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5 results for “single-trial”
InterTVA. Single-trial beta maps for event-related voice localizer.
<p>The InterTVA dataset has been acquired with two main objectives. First, from a neuroscientific perspective, it aims at studying the inter-individual differences observed in people's ability at performing voice perception and voice identification tasks. Secondly, from a methodological perspective, it should allow benchmarking multi-view machine learning methods. Indeed, it includes several MRI modalities: anatomical MRI, diffusion MRI and several sessions of functional MRI -- one resting state run, one event-related voice localizer run (passive listening of vocal and non-vocal sounds), and four runs during which the subject performed a voice identification task.</p> <p>The present dataset contains a pre-processed version of the data acquired during the event-related voice localizer run. A GLM was performed using one regressor for each of the 144 trials, during each of which the participant was passively listening to a vocal or non-vocal stimulus. We therefore provide 144 beta maps for 39 subjects out of the 40 participants (one was excluded for excessive motion). This allows performing group-level MVPA using an inter-subject pattern analysis (ISPA) approach, as described in the following paper:</p> <p>Q. Wang, B. Cagna, T. Chaminade, et S. Takerkart, « Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA », <em>NeuroImage</em>, vol. 204, p. 116205, janv. 2020. https://doi.org/10.1016/j.neuroimage.2019.116205</p> <p>The source code to perform ISPA is also available: http://www.github.com/SylvainTakerkart/inter_subject_pattern_analysis</p> <p>If you use this data, please cite the following paper which describes it exhaustively:</p> <p>V. Aglieri, B. Cagna, P. Belin, S. Takerkart, "Single-trial fMRI activation maps measured during the InterTVA event-related voice localizer. A data set ready for inter-subject pattern analysis", Data in Brief, vol. 29, p. 105170, April 2020. https://doi.org/10.1016/j.dib.2020.105170</p>
Single-trial learning leads to mid-term memory formation in an ant during an appetitive but not an aversive task
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Data from: Using matrix and tensor factorizations for the single-trial analysis of population spike trains
Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.
Data from: Using matrix and tensor factorizations for the single-trial analysis of population spike trains
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Decibel/Regeneron Single-Trial ABR Waveforms used in ABRpresto
<p>This is a dataset of single-trial ABR data with ABR thresholds judged by expert human raters. It was used to test the <a href="https://github.com/Regeneron-RGM/ABRpresto/">ABRpresto algorithm</a>, which is reported <a href="https://www.biorxiv.org/content/10.1101/2024.10.31.621303v2">here</a> and in submission. It contains a total of 7,857 ABR waveform stacks from 351 mice. It includes mice with normal hearing and hearing loss from multiple strains and genotypes, totaling 123,140 unique ABR waveforms.</p> <p>The data is in psi format, example code in the <a href="https://github.com/Regeneron-RGM/ABRpresto/blob/main/scripts/Example_convert_psi_to_csv.py">ABRpresto github</a> illustrates how to convert it to csv if desired. It also includes two csv files:</p> <ul> <li>"ABRpresto thresholds 10-29-24.csv" contains the resulting thresholds generated by running the dataset through the ABRpresto algorithm.</li> <li>"Manual Thresholds.csv" contains the thresholds picked by expert human raters using an interactive GUI similar to that available <a href="https://github.com/bburan/abr">here</a>. It also contains min_level and max_level columns that describe the minimum and maximum levels tested for each dataset.</li> </ul> <p> See <a href="https://github.com/Regeneron-RGM/ABRpresto/blob/main/scripts/Example_plot_performance.py">Example_plot_performance.py </a>for an example of how to use these files to plot algorithm performance.</p> <p>Data were collected using a customized version of Psiexperiment, a plugin-based framework for auditory experiments (Buran and David, 2020, https://zenodo.org/records/3661386). Five-millisecond tone-pips with a 0.5 ms rise-fall time delivered at 81/s in alternating polarity were used for frequency-specific measurements of hearing function. Sound levels were tested in 5 dB steps, at either a 15–105 dB, 15–85dB, or 65–105 dB sound pressure level (SPL) range. Stimulus frequencies ranged from 4 to 45.3 kHz in half-octave steps. Stimuli were presented in interleaved order such that a train of tone-pips containing a single presentation of each level and frequency was repeated in an interleaved ramp paradigm (as described in Buran et al. 2020). Electrical signals were recorded through a digitizing amplifier (TDT RA4PA or Medusa4Z). Responses from 512 repetitions were recorded, and individual response to each trial and averaged responses were stored for further analysis. All experiments were approved by the Animal Care and Use Committee of Decibel Therapeutics.</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.