Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
15
datasets available to search
ShareScore release 0.7.1
Dataset results
15 results for “barrel cortex”
Data set for "Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing"
<p>Data set for: Gasselin C, Hohl B, Vernet A, Crochet C, Petersen CCH (2021) Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron doi: 10.1016/j.neuron.2020.12.018</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2021_Gasselin_Neuron.pdf" is the Open Access pdf of the online publication in Neuron.</p> <p>2. The file named "Gasselin_data_code.zip" (~9 GB) is a zipped version of a folder "Gasselin_data_code" (~13 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Gasselin_data_code’). Each code computes and plots the results used in the corresponding figure. Figures and Tables are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘Functions’ contains functions called by the main codes.</p> <p>The main folder contains the following codes:</p> <p><em>Gasselin_Figure1: computes and plots the results for the panels D, E and F of figure 1.</em></p> <p><em>Gasselin_Figure2: computes and plots the results for the panels B and C of figure 2.</em></p> <p><em>Gasselin_Figure3: computes and plots the results for the panels B, C and D of figure 3.</em></p> <p><em>Gasselin_Figure4: computes and plots the results for the panels A, B and C of figure 4.</em></p> <p><em>Gasselin_FigureS1: computes and plots the results for the panels A, B and C of figure S1.</em></p> <p><em>Gasselin_FigureS2: computes and plots the results for the panels A and B of figure S2.</em></p> <p> </p> <p>The subfolder ‘Data’ contains the data structures used for the different figures:</p> <p><em>data_figure1.mat</em></p> <p><em>data_figure2.mat</em></p> <p><em>data_figure3.mat</em></p> <p><em>data_figure4_MECA.mat</em></p> <p><em>data_figure4_Activation.mat</em></p> <p><em>data_figure4_Inactivation.mat</em></p> <p><em>data_figureS2_Activation.mat</em></p> <p><em>data_figureS2_Inactivation.mat</em></p> <p><em>data_Axon.mat</em></p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>Mouse_Name</em> : name of the mouse.</p> <p><em>Mouse_DateOfBirth</em>: date of birth of the mouse (YMD).</p> <p><em>Mouse_Sex</em>: sex of the mouse (F or M).</p> <p><em>Mouse_Genotype</em>: genotype of the mouse.</p> <p><em>Mouse_Drug</em>: experimental condition of the recording (control = ‘No Drug’; blockade of glutamatergic transmission = ‘CNQX_DAPV’; blockade of glutamatergic transmission and nicotinic receptors = ‘CNQX_DAPV_MECA’; blockade of nicotinic receptors only = ‘MECA’).</p> <p><em>Mouse_Virus</em>: virus injected if any.</p> <p><em>Cell_Counter</em>; cell recorded in a given mouse.</p> <p><em>Cell_Type</em>: type of the recorded cell based on 2P imaging. (EXC, VIP, PV, SST, 5HT3aR_non_VIP).</p> <p><em>Cell_Depth</em>: depth of the recorded cell relative to pia (µm).</p> <p><em>Cell_TargetedBrainArea</em>: cortical area targeted (C2 column of the barrel cortex = C2).</p> <p><em>Cell_Fluorescence</em>: expression of the genetically encoded fluorophore (FALSE or TRUE). A neuron recorded in a VIP_IRES_Cre x LSL_tdTomato (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence</em>=TRUE is considered as a VIP neuron (cf <em>Cell_Type</em>).</p> <p><em>Sweep_Counter</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-60 s).</p> <p><em>Sweep_Type</em>: experimental condition during that sweep (Only spontaneous whisking onset = ‘Onset’; Whisking onset and whisker stimulus = ‘Onset_Whisker_Stim’ ; Optogenetic stimulation = ‘Opto_Stim’; Optogenetic activation = ‘Opto_Activation’; Optogenetic inactivation = ‘Opto_Inactivation’; ).</p> <p><em>Sweep_Start_Time</em>: time at the beginning of the sweep recording (YMDHms).</p> <p><em>Sweep_WhiskerAngle</em>: C2 whisker angular position extracted from simultaneous high-speed video filming (deg).</p> <p><em>Sweep_WhiskerAngle_SamplingRate</em>: sampling rate of the whisker angle trace.</p> <p><em>Sweep_WhiskingOnset_Time</em>: time of identified whisking onset - excluding any whisker stimulus shortly before or after (s).</p> <p><em>Sweep_MembranePotential</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>Sweep_MembranePotential_SamplingRate</em>: sampling rate of the membrane potential signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_CurrentInjected</em>: current injected into the cell (pA).</p> <p><em>Sweep_CurrentInjected_SamplingRate</em>: sampling rate of current signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_WhiskerStim_Name</em>: whisker to which the magnetic stimulus was applied to (C2 or B2&C2).</p> <p><em>Sweep_WhiskerStim_Time</em>: onset times of the whisker stimulus (s).</p> <p><em>Sweep_OptoStim_Power</em>: light power applied for optogenetic manipulations (% of the max power).</p> <p><em>Sweep_OptoStim_Time</em>: onset times of the light pulses for optogenetic manipulations (s).</p> <p><em>Cell_ID</em>: unique cell identifier (= <em>Mouse_Name</em>+<em>Cell_Counter</em>).</p> <p><em>SpikeThreshold</em>: spike threshold used to detect APs (mV).</p> <p><em>Trial_WhiskingOnset</em>: data structure containing the cut signals used to compute averaged responses around whisking onset times.</p> <p><em>Trial_WhiskerStim</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times.</p> <p><em>Trial_WhiskerStim_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without whisker movements.</p> <p><em>Trial_WhiskerStim_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with whisker movements.</p> <p><em>Trial_Opto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times.</p> <p><em>Trial_OptoAndWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and no whisker movements.</p> <p><em>Trial_OptoAndWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and without whisker movements.</p> <p><em>Trial_OnlyWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyOpto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times in trials without whisker stimulus.</p>
Data set for "Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas"
<p>Data set for: Liu Y, Foustoukos G, Crochet S and Petersen CCH (2022) Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas. Front Neuroanat 15: 791015. https://doi.org/10.3389/fnana.2021.791015</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2022_Liu_FrontNeuroanat.pdf</strong>" is the Open Access pdf of the online publication in Frontiers in Neuroanatomy.</p> <p>2. The file named "<strong>Liu_data_code.zip</strong>" (~1 GB) is a zipped version of a folder ‘<em>Liu_data_code</em>’, which contains the data analyzed in the study along with the Python codes used to generate the published figures. The original high resolution image stacks obtained through whole-brain two-photon serial tomography are unfortunately too large for Zenodo, and only highly-downsampled data are included in this upload, which were used for registration with the Allen CCFv3. Instructions on how to view and analyse the anatomical data are provided in the 'README.docx' file, which you will find upon unzipping the folder.</p> <p> </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 set for "Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex"
<p>Data set for: Sermet BS, Truschow P, Feyerabend M, Mayrhofer JM, Oram TB, Yizhar O, Staiger JF, Petersen CCH (2019) Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex. eLife 8: e52665. https://doi.org/10.7554/eLife.52665</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2019_Sermet_eLife.pdf" is the Open Access pdf file of the manuscript published in eLife.</p> <p>2. The file named "Sermet_data_code.zip" (~5 GB) is a zipped version of a folder "Sermet_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and one '.mat' data file. In order to run the analysis of the data set, you need to execute 'PopPlot.m'.</p>
Neural coding in barrel cortex during whisker-guided locomotion
<p>Data accompanying publication at <a href="https://doi.org/10.7554/eLife.12559">https://doi.org/10.7554/eLife.12559</a> and code at <a href="https://doi.org/10.5281/zenodo.2949955">https://doi.org/10.5281/zenodo.2949955</a>. For example usage see the notebooks in the repository.</p> <p>The data is organized according to animal id, `00` - `18`.</p> <p>Animals `00` - `12` are electrophysiology data. Each electrophysiology animal data contains the timestamps of the extracted spikes and various processed tabular data. For usage see the `ephys-traces.ipynb` and `ephys-table.ipynb`. Raw voltage traces are not provided.</p> <p>Animals `13` - `18` are imaging data. Each imaging animal data contains timeseries of extracted calcium transients, pixel-wise regression maps of the field of view and various processed tabular data. For usage see the `imaging-raw.ipynb`, `imaging-traces.ipynb`, and `imaging-traces.ipynb`. Raw imaging movies are not provided.</p>
Data set for "Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing"
<p>Data set for: Kiritani T, Pala A, Gasselin C, Crochet S, Petersen CCH (2023) Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing. PLOS ONE 18: e0287174. doi: 10.1371/journal.pone.0287174</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2023_Kiritani_PLOSONE.pdf" is the Open Access pdf of the online publication in PLOS ONE.</p> <p>2. The file named "Kiritani_data_code.zip" (~5 GB) is a zipped version of a folder "Kiritani_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder 'Kiritani_data_code' and all subfolders. Directly from this folder, you should first run the codes in the folder 'Data_Analysis_Codes', sequentially executing 'Analysis_1.m' through to 'Analysis_9.m'. Note, execution of 'Analysis_9.m' can take a long time (~1 hour on a good desktop PC). You can then run the codes in the folder 'Figure_Plotting_Codes' to generate the figures published in the journal article. In the folder 'Data', you can also find a DataViewer to visualise the data sets, which you can run by executing 'DataViewer.m' directly from the subfolder ‘Data’.</p> <p> </p>
Nitric oxide is not responsible for initial sensory-induced neurovascular coupling response in the barrel cortex of lightly anaesthetised mice
Open the record for dataset details and reuse information.
Data set for "Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task"
<p>Data set for: Vavladeli A, Daigle T, Zeng H, Crochet S, Petersen CCH (2020) Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task. FUNCTION 1: zqaa008. doi: 10.1093/function/zqaa008</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2020_Vavladeli_FUNCTION.pdf" is the Open Access pdf file of the manuscript published in FUNCTION.</p> <p>2. The file named "Vavladeli_data_code.zip" (~2 GB) is a zipped version of a folder named "Vavladeli_data_code" (~2 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and two '.mat' data files. In order to run the analysis of the data set, you need to execute the '.m' file with the corresponding figure name.</p>
Simultaneous two-photon voltage or calcium imaging and multi-channel LFP recordings in barrel cortex of awake and anesthetized mice
<p>Neuronal population activity, both spontaneous and sensory-evoked, generates propagating waves in cortex. However, high spatiotemporal-resolution mapping of these waves is difficult as calcium imaging, the work horse of current imaging, does not reveal subthreshold activity.</p> <p>Here, we present a platform combining voltage or calcium two-photon imaging with multi-channel local field potential (LFP) recordings in different layers of the barrel cortex from anesthetized and awake head-restrained mice. A chronic cranial window with access port allows injecting a viral vector expressing GCaMP6f or the voltage-sensitive dye (VSD) ANNINE-6plus, as well as entering the brain with a multi-channel neural probe. We present both average spontaneous activity and average evoked signals in response to multi-whisker air-puff stimulations.</p> <p>Time domain analysis shows the dependence of the evoked responses on the cortical layer and on the state of the animal, here separated into anesthetized, awake but resting, and running. The simultaneous data acquisition allows to compare the average membrane depolarization measured with ANNINE-6plus with the amplitude and shape of the LFP recordings. The calcium imaging data connects these data sets to the large existing database of this important second messenger. Interestingly, in the calcium imaging data, we found a few cells which showed a decrease in calcium concentration in response to vibrissa stimulation in awake mice.</p> <p>This system offers a multimodal technique to study the spatiotemporal dynamics of neuronal signals through a 3D architecture in vivo. It will provide novel insights on sensory coding, closing the gap between electrical and optical recordings.</p>
A Model of Rat Non-barrel Somatosensory Cortex Anatomy
<p><strong>A full description of the model is available in the two companion manuscripts: </strong></p> <p><a href="https://www.biorxiv.org/content/10.1101/2022.08.11.503144v3.abstract">Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy</a></p> <p><a href="https://www.biorxiv.org/content/10.1101/2023.05.17.541168v5">Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation</a></p> <p><em>We kindly ask that you cite these papers, as well as the Zenodo repository, in any articles or presentations using the model or any of its constituent components.</em></p> <p>---</p> <p>We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat. The modeling process is based on a previously established workflow for a single cortical column, but is extended here to build a much larger circuit in an atlas-based geometry. Neurons in the model belong to 60 different morphological types and are connected by synapses placed by two established algorithms, one modeling local connectivity determined by axo-dendritic overlap, and one for long-range connectivity between sub-regions. Long-range connectivity is defined with topographic mapping and laminar connectivity profiles, providing intrinsic feed-forward and feedback pathways. Additionally, we incorporate core- and matrix-type thalamocortical projection systems, associated with VPM and POm thalamic nuclei respectively, that enable extrinsic input.</p> <p>The model comprises 211712 neurons in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas, scaled down to juvenile size. It is available 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 associated with all systems described above. Modeled synapses are associated with their exact location in the dendritic tree, and additional anatomical parameters, such as spine length (where biologically plausible). Extrinsic synaptic connections from neurons in the remainder of non-barrel somatosensory cortex and thalamic inputs are also contained.</p> <p>Note that this is an <em>anatomical</em> model: Parameters and files related to neuronal and synaptic <em>physiology</em> can be found in our <a href="../record/7930276">release of the <em>physiological </em>model</a>.</p> <p><strong>[UPDATE 23/07/17]: </strong>Added a zip archive containing the voxel atlas data used. This comprises the region atlas (brain_regions, hierarchy) and generated voxelized densities for each neuron type ([cell_density]*). All atlas files are in the .nrrd format, best loaded using the python package <a href="https://github.com/BlueBrain/voxcell">voxcell</a>. The atlas files cover the entire S1 regions, with the location of the part of the model released here indicated by <em>published_volume.nrrd. </em><strong>All other files remained unchanged!</strong></p> <p>Please refer to the documentation of the SONATA format for information how to load and analyze the model. A jupyter notebook has been included with basic examples of how to load the data using our open-source packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a> and <a href="https://bluebrainsnap.readthedocs.io/en/stable/">Blue Brain SNAP</a>.</p> <p><em>This study was supported by funding to the Blue Brain Project, a research center of the Ecole polytechnique federale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology. RL, JPS and JL were supported by EPSRC under grant number EP/P025072/1. RL was supported by a collaboration grant from EPFL.</em></p>
Two photon data from: Functional and structural properties of highly responsive somatosensory neurons in mouse barrel cortex
Open the record for dataset details and reuse information.
Simultaneous two-photon voltage or calcium imaging and multi-channel LFP recordings in barrel cortex of awake and anesthetized mice
Open the record for dataset details and reuse information.
Data from: Olfactory modulation of barrel cortex activity during active whisking and passive whisker stimulation
<p>Rodents depend on olfaction and touch to meet many of their fundamental needs. The joint significance of these sensory systems is underscored by an intricate coupling between sniffing and whisking. However, the impact of simultaneous olfactory and tactile inputs on sensory representations in the cortex remains elusive. To study these interactions, we recorded large populations of barrel cortex neurons using 2-photon calcium imaging in head-fixed mice during olfactory and tactile stimulation. We find that odors alter barrel cortex activity in at least two ways, first by enhancing whisking, and second by central cross-talk that persists after whisking is abolished by facial nerve sectioning. Odors can either enhance or suppress barrel cortex neuronal responses, and while odor identity can be decoded from population activity, it does not interfere with the tactile representation. Thus, barrel cortex represents olfactory information which, in the absence of learned associations, is coded independently of tactile information.</p>
RORα coordinates thalamic and cortical maturation to instruct barrel cortex development.
GEO Series GSE80317. Mus musculus. 16 samples. Type: Expression profiling by array.
GAP-43 HZ and KO mouse barrel cortex show modifications in genes associated with synaptogenesis and barrel map formation
GEO Series GSE12687. Mus musculus. 9 samples. Type: Expression profiling by array.
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.