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95 results for “long term memory”
Lateralisation of short- and long-term visual memories in an insect
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Processing in working memory boosts long-term memory representations and their retrieval
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Data for "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"
<p><strong>Data for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"</strong></p> <p>GitHub: <a href="https://github.com/gauchm/mts-lstm">https://github.com/gauchm/mts-lstm</a></p> <p>This dataset contains the hourly NLDAS forcings and USGS streamflow data.</p> <p>For training with our codebase, we recommend using the combined NetCDF file, but you can also use the csv files (but it will take much longer to load the data).</p> <p> </p> <p><em>Related Datasets: </em><a href="https://doi.org/10.5281/zenodo.4071885">https://doi.org/10.5281/zenodo.4071885</a> contains the models trained with the forcings and streamflow from this dataset.</p>
Models and Predictions for "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"
<p><strong>Models and Predictions for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"</strong></p> <p>GitHub: <a href="https://github.com/gauchm/mts-lstm">https://github.com/gauchm/mts-lstm</a></p> <p><strong>Results</strong></p> <p>The file `results.tar.gz` contains:</p> <ul> <li>ensembled predictions for all models (generated from the models in `models/` using the <a href="https://neuralhydrology.readthedocs.io/en/latest/api/neuralhydrology.utils.nh_results_ensemble.html">`nh-results-ensemble` command</a>). These predictions were used in the `results-analysis.ipynb` and `odelstm-analysis.ipynb` notebooks on the GitHub repository for the paper.</li> <li>the NWM predictions <ul> <li>`nwm_chrt_v2_1h.p` contains hourly NWM predictions for the CAMELS basins between 1993 and 2007. The file is derived from the reanalysis on <a href="https://docs.opendata.aws/nwm-archive/readme.html">aws</a>.</li> <li>`nwm_results.p` is derived from `nwm_chrt_v2_1h.p` and contains hourly and day-aggregated results and performance metrics for the test period of our paper.</li> </ul> </li> <li>a file `signatures.p` with hydrologic signatures that were calculated from the models' predictions. These signatures were used in the `results-analysis.ipynb` notebook on the GitHub repository for the paper.</li> </ul> <p><strong>Models</strong></p> <p>The tar.gz files prefixed with `models-` contain the trained MTS-LSTM, sMTS-LSTM, and ODE-LSTM models from our experiments. For each experiment, there exist 10 model setups (one for each random seed).<br> Besides the trained models, each model's tar.gz also contains the predictions on the test or validation perod and the configuration file used to train the model.</p> <p><em>MTS-LSTM</em></p> <ul> <li>`mtslstm_seed*` -- the MTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`mtslstm_multiforcing_seed*` -- the MTS-LSTM from the section on per-timescale input data, experiment "multi-forcing B" (using just NLDAS as hourly inputs)</li> <li>`mtslstm_multiforcing_dailyhourly_seed*` -- the MTS-LTSM from the section on per-timescale input data, experiment "multi-forcing A" (ingesting daily forcings into the hourly model)</li> <li>`mtsltsm_136H1D_seed*` -- the MTS-LTSM from the section on prediction at other timescales (1-, 3-, 6-hourly and daily predictions)</li> </ul> <p><em>sMTS-LSTM</em></p> <ul> <li>`smtslstm_seed*` -- the sMTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`smtslstm_noregularization_seed*` -- the sMTS-LSTM from the section on cross-timescale consistency (trained without regularization)</li> </ul> <p><em>Time-Continuous Experiments</em></p> <p>The file `models-timecontinuous.tar.gz` contains one sub-folder per basin on which we conducted our initial experiments.<br> Each basin directory contains:</p> <ul> <li>Experiment A (trained on daily and 12-hourly, evaluated on hourly): <ul> <li>`odelstm_a_seed*` -- the ODE-LSTM from experiment A</li> <li>`mtslstm_a_seed*` -- the MTS-LSTM from experiment A</li> </ul> </li> <li>Experiment B (trained on hourly and 3-hourly, evaluated on daily) <ul> <li>`odelstm_b_seed*` -- the ODE-LSTM from experiment B</li> <li>`mtslstm_b_seed*` -- the MTS-LSTM from experiment B</li> </ul> </li> </ul> <p><em>Related Datasets: </em><a href="https://doi.org/10.5281/zenodo.4072700">https://doi.org/10.5281/zenodo.4072700</a> contains the hourly NLDAS forcings and USGS streamflow required to use the models from this dataset.</p>
Learning strategies and long-term memory in Asian short-clawed otters (Aonyx cinereus) data
<p>Data submitted here, are those used in the writing of our manuscript entitled "Learning strategies and long-term memory in Asian short-clawed otters (<i>Aonyx cinereus</i>)" which has been submitted to Royal Society Open Science for publication. Abstract for that manuscript is below</p> <p>Social learning, namely learning from information acquired from others or their products, is widespread throughout the animal kingdom. There is growing evidence that animals selectively employ 'social learning strategies', which for example, determine when<i> </i>they should copy others instead of learning asocially, and whom they should copy. Furthermore, once animals have acquired new information, it is beneficial for them to commit it to long-term memory, especially when it concerns the discovery of profitable resources. Research into social learning strategies and long-term memory has covered a wide range of taxa. However, otters (subfamily Lutrinae), popular in zoos due to their sociability and playfulness, remained neglected until a recent study provided evidence of social learning in captive smooth-coated otters (<i>Lutrogale perspicillata</i>), but not in Asian short-clawed otters (<i>Aonyx cinereus</i>). We investigated Asian short-clawed otters' learning strategies and long-term memory performance in a foraging context. We presented novel extractive foraging tasks twice to captive family groups and used network-based diffusion analysis to provide evidence of social learning and long-term memory in this species. A major cause of wild Asian short-clawed otter declines is prey scarcity. Furthering our understanding of how they learn about and remember novel food sources could inform key conservation strategies.</p>
Dataset for Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes
<p>This data set is created for a purpose of publication "<span>Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes". It contains of dataset of kicks and lstm models for predictions a force of kicks upon IMU data. Detailed description of file names are in readme file. Folders are divided into specific kicks - turning or side kick in sport or traditional versions.</span></p>
Long-term memory T cells as preventive anticancer immunity elicited by TuA-derived heteroclitic peptides
<p>The host’s immune system may be primed against antigens during the lifetime (e.g. microorganisms antigens—MoAs), and swiftly<br> recalled upon growth of a tumor expressing antigens similar in sequence and structure. C57BL/6 mice were immunized in a preventive<br> setting with tumor antigens (TuAs) or corresponding heteroclitic peptides specific for TC-1 and B16 cell lines. AQ1 Immediately or 2-<br> months after the end of the vaccination protocol, animals were implanted with cell lines. The specific anti-vaccine immune response as<br> well as tumor growth were regularly evaluated for 2 months post-implantation. The preventive vaccination with TuA or their<br> heteroclitic peptides (hPep) was able to delay (B16) or completely suppress (TC-1) tumor growth when cancer cells were implanted<br> immediately after the end of the vaccination. More importantly, TC-1 tumor growth was significantly delayed, and suppressed in 6/8<br> animals, also when cells were implanted 2-months after the end of the vaccination. The vaccine-specific T cell response provided a<br> strong immune correlate to the pattern of tumor growth. A preventive immunization with heteroclitic peptides resembling a TuA is able<br> to strongly delay or even suppress tumor growth in a mouse model. More importantly, the same effect is observed also when tumor<br> cells are implanted 2 months after the end of vaccination, which corresponds to 8 – 10 years in human life. The observed potent tumor<br> control indicates that a memory T cell immunity elicited during the lifetime by a antigens similar to a TuA, i.e. viral antigens, may<br> ultimately represent a great advantage for cancer patients and may lead to a novel preventive anti-cancer vaccine strategy.</p>
Experimental data for: Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels
<p>Experimental data set for: Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels. Provides all raw data needed to reproduce experimental graphs presented within the paper.</p>
Concept Drift Datasets for - LSTM-SCCM: Long Short-Term Memory Stream Cruise Control Method for Automated Drift Adaptation
<p><strong>Datasets Guide:</strong></p> <p><strong>Drift Locations:</strong> Abrupt marks sudden drift onset. Incremental drift recurs every $K$ points. Gradual shows drift as a sequence of concepts c1 and c2.</p> <p><strong>E.D.</strong>: Euclidean distance between coefficients in space. </p> <p><strong>C-Start</strong>: Start Concept.</p> <p><strong>C-End</strong>: End Concept.</p> <p><strong>E.D. (Consec.)</strong>: E.D. between each two consecutive concepts in sequence.</p> <p> </p> <table style="border-collapse: collapse; width: 100.042%; height: 627px;"><colgroup><col style="width: 12.4949%;"><col style="width: 12.4949%;"><col style="width: 12.4949%;"><col style="width: 12.4949%;"><col style="width: 12.4949%;"><col style="width: 12.4949%;"><col style="width: 12.4949%;"><col style="width: 12.4949%;"></colgroup> <tbody> <tr style="height: 58.7812px;"> <td style="height: 58.7812px;"><strong>Dataset</strong></td> <td style="height: 58.7812px;"><strong>Drift Type</strong></td> <td style="height: 58.7812px;"><strong>Data Points</strong></td> <td style="height: 58.7812px;"><strong>Dimensions</strong></td> <td style="height: 58.7812px;"><strong>Noise</strong></td> <td style="height: 58.7812px;"><strong>Drift Locations</strong></td> <td style="height: 58.7812px;"><strong>E.D</strong><br><strong>(C-Start</strong><br><strong>to C-End)</strong></td> <td style="height: 58.7812px;"><strong>E.D.</strong><br><strong>(Consec.)</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>DS1</strong></td> <td style="height: 19.5938px;">Abrupt</td> <td style="height: 19.5938px;">1k</td> <td style="height: 19.5938px;">2</td> <td style="height: 19.5938px;">10</td> <td style="height: 19.5938px;">500</td> <td style="height: 19.5938px;">128.05</td> <td style="height: 19.5938px;">128.05</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>DS2</strong></td> <td style="height: 19.5938px;">Abrupt</td> <td style="height: 19.5938px;">10k</td> <td style="height: 19.5938px;">10</td> <td style="height: 19.5938px;">20</td> <td style="height: 19.5938px;">5k</td> <td style="height: 19.5938px;">327.23</td> <td style="height: 19.5938px;">327.23</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>DS3</strong></td> <td style="height: 19.5938px;">Abrupt</td> <td style="height: 19.5938px;">20k</td> <td style="height: 19.5938px;">50</td> <td style="height: 19.5938px;">30</td> <td style="height: 19.5938px;">10k</td> <td style="height: 19.5938px;">344.95</td> <td style="height: 19.5938px;">344.95</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>DS4</strong></td> <td style="height: 19.5938px;">Abrupt</td> <td style="height: 19.5938px;">100k</td> <td style="height: 19.5938px;">500</td> <td style="height: 19.5938px;">50</td> <td style="height: 19.5938px;">50k</td> <td style="height: 19.5938px;">324.11</td> <td style="height: 19.5938px;">324.11</td> </tr> <tr style="height: 78.375px;"> <td style="height: 78.375px;"><strong>DS5</strong></td> <td style="height: 78.375px;">Incremental</td> <td style="height: 78.375px;">1k</td> <td style="height: 78.375px;">2</td> <td style="height: 78.375px;">10</td> <td style="height: 78.375px;">every 100</td> <td style="height: 78.375px;">399.93</td> <td style="height: 78.375px;">[4.9, 6.1, 7.9,10.5, 14.8, 22.2, 37.0, 74.0, 222.1]</td> </tr> <tr style="height: 97.9688px;"> <td style="height: 97.9688px;"><strong>DS6</strong></td> <td style="height: 97.9688px;">Incremental</td> <td style="height: 97.9688px;">10k</td> <td style="height: 97.9688px;">10</td> <td style="height: 97.9688px;">20</td> <td style="height: 97.9688px;">every 1k</td> <td style="height: 97.9688px;">1205.10</td> <td style="height: 97.9688px;">[14.8, 18.5, 23.9, 31.8, 44.6, 66.9, 111.5, 223.1, 669.5]</td> </tr> <tr style="height: 97.9688px;"> <td style="height: 97.9688px;"><strong>DS7</strong></td> <td style="height: 97.9688px;">Incremental</td> <td style="height: 97.9688px;">20k</td> <td style="height: 97.9688px;">50</td> <td style="height: 97.9688px;">30</td> <td style="height: 97.9688px;">every 2k</td> <td style="height: 97.9688px;">1439.39</td> <td style="height: 97.9688px;">[17.7, 22.2, 28.5, 38.0, 53.3, 79.9, 133.2, 266.5, 799.6]</td> </tr> <tr style="height: 78.375px;"> <td style="height: 78.375px;"><strong>DS8</strong></td> <td style="height: 78.375px;">Incremental</td> <td style="height: 78.375px;">100k</td> <td style="height: 78.375px;">500</td> <td style="height: 78.375px;">50</td> <td style="height: 78.375px;">every 10k</td> <td style="height: 78.375px;">1671.75</td> <td style="height: 78.375px;">[20.6, 25.7, 33.1, 44.2, 61.9, 92.8, 154.7, 309.5]</td> </tr> <tr style="height: 78.375px;"> <td style="height: 78.375px;"><strong>DS9</strong></td> <td style="height: 78.375px;">Gradual</td> <td style="height: 78.375px;">1k</td> <td style="height: 78.375px;">2</td> <td style="height: 78.375px;">10</td> <td style="height: 78.375px;">[250-c1, 100-c2, 100-c1, 200-c2, 100-c1, 250-c2]</td> <td style="height: 78.375px;">601.92</td> <td style="height: 78.375px;">[377.4, 399.9, 829.2, 829.2, 624.36]</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>DS10</strong></td> <td style="height: 19.5938px;">Gradual</td> <td style="height: 19.5938px;">10k</td> <td style="height: 19.5938px;">10</td> <td style="height: 19.5938px;">20</td> <td style="height: 19.5938px;">[2.5k-c1, 1k-c2, 1k-c1, 2k-c2, 1k-c1, 2.5k-c2]</td> <td style="height: 19.5938px;">2049.14</td> <td style="height: 19.5938px;">[1158.3, 1205.1, 1885.4, 1885.4, 2167.7]</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>DS11</strong></td> <td style="height: 19.5938px;">Gradual</td> <td style="height: 19.5938px;">20k</td> <td style="height: 19.5938px;">50</td> <td style="height: 19.5938px;">30</td> <td style="height: 19.5938px;">[5k-c1, 2k-c2, 2k-c1, 4k-c2, 2k-c1, 5k-c2]</td> <td style="height: 19.5938px;">1756.43</td> <td style="height: 19.5938px;">[1469.9, 1439.3, 1875.3, 1875.3, 1843.6]</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>DS12</strong></td> <td style="height: 19.5938px;">Gradual</td> <td style="height: 19.5938px;">100k</td> <td style="height: 19.5938px;">500</td> <td style="height: 19.5938px;">50</td> <td style="height: 19.5938px;">[25k-c1, 10k-c2, 10k-c1, 20k-c2, 10k-c1, 25k-c2]</td> <td style="height: 19.5938px;">1364.12</td> <td style="height: 19.5938px;">[2126.2, 2032.1, 1238.2, 1238.2, 1322.0]</td> </tr> </tbody> </table> <p> </p>
Fig. 1 in Long-term memory in the parasitoid Trichogramma telengai Sorokina, 1987 (Hymenoptera: Trichogrammatidae)
Fig. 1. The performance of T. telengai in each of three tests: a — the percentage (%) of time spent by T. telengai in the target sector in each of the three tests (M ± SE), t-test: *p <0.05. b — the learning index in each of the three tests (M ± SE), t-test: *p <0.05. The column headers indicate the sample sizes. Рис. 1. ПокаЗатели перемеЩениЯ T. telengai в каждом иЗ трех тестов: a — процент (%) времени, проведенного T. telengai в целевом секторе в каждом иЗ трех тестов (M ± SE), t-test: *p <0.05. b — индекс обучениЯ в каждом иЗ трех тестов (M ± SE), t-test: *p <0.05. В основании столбцов укаЗаны объемы выборок.
No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity
<p>This is the EEG and behavioral data for the long-term memory ping study by Sander van Bree, Abbie Sarah Mackenzie, and Maria Wimber.</p> <p>Paper title: No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity</p> <p>The behavioral data is named "behav_res_pp", which is the output of script 1 (s0_extractdata.m) on Github. The EEG data is named "pp_reorder" and it is the output of script 5 (s5_correctdata.m); i.e., it is both preprocessed and correctly formatted for the main analyses.</p> <p>Move the behavioral data to folder /data/behav_data/ and the EEG data to folder /data/eeg_data/</p> <p>For the analysis scripts and more information, check Github: https://github.com/sandervanbree/MemPing</p>
Acute and Long Term Effects of VNS on Memory in Patients With Refractory Epilepsy
ClinicalTrials.gov study NCT05031208. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Long-term Effects of Visual Spatial Working Memory Training Program Performed at Preschool Age in Very Preterm Infants With Visual Spatial Working Memory Deficit. A Randomized Controlled Trial
ClinicalTrials.gov study NCT02757794. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Major Memory 2: A Long-term Group Cognitive Stimulation Program
ClinicalTrials.gov study NCT04178564. IPD Sharing: NO. Countries: 1. Publications: 14.
Data from: Crop identity and memory effects on aboveground arthropods in a long-term crop rotation experiment
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Learning strategies and long-term memory in Asian short-clawed otters (Aonyx cinereus) data
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Nutraceutical Effects on Long-Term Memory
ClinicalTrials.gov study NCT01963767. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Magnesium efflux from Drosophila Kenyon Cells is critical for normal and diet-enhanced long-term memory
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Pharmacological inhibition of CDK4/6 augments long-term anti-tumor immunity through the induction of T cell memory [01_mouse_TIL]
GEO Series GSE182650. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
Pharmacological inhibition of CDK4/6 augments long-term anti-tumor immunity through the induction of T cell memory [02_patient_samples]
GEO Series GSE182651. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
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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.