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5,448 results for “Neurons”
Language intensity classification and Neuronal Networks
<p>Kohonen Self-Organizing Maps (SOM) are a particular type of artificial neural network created by Teuvo Kohonen. Their unsupervised learning makes them suitable for application, among other things, to grouping tasks. Occupations have been grouped into this analysis considering the similarity in value of the variables that define this occupation in terms of language proficiency requirements. The 8 variables used were: v1-speaking skills, v2-writing skills, v3-speech clarity, v4-speech recognition, v5-English knowledge, v6-speaking ability, v7-communication with people outsiders, v8-communication with superiors, equals or subordinates. After applying this authomatic classification technique, the SOC occupations are classified according to five distinc groups with decreasing linguistic intensity:</p> <p>Class 1: High linguistic intenisty requirements<br> Class 2: Medium-high linguistic intenisty requirements<br> Class 3: Medium linguistic intenisty requirements<br> Class 4: Medium-low linguistic intenisty requirements<br> Class 5: Low linguistic intenisty requirements</p>
Stimulation of medial amygdala GABA neurons with kinetically different channelrhodopsins yields opposite behavioral outcomes
<p>This dataset continues the dataset accessible by doi 10.5281/zenodo.4311847. The latter also contains all the relevant metadata description.</p>
Semi-automated Quantitative Morphometric Analysis of E18 Rat Hippocampal Neurons from 0.5 to 6 Days In Vitro
<p>This is the dataset presented in "Semi-automated quantitatve evaluation of neuron developmental morphology <em>in vitro</em> using the change-point test" by AS Liao, W Cui, VS Webster-Wood, and YJ Zhang (submitted to Neuroinformatics 2022).</p>
Supplementary dataset to publication: "Neuroglia Infection by Rabies Virus after Anterograde Virus Spread in Peripheral Neurons"
<p>Supplementary data to the publication: Potratz M., Zaeck L.M., Weigel C., Klein A., Freuling C.M., Müller T., <strong>Finke S.</strong> <strong>2020. </strong>Neuroglia Infection by Rabies Virus after Anterograde Virus Spread in Peripheral Neurons. <strong>Acta Neuropathologica Communications. </strong>8:199. doi.org/10.1186/s40478-020-01074-6.</p>
Neurons for infant social behaviors in the mouse zona incerta
<p><strong>Neurons for infant social behaviors in the mouse zona incerta</strong></p> <p>Repository containing datasets supporting the study.</p> <p>Github link to related analysis code: https://github.com/yxl95/zona_incerta_infant_social_behavior</p>
The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons
<p>This project contains the annotated transcriptomes in GTF format used in the manuscript "The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons" DOI: <a href="https://doi.org/10.1093/molbev/msae123">https://doi.org/10.1093/molbev/msae123</a></p>
Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus: optogenetical stimulation data
<p>This dataset contains 2-photon calcium imaging data from the paper 'Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus'. This is the calcium imaging data from CA1 pyramidal cells and interneurons, including both spontaneous activity and activity in response to optogenetic stimulation.</p> <p><strong>Data organization</strong></p> <p>This dataset contains all the data related to the all-optical part of the paper and was analyzed using the code from the <a href="https://gitlab.com/cossartlab/bocchio-vorobyev-et-al-2023/-/tree/main/Optogenetical%20stimulation?ref_type=heads">lab repository</a>. The original calcium imaging movies are excluded due to size limitations.</p> <p><strong>Further information</strong></p> <p>Please email vorobev[a t]phystech.edu if you need further information on the data or if you wish to access the raw calcium imaging movies (not uploaded here due to storage limitations).</p>
Data set for "Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex"
<p>Data set for: Yamashita T, Vavladeli A, Pala A, Galan K, Crochet S, Petersen SSA, Petersen CCH (2018) Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex. Front Neuroanat 12: 33. https://doi.org/10.3389/fnana.2018.00033</p> <p>There are 25 files in this data upload:</p> <p>1. '2018_Yamashita_FrontNeuroanat.pdf' - this a pdf version of the online publication.</p> <p>2. 'Yamashita_Figure2_Quantification.xlsx' - this is a Microsoft Excel file giving the locations of high density axonal projections from layer 2/3 pyramidal neurons in the mouse C2 barrel column in the coordinate frame of Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press. The data are plotted in Figure 2 of Yamashita et al., 2018.</p> <p>3. 'Yamashita_Figure7_Quantification.xlsx' - this is a Microsoft Excel file giving the dendritic length, number of dendrites, number of dendritic nodes and total axonal length, as well as the axonal length in the different projection zones for each reconstructed neuron. The data are plotted in Figure 7 of Yamashita et al., 2018.</p> <p>4. 'Yamashita_SupMov1_S2P_AP049.mov' - this is a QuickTime video file, showing the 3D structure of neuron AP049 featured in Figure 3 of Yamashita et al., 2018.</p> <p>5. 'Yamashita_SupMov2_M1P_TY308.mov' - this is a QuickTime video file, showing the 3D structure of neuron TY308 featured in Figure 5 of Yamashita et al., 2018.</p> <p>6. 'AV198.zip' - this zipped folder contains data relating to mouse AV198: a) 'AV198_stack.tif' the z-stack of whole-brain fluorescence images from expression of tdTomato in layer 2/3 neurons of the C2 barrel column of mouse AV198. b) 'AV198_ROI_Box.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a box. c) 'AV198_ROI_Point.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a point. d) 'AV198_Paxinos' is a folder showing the coronal fluorescent brain sections in pdf format overlaid on the equivalent drawing from Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press.</p> <p>7. 'AV199.zip' - same as 'AV198.zip' but for mouse AV199.</p> <p>8. 'AV201.zip' - same as 'AV198.zip' but for mouse AV201.</p> <p>9. 'AV202.zip' - same as 'AV198.zip' but for mouse AV202.</p> <p>10. 'AV203.zip' - same as 'AV198.zip' but for mouse AV203.</p> <p>11. 'AP042.ASC' - Neurolucida (http://www.mbfbioscience.com/neurolucida) data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP042. Brain contours are also traced.</p> <p>12. 'AP044.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP044. Brain contours are also traced.</p> <p>13. 'AP046.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP046. Brain contours are also traced.</p> <p>14. 'AP047.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP047. Brain contours are also traced.</p> <p>15. 'AP049.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP049. Brain contours are also traced.</p> <p>16. 'TY220.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY220. Brain contours are also traced.</p> <p>17. 'TY288.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY288. Brain contours are also traced.</p> <p>18. 'TY300.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY300. Brain contours are also traced.</p> <p>19. 'TY302.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY302. Brain contours are also traced.</p> <p>20. 'TY308.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY308. Brain contours are also traced.</p> <p>21. 'TY310.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY310. Brain contours are also traced.</p> <p>22. 'TY337.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY337. Brain contours are also traced.</p> <p>23. 'TY345.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY345. Brain contours are also traced.</p> <p>24. 'TY367.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY367. Brain contours are also traced.</p> <p>25. 'TY369.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY369. Brain contours are also traced.</p>
Data accompanying the master thesis: A neuronal model for visually evoked startle responses in schooling fish
<p>This dataset contains data that was generated and analyzed for the master thesis "A neuronal model for visually evoked startle responses". All related material, including analysis code, of the master thesis can be found at https://github.com/awakenting/master-thesis.</p>
Source and Tissue Agnostic Reconstruction of Neurons
<p>Recent successes in deep learning have started to impact neuroscience. Of particular significance are claims that current segmentation algorithms achieve "super-human" accuracy in an area known as connectomics. However, as we will show, these algorithms do not effectively generalize beyond the particular source and brain tissues used for training -- severely limiting their usability by the broader neuroscience community. To fill this gap, we describe a novel connectomics challenge for source- and tissue-agnostic reconstruction of neurons (STARN), which favors broad generalization over fitting specific datasets. We first demonstrate that current state-of-the-art approaches to neuron segmentation perform poorly on the challenge. We further describe a novel convolutional recurrent neural network module that combines short-range horizontal connections within a processing stage and long-range top-down connections between stages. The resulting architecture establishes the state of the art on the STARN challenge -- improving the prospect for computer vision to allow for widespread fully-automated connectomics analysis.</p> <p>Here, we address the poor generalization of computer vision systems in connectomics with a novel challenge: the source- and tissue-agnostic reconstruction of neurons. This challenge presents a "training" dataset consisting of five publicly available and annotated tissue volumes representing a variety of organisms and imaging configurations (CREMI, FIB-25, and SNEMI3D); evaluation is performed on an independent volume <a href="https://www.nature.com/articles/nature09818">[1]</a> annotated by our group. We will demonstrate that the STARN challenge defeats state-of-the-art systems for neuron reconstruction.</p>
in vivo electrophysiological data of DRN serotonin neurons
<p>This repository contains in vivo electrophysiological data of DRN serotonin neurons of freely behaving mice.</p> <p>The data is related to the following research article: Li, Y., Zhong, W., Wang, D. et al. Serotonin neurons in the dorsal raphe nucleus encode reward signals. Nat Commun 7, 10503 (2016). https://doi.org/10.1038/ncomms10503 Please refer to the original publication for details.</p> <p>Please refer to the Readme.txt in the files for details.</p> <p> </p>
Metabolomics data associated with "Glial swip-10 controls systemic mitochondrial function, oxidative stress, and neuronal viability via copper ion homeostasis"
<p>Raw feature tables used for metabolomic analysis of the <em>Caenorhabditis elegans</em> mutant <em>swip-10</em>. The data were generated using liquid chromatography coupled high-resolution mass spectrometry. Two different columns were used: HILIC (+ ESI) and C18 (-ESI), coupled to a Thermo Q-Exactive Orbitrap mass spectrometer. The feature tables were generated using open-source peak peaking and alignment R packages: apLCMS and xMAanalyzer. See more details in the associated manuscript.</p>
Data set for "Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices"
<p>Data set for: Liu Y, Bech P, Tamura K, Délez LT, Crochet S, Petersen CCH (2024) Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices. eLife 13: RP97602. https://doi.org/10.7554/eLife.97602</p> <p>There are 3 files in this upload:</p> <p>1. The file named "2024_Liu_eLife.pdf" is the Open Access pdf of the online publication in eLife.</p> <p>2. The file named "Liu_anatomy_data_code.zip" (~35 GB) is a zipped version of a folder "Liu_anatomy_data_code" (~111 GB), which contains the anatomical data analysed in the study along with the Python codes used to generate the published figures 1-7 and their associated figure supplements. </p> <p>3. The file named "Liu_function_data_code.zip" (~10 GB) is a zipped version of a folder "Liu_function_data_code" (~35 GB), which contains the functional data analysed in the study along with the Python codes used to generate the published figure 8 and its associated figure supplement. </p> <p>After unzipping, the Python codes should run as a Jupyter notebook (anatomy .ipynb code) or Python code (function .py code) in Anaconda.</p>
Heterogeneous Habenular Neuronal Ensembles during Selection of Defensive Behaviors
<p>Optimal selection of threat-driven defensive behaviors is paramount to an animal's survival. The lateral habenula (LHb) is a key neuronal hub coordinating behavioral responses to aversive stimuli. Yet, how individual LHb neurons represent defensive behaviors in response to threats remains unknown. Here, we show that in mice, a visual threat promotes distinct defensive behaviors, namely runaway (escape) and action-locking (immobile-like). Fiber photometry of bulk LHb neuronal activity in behaving animals reveals an increase and a decrease in calcium signal time-locked with runaway and action-locking, respectively. Imaging single-cell calcium dynamics across distinct threat-driven behaviors identify independently active LHb neuronal clusters. These clusters participate during specific time epochs of defensive behaviors. Decoding analysis of this neuronal activity reveals that some LHb clusters either predict the upcoming selection of the defensive action or represent the selected action. Thus, heterogeneous neuronal clusters in LHb predict or reflect the selection of distinct threat-driven defensive behaviors.</p>
A deep learning-based dataset of WFA-positive perineuronal nets and parvalbumin neurons localizations in the adult mouse brain
<p><strong>Quality-controlled predictions of deep learning models for cell counting</strong></p> <p>This dataset contains high-resolution images for the visualization of perineuronal nets (PNNs) and parvalbumin-expressing (PV) cells analyzed in the paper:</p> <p><em>A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain.</em></p> <p>The dataset integrates the raw data published on a <a href="https://zenodo.org/record/7419282">previous upload</a> on Zenodo.</p> <p>Cell locations were obtained using two deep-learning models for cell counting (publicly available on <a href="http://github.com/ciampluca/counting_perineuronal_nets">GitHub</a>, details in the paper by <a href="https://www.sciencedirect.com/science/article/pii/S1361841522001475">Ciampi et al., 2022</a>). The output of the deep-learning pipeline was filtered based on the <em>score</em> assigned to each cell prediction, by removing all the PNNs with a score lower than 0.4 and all the PV cells with a score lower than 0.55. Cases of artefactual cell detection were finally removed manually by visual inspection of the images. </p> <p><strong>Content</strong></p> <p>The dataset contains microscopy images of coronal brain slices from 7 adult mice. The objects highlighted in these images represent the final set of PNNs/PV cells that were used in all the analysis of the paper.</p> <p><strong>Folder Structure and file naming conventions</strong></p> <p>There are separate folders for each mouse. Each folder is named with the ID of that mouse. Within each folder, images are assigned a code specifying the channel (C1 for PNNs, C2 for PV cells).</p> <p> </p>
Data from: Kir2.1 modulation in macrophages sensitises dorsal root ganglion neurons through TNF secretion after nerve injury
<p>This data pertain to the manuscript titled "Kir2.1 modulation in macrophages sensitises dorsal root ganglion neurons through TNF secretion after nerve injury", currently in preprint on BioRxiv (https://doi.org/10.1101/2023.06.21.545843). The name of the data files correspond to the for each figure in the study. The data file in .csv format are organized so that they can easily be opened in R or other analysis language. To understand them and how they are labelled, it is advised to open the figure next to them and find the appropriate panel.</p> <p>Here are included:</p> <ul> <li>Example images of section of mouse dorsal root ganglion (DRG) after spared nerve injury (SNI), labelled for CX3CR1+ cells, Ki67 and MHC class II by immunohistochemistry.</li> <li>LC-MS-MS proteomic data set of CX3CR1+ cells from DRG of mice after SNI.</li> <li>Voltage clamp data of CX3CR1+ cells from DRG of mice after SNI</li> <li>Electrophysiological data sets (multi-electrode array, current clamp and voltage clamp) of dissociated DRG neurons treated with medium conditioned by CX3CR1+ or GFAP+ cells sorted from ipsilateral or contralateral DRG from mice after SNI. In addition, pharmacological treatments were added to the conditioned medium (CM).</li> </ul>
Analysis of AaH-II effect in the axon initial of neocortical pyramidal neurons
<p>This dataset contains data in HEK293 neurons expressing either human Na<sub>v</sub>1.2 or human Na<sub>v</sub>1.6 and whole-cell electrophysiological recordings and imaging data from neocortical layer-5 pyramidal neuron in brain slices of the mouse.</p> <p> </p> <p>This dataset is used in the paper:</p> <p>Abbas F, Blömer LA, Millet H, Montnach J, De Waard M, Canepari M. Analysis of the effect of the scorpion toxin AaH-II on action potential generation in the axon initial segment. bioRxiv, 2023. https://www.biorxiv.org/content/10.1101/2023.10.06.561226v1</p>
Source data for "Feed-forward metabotropic signaling by Cav1 Ca2+ channels supports pacemaking in pedunculopontine cholinergic neurons"
<p><strong>Fig.1A_ChAT.tif</strong></p><p>Confocal image (green channel, anti-ChAT staining) for Fig.1A</p><p> </p><p><strong>Fig.1A_tdTomato.tif </strong></p><p>Confocal image (red channel, tdTomato) for Fig.1A</p><p> </p><p><strong>Fig.1B_ChAT.tif</strong></p><p>Confocal image (green channel, anti-ChAT staining) for Fig.1B</p><p> </p><p><strong>Fig.1B_tdTomato.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.1B</p><p> </p><p><strong>Fig.1C_DIC.png</strong></p><p>Differential interference contrast micrograph for Fig.1C left</p><p> </p><p><strong>Fig.1C_Fluo.png</strong></p><p>Epifluorescent illumination micrograph for Fig. 1C right</p><p> </p><p><strong>Fig.1DEH.xlsx</strong></p><p>Numerical data for the charts in Fig. 1D, Fig.1E, Fig.1H</p><p> </p><p><strong>Fig.1F.tif</strong></p><p>MAX projection of z-stack of 2PLSM images (red channel, Alexa 594) used to generate Fig.1F </p><p> </p><p><strong>Fig.1F_inset.tif</strong></p><p>2PLSM image (green channel, Fura-2) for the right inset of Fig.1F</p><p> </p><p><strong>Fig.2A_inset.tif</strong></p><p>Confocal image (green channel, GFP) for the higher magnification inset of Fig.2A</p><p> </p><p><strong>Fig.2A.tif</strong></p><p>Confocal image (green channel, GFP) for Fig.2A</p><p> </p><p><strong>Fig.2B_bottom.tif</strong></p><p>Confocal image (green channel, GFP) for Fig.2B (bottom and overlay panels)</p><p> </p><p><strong>Fig.2B_top.tif</strong></p><p>Confocal image (red channel, td Tomato) for Fig.2B (top and overlay panels)</p><p> </p><p><strong>Fig.2CE.xlsx</strong></p><p>Numerical data for the charts in Fig. 2C, Fig. 2E</p><p> </p><p><strong>Fig.3B.tif</strong></p><p>Confocal image (green channel, MitoGCaMP6) for Fig.3B and overlay in Fig.3D</p><p> </p><p><strong>Fig.3C.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.3C and overlay in Fig.3D</p><p> </p><p><strong>Fig.3E.tif</strong></p><p>2PLSM image (green channel, MitoGCaMP6) for Fig.3E</p><p> </p><p><strong>Fig.3GIJ.xlsx</strong></p><p>Numerical data for the charts in Fig. 3G, Fig. 3I, Fig.3J</p><p> </p><p><strong>Fig.4B.tif</strong></p><p>2PLSM image (green channel, MitoGCaMP6) for Fig.4B</p><p> </p><p><strong>Fig.4DFG.xlsx</strong></p><p>Numerical data for the charts in Fig.4D, Fig.4F, Fig.4G</p><p> </p><p><strong>Fig.5A.tif</strong></p><p>Confocal image (green channel, PercevalHR) for Fig.5A and overlay in Fig.5C</p><p> </p><p><strong>Fig.5B.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.5B and overlay in Fig.5C</p><p> </p><p><strong>Fig.5D.tif</strong></p><p>2PLSM image (green channel, PercevalHR) for Fig.5D</p><p> </p><p><strong>Fig.5GHJ.xlsx</strong></p><p>Numerical data for the charts in Fig.5G, Fig.5H, Fig.5J</p><p> </p><p><strong>Fig.6BCD.xlsx</strong></p><p>Numerical data for the charts in Fig.6b, Fig.6C, Fig.6D</p><p> </p><p><strong>Fig.7A.tif</strong></p><p>Confocal image (green channel, mito-roGFP) for Fig.7A and overlay in Fig.7C</p><p> </p><p><strong>Fig.7B.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.7B and overlay in Fig.7C</p><p> </p><p><strong>Fig.7D.tif</strong></p><p>2PLSM image (green channel, mito-roGFP) for Fig.7D</p><p> </p><p><strong>Fig.7F.xlsx</strong></p><p>Numerical data for the charts in Fig.7F</p>
DS3_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_neurons electrophysiology
<p>Whole-cell current clamp recordings of the electrical activity of neurons plated on P3HT flat and pillars</p>
Hippocampal hub neurons maintain distinct connectivity throughout their lifetime
<p>The temporal embryonic origins of cortical GABA neurons are critical for their specialization. In the neonatal hippocampus, GABA cells born the earliest (ebGABAs) operate as ‘hubs’ by orchestrating population synchrony. However, their adult fate remains largely unknown. To fill this gap, we have examined CA1 ebGABAs using a combination of electrophysiology, neurochemical analysis, optogenetic connectivity mapping as well as ex vivo and in vivo calcium imaging. We show that CA1 ebGABAs not only operate as hubs during development, but also maintain distinct morpho-physiological and connectivity profiles, including a bias for long-range targets and local excitatory inputs. In vivo, ebGABAs are activated during locomotion, correlate with CA1 cell assemblies and display high functional connectivity. Hence, ebGABAs are specified from birth to ensure unique functions throughout their lifetime. In the adult brain, this may take the form of a long-range hub role through the coordination of cell assemblies across distant regions.</p>
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.