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.
42
datasets available to search
ShareScore release 0.9.0
Dataset results
42 results for “cortical dynamics”
A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
<p>The file <strong>Corpora.txt </strong>keeps the corpus used to train the model and the different instances of the classifier. It is basically a text file with one sentence per line from the original corpus called <strong>test.tsv</strong> available at <a href="https://github.com/google-research-datasets/wiki-split.git">https://github.com/google-research-datasets/wiki-split.git</a>. We eliminated punctuation marks and special characters from the original file putting each sentence per line.</p> <p><strong>Enju_Output.txt </strong>holds the outputs generated by Enju in -so mode (Output in stand-off format) using Corpora.txt as input. This file has basically a natural language English per-sentence parse with a wide-coverage probabilistic for HPSG grammar.</p> <p>The file <strong>Supervision.txt </strong>keeps the grammatical tags of the corpus. This file holds a tag per word and each tag is situated in a single line. Sentences are separated by one empty line while tags from words in the same sentence are located in adjacent lines.</p> <p>The file<strong> Word_Category.txt</strong> carries the coarse-grained word category information needed by the model and introduced in it by apical dendrites. Each word in the corpus has a word-category tag which provides additional constraints to those provided by lateral dendrites. This file contains a tag per word and each tag is situated in a single line. Sentences are separated by one empty line while tags from words in the same sentence are located in adjacent lines.</p> <p>The file <strong>SynSemTests.xlsx</strong> keeps all the grammar classification results as well as the statistical analysis in the classification tests.</p>
Dynamic structure of motor cortical neuron co-activity carries behaviorally relevant information
<p>(This is the dataset used in <a href="https://doi.org/10.1101/2022.05.18.492501" rel="noopener" title="Dynamic Structure Of Motor Cortical Neuron Co-Activity Carries Behaviorally Relevant Information">Dynamic Structure Of Motor Cortical Neuron Co-Activity Carries Behaviorally Relevant Information</a>, Abstract below)</p> <p><span>Skillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of co-activity between neurons. Using pairwise spike time statistics, co-activity can be summarized as a functional network (FN). Here, we show that the structure of FNs constructed from an instructed-delay reach task in non-human primates are behaviorally specific: low dimensional embedding and graph alignment scores show that FNs constructed from closer target reach directions are also closer in network space. Using short intervals across a trial we constructed temporal FNs and found that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly decodable shortly after the instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the instruction cue consistent with the hypothesis that information external to the recorded population temporarily alters the structure of the network at this moment.</span></p>
Cortical astrocyte histamine-1-receptors regulate intracellular calcium and extracellular adenosine dynamics across sleep and wake
Open the record for dataset details and reuse information.
Dynamic structure of motor cortical neuron co-activity carries behaviorally relevant information
Open the record for dataset details and reuse information.
Complementary cortical and thalamic contributions to cell-type-specific striatal activity dynamics during movement
Open the record for dataset details and reuse information.
Data-driven segmentation of cortical calcium dynamics
Open the record for dataset details and reuse information.
Dynamic cortical behavior of plant protoplasts reveals unexpected similarities between plant and animal cells
<p>The raw data presented in this folder corresponds to the publication<br># Dynamic cortical behavior of plant protoplasts reveals unexpected similarities between plant and animal cells</p> <p>Johanna E. M. Dickmann 1,2, Marjolaine Martin 1,§, Claire Lionnet 1,§, Zoe Nemec-Venza 1, Olivier Hamant 1,2</p> <p>1 Laboratoire Reproduction et Développement des Plantes, ENS de Lyon, INRAE, CNRS, UCBL1 <br>2 Correspondence: olivier.hamant@ens-lyon.fr, johanna.dickmann@ens-lyon.fr <br>§ Equal contribution </p> <p>ORCIDs:<br>* Johanna Dickmann: 0000-0002-0861-4440<br>* Zoe Nemec-Venza: 0000-0002-2346-2596<br>* Olivier Hamant: 0000-0001-6906-6620</p> <p>Submitted to bioRxiv in November 2024 </p> <p>This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 521501033 to J.D. and the European Research Council (ERC-2021-AdG-101019515 “Musix” to O.H.).</p> <p>## Data organization</p> <p>The data are organized according to the figure panels in the publication. For large experiments, the folders may contain subfolders for each experimental repeat and/or condition. For explanations on the data and the methods, please refer to the publication.</p> <p>All data presented here are the original raw data output of the microscopes in the CZI format, a proprietary format developed by Zeiss, encapsulating both 4D image data and metadata, i.e. acquisition settings. This format is supported by open-source software such as Fiji and Open Microscopy Environment.</p> <p>Refer to the README files in the subfolders for information on which exact file was used to display in the figure.</p> <p>## Explanation of the file names</p> <p>### Arabidopsis experiments</p> <p>The filenames contain the following information, separated by underscores:<br>* an experiment identifier (e.g.”PLA001”, “PRO077”)</p> <p>* the line of the imaged plant material (e.g. “pUBQ10-LTi6B-TdTomato”)</p> <p>* sometimes information on the ecotype of the line (e.g. “Col-0”)</p> <p>* the age of the plants (e.g. “7d” = 7 day old plants)</p> <p>* sometimes information on a stain added (e.g. “FM4-64_0_5ugPml” = FM4-64 dye at a final concentration of 5 ug/ml)</p> <p>* sometimes information on a treatment (e.g. “beforeFDA” = image taken before FDA was added; “FDA2.5ugPml” = after adding FDA at a final concentration of 2.5 ug/ml, sometimes with additional information on the time between adding the FDA and imaging, e.g. “25min”)</p> <p>* sometimes information on the centrifugation speed (e.g. “100g”)</p> <p>* sometimes information on the imaging support (e.g. “bucket” = NOA73 container, “wells” = NOA73 microwells, “coverslip”)</p> <p>* sometimes information on the imaging mode (“z-stack”, “t-series” = time series/timelapse, “6x” = zoom of 6 in Zen software)</p> <p>* the solution the sample was imaged in (“Solution A” or “A” = hyperosmotic buffer with D-mannitol, “AS” = hyperosmotic buffer with D-sorbitol)</p> <p>* sometimes information on experimental setup (“ON” = overnight timelapse imaging)</p> <p>* increasing numbers at the end of the file name indicate subsequent fields of view or positions imaged with the same settings</p> <p>* for Fig. 1A: information about the length of the plasmolysis (“50 min”)</p> <p>* for Fig. 4c,d: information on which solution the protoplasts are and have been imaged in: “A” = hyperosmotic buffer solution with 600 mM D-mannitol. “B” = hyperosmotic buffer solution with 280 mM D-mannitol. Times indicate time between addition of new buffer and onset of imaging of the position list.</p> <p>* for Fig. 4e-f: the concentration of the hyperosmotic buffer solution is indicated. For the control, the number of additions of hyperosmotic buffer solution with 600 mM D-mannitol is indicated.</p> <p>### Physcomitrium patens experiments</p> <p>The filenames contain the following information, separated by underscores:<br>* an experiment identifier (e.g. "PyP001") </p> <p>* “Physco_wt” referring to Physcomitrium patens wild type</p> <p>* the age of the moss tissue used for protoplasting (e.g. “6d” = 6 days)</p> <p>* information on the stain added (e.g. “ Fm4-64_2ugPml” = FM4-64 dye at a final concentration of 2 ug/ml)</p> <p>* information on the imaging support (e.g. “bucket” = NOA73 container, “coverslip”)</p> <p>### Maize experiments</p> <p>The filenames contain the following information, separated by underscores:<br>* the date on which the experiment was performed (yyyymmdd)</p> <p>* the plant species (“Maize”)</p> <p>* sometimes information in the solution used for digestion ("A+E" = hyperosmotic buffer solution with D-mannitol)</p> <p>### Bead experiments</p> <p>The filenames contain the following information, separated by underscores:<br>* an experiment identifier (e.g. “beads008”)</p> <p>* a description of the beads (“fluoresbrite1micron” = Fluoresbrite beads of a diameter of 1 um)</p> <p>* sometimes a short description of the protocol (e.g. “SolAwashed-2-3mLsolA” = NOA73 microwells were washed in hyperosmotic buffer with 600 mM D-mannitol 2x prior to imaging, beads were imaged in 3 ml hyperosmotic buffer solution with 600 mM D-mannitol.)</p> <p>* an information on the size of the field of view (e.g. “small FOV” = small field of view, i.e. one microwell with beads)</p> <p>* information at which approx. height of the microwell the image was taken (“TopOfWells” = close to the opening of the wells on the top)</p> <p>* sometimes information on the zoom of the Zen software (e.g. “7x”)</p>
Approximate Entropy of Spiking Series Reveals Different Dynamical States in Cortical Assemblies
<p>Self-organized criticality theory proved that information transmission and computational performances of neural networks are optimal in critical state. By using recordings of the spontaneous activity originated by dissociated neuronal assemblies coupled to Micro-Electrode Arrays (MEAs), we tested this hypothesis using Approximate Entropy (ApEn) as a measure of complexity and information transfer. We analysed 60 min of electrophysiological activity of three neuronal cultures exhibiting either sub-critical, critical or super-critical behaviour. The firing patterns on each electrode was studied in terms of the inter-spike interval (ISI), whose complexity was quantified using ApEn. We assessed that in critical state the local complexity (measured in terms of ApEn) is larger than in sub- and super-critical conditions. Our estimations were stable when considering epochs as short as 5 min. These preliminary results indicate that ApEn has the potential of being a reliable and stable index to monitor local information transmission in a neuronal network during maturation. Thus, ApEn applied on ISI time series appears to be potentially useful to reflect the overall complex behaviour of the neural network, even monitoring a single specific location.</p>
Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner
<p>The spiking activity of populations of cortical neurons is well described by a small number of population-wide covariance patterns, the "latent dynamics". These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable across behaviour: in each of the primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recording.</p>
Cortical mechanisms of smooth eye movements revealed by dynamic covariations of neural and behavioral responses
<p>Neural activity in the frontal eye fields controls smooth pursuit eye movements, but the relationship between single neuron responses, cortical population responses, and eye movements is not well understood. We describe an approach to dynamically link trial-to-trial fluctuations in neural responses to parallel variations in pursuit and demonstrate that individual neurons predict eye velocity fluctuations at particular moments during the course of behavior, while the population of neurons collectively tiles the entire duration of the movement. The analysis also reveals the strength of correlations in the eye movement predictions derived from pairs of simultaneously recorded neurons and suggests a simple model of cortical processing. These findings constrain the primate cortical code for movement, suggesting that either a few neurons are sufficient to drive pursuit at any given time or that many neurons operate collectively at each moment with remarkably little variation added to motor command signals downstream from the cortex.</p>
Repository for "Light-induced cortical excitability reveals programmable shape dynamics in starfish oocytes"
<p>Data and code repository for paper "Light-induced cortical excitability reveals programmable shape dynamics in starfish oocytes". DOI tbd.</p>
Data and code from: Dynamics of cortical contrast adaptation predict perception of signals in noise
<p>Neurons throughout the sensory pathway adapt their responses depending on the statistical structure of the sensory environment. Contrast gain control is a form of adaptation in the auditory cortex, but it is unclear whether the dynamics of gain control reflect efficient adaptation, and whether they shape behavioral perception. Here, we trained mice to detect a target presented in background noise shortly after a change in the contrast of the background. The observed changes in cortical gain and behavioral detection followed the dynamics of a normative model of efficient contrast gain control; specifically, target detection and sensitivity improved slowly in low contrast, but degraded rapidly in high contrast. Auditory cortex was required for this task, and cortical responses were not only similarly affected by contrast but predicted variability in behavioral performance. Combined, our results demonstrate that dynamic gain adaptation supports efficient coding in auditory cortex and predicts the perception of sounds in noise.</p>
Cortical and Biomechanical Dynamics of Ankle Robotics Training in Stroke
ClinicalTrials.gov study NCT01072032. IPD Sharing: NO. Countries: 1. Publications: 1.
Data and code from: Dynamics of cortical contrast adaptation predict perception of signals in noise
Open the record for dataset details and reuse information.
Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner
Open the record for dataset details and reuse information.
Data from: Slow cortical dynamics generate context processing and novelty detection
Open the record for dataset details and reuse information.
Dynamic changes in cortical Neurotrophic Factor-positive interneurons during sleep
<p>The Data provided is sufficient to replicate the analysis presented in "<strong><span>Dynamic changes in cortical </span><span>Neurotrophic Factor-positive interneurons</span><span> during sleep".</span></strong></p> <p><span>The data was collected using nVoke or nVista miniaturized microscopes (Inscopix/Brucker) and normalized by calculating dF/F0 where F0 is the mean over one imaging session. Each imaging session is normalized separately and then all sessions were concatenated (over 48h of the experiment). The cacium data then was synchronized with the EEG/EMG data, to obtain sleep stage information for the calcium data. To assure correct alignment, the raw data were timestamped when recording using TTLs.</span></p> <div> <h4>General Info on the Experiment</h4> <a href="https://github.com/christinemlmuheim/NDNF-INs-and-Sleep#general-info"></a></div> <p>Aim of the code is to analyze calcium activity of NDNF interneurons in layer 1 of V1 of mice over the 24h day and in different sleep stages. The data used to run this script contains timestamped and sleep-scored calcium recordings, aquired with nVoke or nVista systems from Inscopix/Bruker.</p> <div> <h4>Experimental Design:</h4> <a href="https://github.com/christinemlmuheim/NDNF-INs-and-Sleep#experimental-design"></a></div> <p>NDNF-cre mice were injected with a AAV-GECI, equipped with a cranial window over the injection site and EEG/EMG electrodes. For details, see the Manuscript Methods section.</p> <p>After adequate recovery time, animals were mounted with a baseplate to securely but reversibly attache the miniaturized microscope.</p> <p>Animals were recorded over 48h, whereof the first 24hrs were undisturbed (baseline, BL), followed by a 6h sleep deprivation (SD) and 18hrs recovery period. SD started at ZT0 when the lights were turned on.</p>
Multimodal Investigation of Cortico-Basal Ganglia-Thalamo-Cortical Network Dynamics in Dystonic Patients with Deep Brain Stimulation
ClinicalTrials.gov study NCT06716983. IPD Sharing: Not stated. Countries: 1. Publications: 13.
Fast cortical dynamics encode tactile grating orientation during active touch
Open the record for dataset details and reuse information.
Data from: The dynamic role of genetics on cortical patterning during childhood and adolescence
Longitudinal imaging and quantitative genetic studies have both provided important insights into the nature of human brain development. In the present study we combine these modalities to obtain dynamic anatomical maps of the genetic contributions to cortical thickness through childhood and adolescence. A total of 1,748 anatomic MRI scans from 792 healthy twins and siblings were studied with up to eight time points per subject. Using genetically informative latent growth curve modeling of 81,924 measures of cortical thickness, changes in the genetic contributions to cortical development could be visualized across the age range at high resolution. There was highly statistically significant (P < 0.0001) genetic variance throughout the majority of the cerebral cortex, with the regions of highest heritability including the most evolutionarily novel regions of the brain. Dynamic modeling of changes in heritability over time demonstrated that the heritability of cortical thickness increases gradually throughout late childhood and adolescence, with sequential emergence of three large regions of high heritability in the temporal poles, the inferior parietal lobes, and the superior and dorsolateral frontal cortices.
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.