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235 results for “Stimulus”
Valence processing differs across stimulus modalities (Multi-echo)
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Dataset for Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity
<p>Per-trial dataset accompanying the publication Stauch, Peter, Schuler, and Fries (2020): Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity.<br> Additionaly, preprocessing code is provided as Codebase.zip.</p>
Infant N290 event-related potentials and stimulus-specific adaptation to face stimuli
<p>Data for publication: "Neural specialization to human faces at the age of 7 months".</p> <p>Further details will be available at: https://github.com/yrttiahoS/ssa/</p> <p> </p> <p>Metadata:</p> <p>I. Event-related potentials</p> <p>The current study investigated both cortical sensitivity and categorical specificity through event-related potentials (ERPs) previously implicated in face processing in 7-month-old infants (N290) and adults (N170). Using a category-specific repetition/adaptation paradigm, cortical specificity to human faces, or control stimuli (cat faces), was operationalized as changes in ERP amplitude between conditions where a face probe was alternated with categorically similar or dissimilar adaptors. In adults, increased N170 for human vs. cat faces and category-specific release from adaptation for face probes alternated with cat adaptors was found. In infants, a larger N290 was found for cat vs. human probes. Category-specific repetition effects were also found in infant N290 and the P1-N290 peak-to-peak response where latter indicated category-specific release from adaptation for human face probes resembling that found in adults.</p> <p>*Dataset files: N290_P1_infant, N170_P1_adult.sav</p> <p> </p> <p>*EEG data in EEGLAB’s format,</p> <p>Filenames indicate type of data with:</p> <p>1) Initial numerical code indicating (anonymized) participant number, "adult/infant" indicating participant group</p> <p>2) f1/c2/f3/c3 indicating stimulus conditions probe:face, adaptor:face / probe:cat, adaptor:cat / probe:face, adaptor:cat / probe:cat,adaptor:face, respectively</p> <p> 3) Final number _1_ or _2_ indicates block number for adult participants (in the order of presentation)</p> <p>Files with only codes 1) and 2) are continuous data with all triggers included</p> <p> </p> <p>*video-based Quality Control is listed in file: anon_vQC2022-09-22.xlsx*video-based Quality Control is listed in file: anon_vQC2022-09-22.xlsx</p> <p> </p> <p>*Analysis syntax: https://github.com/yrttiahoS/ssa/</p> <p> </p> <p>*Dataset updates: TBA at https://github.com/yrttiahoS/ssa/</p> <p> </p> <p> </p> <p>II. Temperament questionnaire</p> <p>*Data collection: Temperament data were collected from participants of an infant event-related potential (ERP) study as potential correlate of outcome variables and as a descriptor of the participant group. Participants were 7-month-old infants from families volunteering in the brain research study, which were contacted through information from a population registry sample. Infant temperament was assessed by parent-reported IBQ-R short form (Putnam, S. P., Helbig, A., Gartstein, M.A., Rothbart, M.K. & Leerkes, E. M. (2014). <em>Development and assessment of short and very short forms of the Infant Behavior Questionnaire-Revised.</em> Journal of Personality Assessment, 96, 445-458. Finnish translation: Professor Katri Räikkönen-Talvitie and the Developmental Psychology Research Group University of Helsinki, Finland)</p> <p>*Authors of the dataset: Santeri Yrttiaho, Mikko Peltola, Anneli Kylliäinen, Tiina, Parviainen, Jari Hietanen</p> <p>*Site of data collection: Human Information Processing laboratory, Tampere University, Finland</p> <p>*Funding: Emil Aaltonen Foundation, Tampere University, Academy of Finland</p> <p>*Participant demographics: Participant group is described by age of M(SD) = 30.5(0.5) weeks. Participants were from Tampere metropolitan area, Finland. Data were collected during Fall 2020 (October 16th – December 7th, 2020).</p> <p>*Data content. Initial data will contain group level statistics and the individual data will be made available as additional files after publication of study results in a peer-reviewed article. Data is anonymized. Individual data contains IBQ-R short form items, scales, and factors as well as participant gender and age in weeks. Data also includes variable indicating whether the participant was included in the final ERP analysis after EEG quality control.</p> <p>*variable names: see 'Scale abbreviations.docx' and for full discussion the original article by Putnam et al. (2014).</p> <p>*Dataset files</p> <p>1. IBQ-R-short-Finnish_dataset.sav / data of items, scales, and factors</p> <p>2. SSA_infant_ibq-r_summary.sps / syntax for computing scores</p> <p>3. IBQ-R-short-Finnish_descriptives.spv / output of descriptive statistics</p> <p>4. Scale abbreviations.docx / explanation of variable names</p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset: Auditory brainstem responses to varying stimulus presentation rates of 12 bat species in the wild and captivity
<p>Dataset for the Dataset Publication: Auditory brainstem responses to varying stimulus presentation rates of 12 bat species in the wild and captivity</p> <p>There are two datasets available: 1) the measured ABRs from Experiments 1 and 2 and 2) the extracted IOIs:</p> <ol> <li>ABR measurements:</li> </ol> <p>The filename of the ABR recordings from Experiment 1 include the species name, individual ID, sex, stimulus presentation rate (indicated as “modrate”) and recording day and time (yyyyddmm). Each recording file contains 256 measurements of the same stimulus and stimulus presentation rate in columns. An exemplary filename would be “Carollia_perspicillata_cp6male_modrate6_20190905T125903”, meaning that this is a recording of <em>Carollia perspicillata</em> individual cp6 of sex male, tested with a stimulus presentation rate of 6 Hz on the 09.05.2019, and the file was saved at 12:59:03 (the T between date and time stands for “Time”).</p> <p>The filename of the ABR recordings from Experiment 2 include the place of the Experiments (Bad Segeberg) and species name (<em>C. perspicillata</em>), individual ID, sex, stimulus presentation rate (indicated as “modrate”) and recording day and time (yyyymmdd; be aware, that the date format is different between Experiment 1 and 2). Each recording file contains 256 measurements of the same stimulus and stimulus presentation rate in columns. An exemplary filename would be “BadSegeberg_cper_1_male_modrate6_20200622T140952_stimulus_ST01_short ”, meaning that this is a recording of <em>Carollia perspicillata</em> individual cp6 of sex male, tested with a stimulus presentation rate of 6 Hz on the 09.05.2019, and the file was saved at 12:59:03 (the T between date and time stands for “Time”), the individual was presented with stimulus example 01 of the short stimuli.</p> <ol> <li>IOI recordings</li> </ol> <p>The recordings of Inter-Onset-Intervals are all in one single csv file and species and sequence ID is given per row, to be able to analyze the data further.</p> <p> </p>
The longer the first stimulus is explored in softness discrimination the longer it can be compared to the second one
<p>In haptic perception information is often sampled serially over a certain interval of time. For example, a stimulus is repeatedly indented to repeatedly estimate its softness. Albeit such redundant estimates are equally reliable, they seem to contribute differently to the overall haptic percept in a comparison task. When comparing the softness of two silicon rubber stimuli, the within-stimulus weights of estimates of the second stimulus' softness decrease during the exploration. Here we test the hypothesis that such decrease of weights depends on the representation strength of the first stimulus’ softness. We varied the length of the first stimulus’ exploration. Participants subsequently explored two silicon rubber stimuli by indenting the first stimulus (comparison) 1 or 5 times and the second stimulus (standard) always 3 times. We assessed the weights of indentation-specific estimates from the second stimulus by manipulating perceived softness during single indentations. Our results show that the longer the first stimulus is explored<br> the more estimates of the second stimulus' softness can be included in the comparison of the two stimuli. This suggests that the exploration length of the first stimulus determines the strength of its representation which influences the decrease of weights of indentation-specific estimates of the second stimulus.</p> <p> </p> <p>The Zip file contains all data relative to the publication. The data of each participant is contained in a separate folder. This folder contains a *.raw file for each session of the experiment and a "data" folder, which contains movement trajectories (*.trj files) and the staircase reversals for each condition (*.pse files) in separate folders for each session.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
Dataset: Measuring stimulus-evoked neurophysiological differentiation in distinct populations of neurons in mouse visual cortex
<p>This dataset contains the calcium imaging and behavioral data analyzed in our paper, "Measuring stimulus-evoked neurophysiological differentiation in distinct populations of neurons in mouse visual cortex".</p> <p>These data were obtained at the Allen Brain Observatory as part of the <em>OpenScope</em> project, which is operated by the Allen Institute.</p> <p>Analysis code is available at <a href="https://github.com/wmayner/openscope-differentiation">https://github.com/wmayner/openscope-differentiation</a>.</p>
[Stimulus Set] Evoking the N400 event-related potential (ERP) component using a publicly available novel set of sentences with semantically incongruent or congruent eggplants (endings)
<p>During speech comprehension, the ongoing context of a sentence is used to predict sentence outcome by limiting subsequent word likelihood. Neurophysiologically, violations of context-dependent predictions result in amplitude modulations of the N400 event-related potential (ERP) component. While N400 is widely used to measure semantic processing and integration, no publicly-available auditory stimulus set is available to standardize approaches across the field. Here, we developed an auditory stimulus set of 442 sentences that utilized the semantic anomaly paradigm, provided cloze probability for all stimuli, and was developed for both children and adults. With 20 neurotypical adults, we validated that this set elicits robust N400's, as well as two additional semantically-related ERP components: the recognition potential (~250 ms) and the late positivity component (~600 ms). This stimulus set (<a href="https://doi.org/10.5061/dryad.9ghx3ffkg">https://doi.org/10.5061/dryad.9ghx3ffkg</a>) and the 20 high-density (128-channel) electrophysiological datasets (<a href="https://doi.org/10.5061/dryad.6wwpzgmx4">https://doi.org/10.5061/dryad.6wwpzgmx4</a>) are made publicly available to promote data sharing and reuse. Future studies that use this stimulus set to investigate sentential semantic comprehension in both control and clinical populations may benefit from the increased comparability and reproducibility within this field of research.</p>
Astrocytic Gi-GPCR activation enhances stimulus-evoked extracellular glutamate
<p>Astrocytes perform critical functions in the nervous system, many of which are dependent on neurotransmitter-sensing through G protein-coupled receptors (GPCRs). However, whether specific astrocytic outputs follow specific GPCR activity remains unclear, and exploring this question is critical for understanding how astrocytes ultimately influence brain function and behavior. Here, we investigate the outputs of astrocytic Gi-GPCRs, a family of GPCRs which we previously showed is sufficient to increase slow-wave neural activity (SWA) during sleep when activated in cortical astrocytes<sup>1</sup>. We focus on two putative outputs by astrocytes <em>in vivo</em>, the regulation of extracellular glutamate and GABA, by combining fiber photometry recordings of the extracellular indicators iGluSnFR and iGABASnFR with astrocyte-specific chemogenetic Gi-GPCR activation. We find that Gi-GPCR activation does not change spontaneous dynamics of extracellular glutamate or GABA. However, Gi-GPCR activation does specifically increase visual stimulus-evoked extracellular glutamate. Together, these data point towards a complex relationship between astrocytic inputs and outputs <em>in vivo </em>that may depend on behavioral context. Further, they suggest an extracellular glutamate-specific mechanism underlying some astrocytic Gi-GPCR-dependent behaviors, including the regulation of sleep SWA.</p>
PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System
<p>Cities worldwide are growing, putting bigger populations at risk due to urban pollution. Environmental monitoring is essential and requires a major paradigm shift. We need green and inexpensive means of measuring at high sensor densities and with high user acceptance. We propose using phytosensing: using natural living plants as sensors. In plant experiments we gather electrophysiological data with sensor nodes. We expose the plant <em>Zamioculcas zamiifolia</em> to five different stimuli: wind, temperature, blue light, red light, or no stimulus. Using that data we train ten different types of artificial neural networks to classify measured time series according to the respective stimulus. We achieve good accuracy and succeed in running trained classifying artificial neural networks online on the microcontroller of our small energy-efficient sensor node. To indicate later possible use cases, we showcase the system by sending a notification to a smartphone application once our continuous signal analysis detects a given stimulus.</p> <p> </p> <p>Data repository for our paper "PhytoNodes for Environmental Monitoring: Stimulus Classification based on<br> Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System", submitted to the GoodIT conference. Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>raw_data: </em>The datasets from our plant experiments for the stimuli wind, temperature, red light, blue light, and no stimulus.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. Find the training and testing datasets in the archives folder as well as the trained classifiers in the results folder.</li> <li><em>classification_results.ods: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time).</li> <li><em>TFLite_Models: </em>The trained classifiers in TensorFlow Lite Format.</li> <li><em>00_AI_BLE_MeasuringOnlyWind: </em>Source code for classification on STM-based PhytoNodes (using MCDCNN two-class classifier) and Bluetooth communication. The code is written for the STM32WB55 Nucleo board and can be transferred to the dongle.</li> <li><em>zavrsniProjekt_iOS: </em>Source code of the iOS app used to receive data from the STM-based PhytoNodes.</li> <li><em>Watchplant_application_documentation.pdf: </em>Instructions to build and use the iOS app.</li> </ul>
Comparing the representation of a simple visual stimulus across the cerebellar network [dataset]
<p>This repository contains all the raw data that has been used to generate the figures in the paper "Comparing the representation of a simple visual stimulus across the cerebellar network".<br> To use the dataset, select all 10 .zip files provided here and unpack them simultaneously.</p> <p> </p> <p>The dataset contains the following folders:</p> <ul> <li>freely_swimming_beh folder contains the data for the freely swimming behavior experiments. Inside, subfolders for each behavioral experiment run can be found, containing the experiment metadata and the behavioral log.</li> <li>flashes folder contains the data for the imaging experiments with the FLASHES protocol. Within this folder, three subfolders can be found with the data for each one of the three different types of imaged cell types (GCs, IONs and PCs). Within those subfolders, a folder for each imaged fish can be found, containing a data dictionary with the extracted ROI traces, and a folder with the experiment metadata.</li> <li>steps folder contains the data for the imaging experiments with the STEPS protocol. Within this folder, three subfolders can be found with the data for each one of the three different types of imaged cell types (GCs, IONs and PCs). Within those subfolders, a folder for each imaged fish can be found, containing a data dictionary with the extracted ROI traces, and a folder with the experiment metadata.</li> <li>fitting folder contains data dictionaries with the results from the model fitting needed to reproduce the manuscript figures.</li> <li>data_dict4decoding_complete.h5 is a dictionary file containing all necessary data to reproduce the decoding experiments.</li> </ul>
BRAIN Journal-Isomorphism Between Estes' Stimulus Fluctuation Model and a Physical- Chemical System-Figure 1. Two compartments containing solution separated by a membrane.
<p>In fact, this equation will be found first if one consults physical or chemical textbooks for<br> diffusion. Also one may be able to find already-existing diffusion simulators to see vivid images of<br> the process.</p>
Figure 1. (a) Part of the acrylic structure where the patient is enclosed to avoid external stimulus; (b) Chin rest, corresponding proportions and measurements.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>The device consists mainly of an acrylic semi-spherical structure (Figure 1(a)) where visual<br> stimuli will be shown, according to a pre-designed therapy. Four servomotors will drive the lasers,<br> two inside the structure (short distances drive the lasers, two inside the structure (short distance<br> therapies) and two outside (middle-long distance therapies). A chin-rest must be used to have a<br> better line of sight fixation. A webcam with infrared light will catch the Purkinje-Sanson images to<br> identify the sight line (Borah, 2006; Halswanter, 2011; Pambakian et al., 2000). LabVIEW software<br> is used to control the device, including an audio stimulus along with an image-processing pipeline.<br> Finally a microcontroller is used to control the servo movements, laser beams and buzzers.</p>
BRAIN Journal-A Repeated Signal Difference for Recognising Patterns-Figure 3. Binary input stimulus dataset
<p>If the input value is 1, then the neuron is likely to be part of the cohesive set and would update the weight and also the global and local counts each event. If the input value is 0, then the neuron is not likely to be part of the cohesive set. For this case, it would not update the weight value as it is 0. It does update the global count as that uses unit increments, but only updates the local count when an earlier trigger switch tells it to. Each update event is also counted, so that averaged totals can be produced. </p>
Dataset: Stimulus selection drives value-modulated somatosensory processing in superior colliculus
<p>Pluta Lab</p> <p>.mat data files used in the following paper</p> <p>Stimulus selection drives value-modulated somatosensory processing in superior colliculus </p> <p>Details can be found in readme.txt</p>
Input data and analyzed data of "Topology of synaptic connectivity constrains neuronal stimulus representation (...)"
<p>This dataset contains the input data, as well as the analyzed data that our <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">preprint</a></p> <p><em><strong>Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies</strong></em></p> <p>to be found on <a href="https://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">bioRxiv</a> is based on. The input data (<em>input_data.zip</em>) contains everything that is needed to run the full <a href="https://github.com/BlueBrain/topological_sampling/">analysis pipeline</a> from start to the generation of the figures found in the manuscript. However, some of the analysis steps can be computationally heavy, so we also provide the output of these expensive steps, that can be simply used in conjunction with jupyter notebooks (<em>notebooks.zip)</em> to generate the figures.</p> <p><strong>Overview</strong></p> <p>An overview image can be found <a href="https://raw.githubusercontent.com/BlueBrain/topological_sampling/master/toposampling_pipeline_overview.png"><strong>here</strong></a></p> <p>Blue squares denote input / output files (that are part of this dataset). Grey circles denote steps of the analysis pipeline (that are implemented in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>). Red rectangles denote configuration files (that are part of this dataset and also in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>).</p> <p>This Dataset can also be browsed, downloaded and accessed as linked open data from the <a href="https://bbp.epfl.ch/nexus/web/studios/public/topological-sampling/studios/data:a7cc7e9f-53c5-4940-929c-95f4c4f57728?workspaceId=data:165e54c5-e8f6-4d85-ac94-53bc3dfe5cd4">BBP knowledge Graph based Data studios</a>.</p> <p><strong>Contained file types and their structure</strong></p> <p>Here, we provide four types of files. Configuration files specify analysis parameters and define the expected locations of the data files. Input files are the inputs into the analysis pipeline. Analyzed files are the outputs of said pipeline. Finally, we provide a number of jupyter notebooks that use the analyzed files to generate the manuscript figures. If you want to re-run the entire analysis pipeline, you need the code and configuration files from the <a href="https://github.com/BlueBrain/topological_sampling/">repository</a>, the input files and notebooks; the analyzed files will be generated as you run the pipeline. For information how to run this, refer to the <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a>. If you only want to generate the figures, you still need the code and configuration files from the repository, as it contains a package related to reading the result files; further, you need the analyzed files in addition to the input files. Of course, you can also run parts of the analysis pipeline and download the outputs for the rest.</p> <p>To run everything smoothly, the files have to be placed into the expected file structure. You can look up and configure the file structure in the configuration files. Below, we describe the default layout, which is very simple (<em>root</em> is where you placed the code from our <a href="https://github.com/BlueBrain/topological_sampling/">repository</a> and can be any location on your file system):</p> <ul> <li>Configuration files<em>: </em>Part of the <a href="https://github.com/BlueBrain/topological_sampling/">repository.</a> Placed into <em>root/working_dir/configs</em></li> <li>Input data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>input_data.zip</em> -- Input data. Contains details on the model used in the manuscript and the output (spike times) of the simulation described in the manuscript. Within the file: <ul> <li>For details, see <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a></li> </ul> </li> </ul> </li> <li>Analyzed data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>classifier_features_results.zip </em>-- Output of the "classifier" step. Results of stimulus classification on the data in <em>features.zip</em></li> <li><em>classifier_manifold_result</em>s.zip -- Output of the "classifier" step. Results of stimulus classification on the data in <em>extracted_components.zip</em></li> <li><em>community_database.zip</em> -- Output of "gen_topo_db". Various topological parameters related to the close neighborhood of neurons in the model</li> <li><em>extracted_components.zip </em>-- Output of "manifold_analysis". Results of factor analysis on the spike times in the <em>input_data</em></li> <li><em>features.zip</em> -- Output of "topological_featurization". A new dimensionality reduction method we introduce in the <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">manuscript</a></li> <li><em>split_spike_trains.zip -- </em>Output of "split_time_windows". The spike trains, split into time windows that are the responses to individual stimuli injected in the simulation</li> <li><em>structural_parameters.zip</em><em> -- </em>Output of "Structural tribe analysis". Values for the topological parameters in <em>community_database.zip</em> associated with the neuron samples specified in <em>tribes.zip</em></li> <li><em>structural_parameters_vol.zip</em> -- Output of "Structural tribe analysis". Same as above, but for volumetric neuron samples.</li> <li><em>triads.zip</em> -- Output of "Triad-counts". Over- and under-expression of triad motifs in the samples in <em>tribes.zip</em>.</li> <li><em>tribes.zip</em><em> -- </em>Output of "sample_tribes". Specific neuron samples that are then analyzed further.</li> </ul> </li> <li>Notebooks: Place into <em>root/notebooks</em> and unzip in place <ul> <li><em>notebooks.zip</em><em> -- </em>Jupyter notebooks. Run them to generate the figures in the manuscript.</li> </ul> </li> </ul> <p> </p> <p><strong>Updates:</strong></p> <p>v1.1.0 (2020/12/11): Added some additional control cases to the results for figure 7. These results will probably not be updated on bioRxiv, but go into the submission to a journal.</p> <p>v1.2.0 (2021/10/05): Updated the notebooks.zip with changes we made in response to reviewers' feedback.</p>
Data from: Measuring motivation for alfalfa hay in feedlot cattle using voluntary interaction with an aversive stimulus
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Stimulus-dependent representational drift in primary visual cortex
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A computational mechanism of cue-stimulus integration for pain in the brain
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[Stimulus Set] Evoking the N400 event-related potential (ERP) component using a publicly available novel set of sentences with semantically incongruent or congruent eggplants (endings)
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Astrocytic Gi-GPCR activation enhances stimulus-evoked extracellular glutamate
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ScienceDex guides
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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.