paper:</p> <div> <pre><code>Cai Lin, Fan Taiyu, Qu Xuzhong, Zhang Ying, Gou Xianyu, Ding Quanwei, Feng Weihua, Cao Tingting, Lv Xiaohua, Liu Xiuli, Huang Qing, Quan Tingwei, Zeng Shaoqun (2024) PointTree: Automatic and accurate reconstruction of long-range axonal projections of single-neuron eLife 13:RP102840 https://doi.org/10.7554/eLife.102840.2</code></pre> </div>
Whole-brain Neuron Reconstructions for Building Neuronal Connections at Multi-levels
<p>The dataset for construct connection networks of single neurons, including 1,877 neuron reconstructions, predicted bouton data, and 18,370 neurons. All neuronal morphologies were resampled with a spacing of 1 µm between consecutive points to ensure uniform reconstruction of the structures. The generated connectomes are also archived on the repository.</p> <p>1877-CELL_METADATA.xlsx: metadata of 1,877 neurons;</p> <p>SEU_1um.zip: 1,877 neuron reconstructions (full morphology);</p> <p>DEN-18370.zip: 1,877 neuron reconstructions (dendritic morphology);</p> <p>predicted_bouton_locations.zip: predicted bouton data (only coordinates and the corresponding neurons);</p> <p>jupyter tutorial: to generate connectivity matices of connectomes, and perform related analysis;</p> <p>arbor-net connections: arbor-net, single-neuron connectivity;</p> <p>bouton-net connections: bouton-net, single-neuron connectivity;</p> <p>all bouton from F1877: processed bouton locations, including their coordinates and brain regions;</p> <p>PACs from F1877: PACs from F1877 dataset, including coordinates, soma locations, and region of locations;</p> <p>SEU_net_w_corticalLayers and SEU_net_wo_corticalLayers: SEU-net brain region connectivity w or wo cortical layers.</p>
Figure 1 from: Nugroho A, Tamtomo DG, Indarto D, Cilmiaty R, Soetrisno (2024) The effect of L-Arginine from Giant Snake Head fish (Channa micropeltes) on neuroinflammation and neuron damage in traumatic brain injury in rats. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e111239
Figure 1 Western blotting analysis and relative expression of GSDMD to Beta Actin.
Data from: Progression of neuronal damage in an in vitro model of the ischemic penumbra
Improvement of neuronal recovery in the ischemic penumbra around a brain infarct has a large potential to advance clinical recovery of patients with acute ischemic stroke. However, pathophysiological mechanisms leading to either recovery or secondary damage in the penumbra are not completely understood. We studied neuronal dynamics in a model system of the penumbra consisting of networks of cultured cortical neurons exposed to controlled levels and durations of hypoxia. Short periods of hypoxia (pO2≈20mmHg) reduced spontaneous activity, due to impeded synaptic function. After ≈6 hours, activity and connectivity partially recovered, even during continuing hypoxia. If the oxygen supply was restored within 12 hours, changes in network connectivity were completely reversible. For longer periods of hypoxia (12–30 h), activity levels initially increased, but eventually decreased and connectivity changes became partially irreversible. After ≈30 hours, all functional connections disappeared and no activity remained. Since this complete silence seemed unrelated to hypoxic depths, but always followed an extended period of low activity, we speculate that irreversible damage (at least partly) results from insufficient neuronal activation. This opens avenues for therapies to improve recovery by neuronal activation.
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>
Data from: Face-selective neurons in the vicinity of the human Fusiform Face Area
No abstract entered
Figure 1 from: Polilov AA (2017) First record of Megaphragma (Hymenoptera, Trichogrammatidae) in Columbia, and third animal species known to have anucleate neurons. Journal of Hymenoptera Research 60: 181-185. https://doi.org/10.3897/jhr.60.19907
Figure 1 - Habitus of Megaphragma caribea, SEM: A dorsal view B lateral view.
Reconstructed neuron meshes from the Ciona connectome
<p>This file contains the reconstructed <em>Ciona</em> neuron meshes from the Ciona connectome. (<a href="https://doi.org/10.7554/eLife.16962">Ryan 2016</a>) The meshes are packaged to load in Unity 2021.3.</p>
Neuronal Uptake Live Imaging Data 2N4R Tau
<p>Live imaging videos for neuronal uptake of 2N4R tau glycoproteins labeled with 10% pHrodo-red at 50 nM concentration over 24 h (brigtfield, fluorescence, and merge channels).</p>
Optogenetic stimulation of kisspeptin neurones within the posterodorsal medial amygdala increases LH pulse frequency in female mice
<p>Kisspeptin within the arcuate nucleus of the hypothalamus is a critical neuropeptide in the regulation of reproduction. Together with neurokinin B and dynorphin A, arcuate kisspeptin provides the oscillatory activity that drives the pulsatile secretion of GnRH, and therefore LH pulses, and is believed to be a central component of the GnRH pulse generator. It is well established that the amygdala also exerts an influence over gonadotrophic hormone secretion and reproductive physiology. The discovery of kisspeptin and its receptor within the posterodorsal medial amygdala (MePD), and our recent finding showing that intra-MePD administration of kisspeptin or a kisspeptin receptor antagonist results in increased LH secretion and decreased LH pulse frequency, respectively, suggests an important role for amygdala kisspeptin signalling in the regulation of the GnRH pulse generator. To further investigate the function of amygdala kisspeptin, the present study used an optogenetic approach to selectively stimulate MePD kisspeptin neurones and examine the effect on pulsatile LH secretion. MePD kisspeptin neurones in conscious Kiss1-Cre mice were virally infected to express the channelrhodopsin 2 protein and selectively stimulated by light via a chronically implanted fibre optic cannula. Continuous stimulation using 5 Hz resulted in an increased LH pulse frequency, which was not observed at the lower stimulation frequencies of 0.5 and 2 Hz. In wild-type animals, continuous stimulation at 5 Hz did not affect LH pulse frequency. These results demonstrate that selective activation of MePD <em>Kiss1</em> neurons can modulate hypothalamic GnRH pulse generator frequency.</p>
Supplemental Data for "LRRK2 kinase activity regulates lysosomal glucocerebrosidase activity in neurons derived from Parkinson's disease patients"
<p>Sequencing of <em>GBA1</em> gene (all exons) and <em>LRRK2</em> (Exon 31 and 41) for all lines included in the manuscript: LRRK2 kinase activity regulates lysosomal glucocerebrosidase in neurons derived from Parkinson's disease patients. We do not observed unexpected point mutations in these genes in non-mutant cell lines. Primers for exon amplification and sequencing of GBA1 were obtained from Stone et al., 2000. PCR conditions were obtained from Lesage et al., 2011, Neumann et al., 2009, and Stone et al., 2000. </p>
Dendritic morphologies of human cortical neurons and Supplementary Materials for comparative studies in mice and humans
<p>This dataset includes local dendritic reconstructions of 2,363 human cortical neurons from the frontal, temporal, and parietal lobes. Together with the related Supplementary Materials in the paper. </p>
ROSMAP meQTL Results for Neurons with CpGs
<p>This dataset contains methylation quantitative trait loci (meQTL) results for the following study:</p> <p><strong><em>"regionalpcs improve discovery of DNA methylation associations with complex traits"</em></strong></p> <p>Tiffany Eulalio*<sup>1</sup>, Min Woo Sun<sup>1</sup>, Olivier Gevaert<sup>1</sup>, Michael D. Greicius<sup>2</sup>, Thomas J. Montine<sup>3</sup>, Daniel Nachun*‡<sup>3</sup>, Stephen B. Montgomery*‡<sup>1,3</sup></p> <p>‡ These authors contributed equally as senior authors</p> <p>* Corresponding authors: Tiffany Eulalio (<a href="mailto:eulalio@alumn.stanford.edu">eulalio@alumn.stanford.edu</a>), Daniel Nachun (<a href="mailto:dnachun@stanford.edu">dnachun@stanford.edu</a>), Stephen B. Montgomery (<a href="mailto:smontgom@stanford.edu">smontgom@stanford.edu</a>)</p> <p> Author affiliations:</p> <p>1. Department of Biomedical Data Science, Stanford University, Stanford, CA</p> <p>2. Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA</p> <p>3. Department of Pathology, Stanford University, Stanford, CA</p> <p> </p> <p><strong>Dataset description</strong>:</p> <p>This dataset contains QTL results generated from FastQTL, organized by region type (full gene, gene body, preTSS, and promoters) and summary types (averages and regional principal components).</p> <p><strong>Contents:</strong></p> <ul> <li><strong>Parquet tar files</strong>: These compressed archives contain output files in Parquet format from FastQTL, split by chromosome. <code>parquet1</code> includes chromosomes 1-10, and <code>parquet2</code> includes chromosomes 11-22.</li> <li><strong>cis_qtl_summary_stats.csv</strong>: Provides summary statistics for each phenotype-variant pair, including effect sizes, p-values, TSS distances, and additional details.</li> <li><strong>cis_qtl.signif_pairs.csv</strong>: Contains the significant QTL results identified by FastQTL.</li> <li><strong>cis_qtls.time_to_run.txt</strong>: Reports the running time for FastQTL analysis.</li> <li><strong>cis_qtls.cis_qtl.txt.gz</strong>: Contains comprehensive results for all cis QTLs.</li> </ul> <p>This dataset is intended to support replication and further exploration of QTL associations across different genomic regions and summary methods.</p>
Dataset: Profiling Neuronal Methylome and Hydroxymethylome of Opioid Use Disorder in the Human Orbitofrontal Cortex
<p>Methylation and Hydroxymethylation data.</p>
Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically-inspired neural network
<p>Data published along with paper: <em>Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically-inspired neural network</em></p> <p>In this data is shared: models (before & after training), stimuli used to train and test, and activity of the model in test.</p>
Treating Parkinson's Disease Through Transplantation of Autologous Stem Cell-Derived Dopaminergic Neurons
ClinicalTrials.gov study NCT06687837. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Neuron Specific Enolase in Ventricular Assist Device Recipients
ClinicalTrials.gov study NCT01643551. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Non-invasive Brain Stimulation and Exercise Intervention for Patients With Motor Neuron Disease
ClinicalTrials.gov study NCT07067229. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of Safety and Tolerability of Fetal Mesencephalic Dopamine Neuronal Precursor Cells for Parkinson's Disease
ClinicalTrials.gov study NCT01860794. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.