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Dataset results
33 results for “replay”
Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance
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Challenges in Replay Detection by TDLM in Post-Encoding Resting State
<p>Data for the paper "Challenges in Replay Detection by TDLM in Post-Encoding Resting State".</p> <p>This extension of the previous dataset contains the resting state data. For each participant, a 8 minutes resting state was recorded before and after the main experiment (localizer plus learning), and before the final retrieval session.</p> <p>Two files are uploaded per participant, the pre-experiment resting state (RS1) and the post-learning resting state (RS2). All files are MaxFiltered and the head positioning has been realigned using MaxFilter movement correction to the head position during the initial localizer. The localizer data has been previously published and can be downloaded in v1 of this dataset at https://doi.org/10.5281/zenodo.8001755</p> <p>All relevant information can be found in the related publication. Behavioural data necessary to reproduce the results will be uploaded to GitHub at https://github.com/CIMH-Clinical-Psychology/DeSMRRest-TDLM-Simulation</p> <p>There are markers in the files as follows:</p> <p>###############################################################<br>## Port Trigger Table<br>## Port Code | Meaning<br>## ---------------------------------------------------<br>## 0 | don't send trigger<br>## 10 | start RS session<br>## 11 | end RS session<br>## 127 | button press has happened<br>## 255 | start and end of session<br>###############################################################</p> <p> </p>
Offline replay supports planning in human reinforcement learning
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Composite of VREAL replays
<p>A 3-minute video clip that shows the beginning of an instruction sequence in a social VR game (<em>VREAL</em>), in which a player deploys another VR tool (<em>Tilt Brush</em>) to instruct a future player how to use the game to capture volumetrically their own gameplay. The composite video comprises two videos made up of 4 unique replays edited together in sync with the original voice track.</p>
Behavioral evidence for memory replay of video episodes in macaque monkeys
<p>Humans recall the past by replaying fragments of events temporally. Here, we demonstrate a similar effect in macaques. We trained six rhesus monkeys with a temporal-order judgement (TOJ) task and collected 5000 TOJ trials. In each trial, they watched a naturalistic video of about 10 s comprising two across-context clips, and after a 2-s delay, performed TOJ between two frames from the video. The monkeys apply a non-linear forward, time-compressed replay mechanism during the temporal-order judgement. In contrast with humans, such compression of replay is however not sophisticated enough to allow them to skip over irrelevant information by compressing the encoded video globally. We also reveal that the monkeys detect event contextual boundaries and such detection facilitates recall by an increased rate of information accumulation. Demonstration of a time-compressed, forward replay like pattern in the macaque monkeys provides insights into the evolution of episodic memory in our lineage</p>
Field-replay Based Load testing Reducing tool
<p>This is a replication package of the paper: Reducing the Length of Field-replay Based Load Testing. Included: source code, JMeter files, data set, figure and scale method introduction.</p> <p>Update: Use a new design of workload based on WorldCup98. Update all access logs and monitor files</p>
Example data set for Replay-triggered Brain-wide Activation in Humans
<p>Example data set for <em>Replay-triggered Brain-wide Activation in Humans </em></p> <p><em>Unzip the file and place it in the directory named "example_data_code".</em></p>
A scheduler log replayer and logs for BFTrainer evaluation
<p>This is a temporary repository to host source for paper: <code>BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes.</code></p> <p>Files</p> <ul> <li><code>BFTrainer-replay.py</code> the main program to replay real scheduler to evaluate our resource allocation algorithm</li> <li><code>jobs.py</code> implements functions to manage jobs.</li> <li><code>progCBC.py</code> or <code>progGRB.py</code> the implementation of mixed Integer linear programming using Gurobi optimizer (progGRB.py). We also open source our implementation (progGRB.py) using free optimizer (e.g., CBC, Pulp and JuMP). You can get an Trial Licenses or Free Academic Licenses from Gurobi if you want to run the current version. You need to adjust the import source in the <code>BFTrainer-replay.py</code> to use the CBC based solver.</li> <li><code>trace.py</code> has functions to manage scheduler logs for the replay evaluation.</li> </ul>
Experience-driven rate modulation is reinstated during hippocampal replay
<p>Replay, the sequential reactivation within a neuronal ensemble, is a central hippocampal mechanism postulated to drive memory processing. While both rate and place representations are used by hippocampal place cells to encode behavioral episodes, replay has been largely defined by only the latter – based on the fidelity of sequential activity across neighboring place fields. Here, we show that dorsal CA1 place cells in rats can modulate their firing rate between replay events of two different contexts. This experience-dependent phenomenon mirrors the same pattern of rate modulation observed during behavior and can be used independently from place information within replay sequences to discriminate between contexts. Our results reveal the existence of two complementary neural representations available for memory processes.</p>
Replaying the evolutionary tape to investigate subgenome dominance in allopolyploid Brassica napus
<p>Interspecific hybridization and allopolyploidization merge evolutionarily distinct parental genomes (subgenomes) into a single nucleus. A frequent observation is that one subgenome is "dominant" over the other subgenome, having a greater number of retained genes and being more highly expressed. Which subgenome becomes dominantly expressed in allopolyploids remains poorly understood. Here we "replayed the evolutionary tape" with six isogenic resynthesized<i> Brassica napus </i>(rapeseed) allopolyploid lines and investigated subgenome dominance patterns over the first ten generations post merger. We found that the same subgenome was consistently more dominantly expressed in all lines and generations and that >70% of biased gene pairs showed the same dominance patterns across all lines and an <i>in silico</i> hybrid of the parents. Gene network analyses indicated an enrichment for network interactions and several biological functions for the <i>Brassica oleracea </i>derived 'BnC' subgenome biased pairs, but no enrichment was identified for <i>Brassica rapa</i> derived 'BnA' subgenome biased pairs. Furthermore, DNA methylation differences between subgenomes mirrored the observed gene expression bias towards the 'BnC' subgenome in all lines and generations. These methylation patterns were consistent with those previously associated with higher expression, but differ from proposed mechanisms from recent conceptual models and with observations in other polyploid systems that exhibit subgenome dominance. Many of these differences in gene expression and methylation were also found when comparing the progenitor genomes, suggesting subgenome dominance is partly related to parental genome differences rather than just a byproduct of allopolyploidization. These findings demonstrate that "replaying the evolutionary tape" in an allopolyploid results in largely repeatable and predictable subgenome expression dominance patterns, partly due to preexisting genetic differences among the parental species.</p>
Data from: Behavioral evidence for memory replay of video episodes in the macaque
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Replaying the evolutionary tape to investigate subgenome dominance in allopolyploid Brassica napus
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Experience-driven rate modulation is reinstated during hippocampal replay
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Inhibitory plasticity supports replay generalization in the hippocampus
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Data from: Hippocampal replay of experience at real-world speeds
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Replay-triggered Brain-wide Activation in Humans
<p>The behavioural data, first-level and second-level fMRI statistical images, and EEG decoded time series are derived from "Replay-triggered brain-wide activation in humans". Please cite this paper if you use any of these data. </p> <p>Paper link:<br><a href="https://doi.org/10.1038/s41467-024-51582-5">https://doi.org/10.1038/s41467-024-51582-5 </a><br><br></p>
REinforcement of the Pancreas in distaL pAncreatectomY (REPLAY)
ClinicalTrials.gov study NCT03030170. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data and code for: Replay of innate vocal patterns during sleep in suboscines
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Composite of sixteen Mindshow replays
<p>A 30-second video clip that shows a complete instruction sequence with 16 synchronised unique replays in a social VR game (<em>Mindshow</em>), in which players record, edit and share staged scenes with avatar puppets that are controlled by the player.</p>
chameleon cholesky replay
replay static dump data replay with static scheduling logs from testing replay executions
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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.