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40 results for “prediction error”
Dataset: Neural correlates of error prediction in a complex motor task
<p>There are two files for each subject:</p> <p>1. errorsegments_sub##.mat -> Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target > 12 cm) in the task (segment and electrode information can be found below).</p> <p>2. hitsegments_sub##.mat -> Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target < 5 cm) in the task (segment and electrode information can be found below).</p> <p>The data in the *.mat-files are stored in a three dimensional matrix: 1300 datapoints x n segments x 14 electrodes</p> <p>datapoints: The first dimension contains 1300 data points for each segment which translates to 2600 ms (500 Hz sampling frequency). The time of the ball´s release set at the 301st data point in each segment.</p> <p>segments: The second dimension stands for the number of segments. Since the number of trials which satisfy the above described distance criterion for hit and error trials differ for participants size, n is variable. </p> <p>electrodes: The third dimension consists of the 14 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz]</p> <p> </p> <p> </p>
Dataset: Brain negativity as an indicator of predictive error processing: The contribution of visual action effect monitoring
<p>There are two files for each subject:</p> <p>1. sub##_error.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target > 12 cm) in the task (segment and electrode information can be found below).</p> <p>2. sub##_hit.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target < 7 cm) in the task (segment and electrode information can be found below).</p> <p><br> The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodes</p> <p>n represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726 (850 ms after ball release) in every segment.</p> <p>datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized,<br> i.e. hit/error #1 -> row 1 to 1400, hit/error #2 -> row 1401 to 2800, ..., hit/error #n -> (n-1) * 1400 + 1 to n * 1400</p> <p>electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]</p>
Dataset: Accuracy of Motor Error Predictions for Different Sensory Signals
<p>Supplementary Data for <em><strong>Accuracy of Motor Error Predictions for Different Sensory Signals </strong></em>article</p> <p>Dataset associated with the following publication:</p> <p>Joch, M., Hegele, M., Maurer, H., Müller, H., & Maurer, L. K. (2018). Accuracy of Motor Error Predictions for Different Sensory Signals. <em>Frontiers in Psychology</em>, <em>9 </em>(August), 1–13. https://doi.org/10.3389/fpsyg.2018.01376</p>
Relevance of predictive and postdictive error information in the course of motor learning.
<p>Dataset associated with the following publication:</p> <p>Maurer, LK, Joch, M, Hegele, M, Maurer, H, & Müller, H (2021). Relevance of predictive and postdictive error information in the course of motor learning. Neuroscience doi:10.1016/j.neuroscience.2021.05.007</p>
Supplements for "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"
<p><strong>Supplements for "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"</strong></p> <p>This supplement includes the following files:</p> <ul> <li>Main_v02.R --- Script in R language to process files (in "PDBs.zip") and produce the dataset ("Dataset_v02.csv")</li> <li>PDBs.zip --- PDB files include filtered structures processed by three programs</li> <li>Dataset_v02.csv --- final filtered version of dataset produced in R language (by "Main_v02.R"). </li> <li>Dataset key.txt --- key to column names in Dataset_v02.csv </li> </ul> <p>The article featuring this dataset is published in:</p> <p>Journal: Journal of Chemical Information and Modeling<br>Title: "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"<br>Author(s): Hameduh, Tareq; Mokry, Michal ; Miller, Andrew ; Heger, Zbynek; Haddad, Yazan</p> <p><a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpubs.acs.org%2Fdoi%2F10.1021%2Facs.jcim.3c00134&data=05%7C01%7C%7C4876a5c33e5f4bd8daf008db7e55a28d%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C638242678496577396%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=NE%2F0dYvc4m5%2B8T0YQtqxY21yk1pA9ZpiADqzx3vEJ4Q%3D&reserved=0">https://pubs.acs.org/doi/10.1021/acs.jcim.3c00134</a></p> <p> </p>
The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity
<p>Raw data and code to generate the figures of the publication "The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity" <a href="https://doi.org/10.7554/eLife.85111.2">https://doi.org/10.7554/eLife.85111</a>. </p>
Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses
<p>Raw data and code to reproduce figures in the manuscript "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses"</p> <p># README</p> <p>## Introduction</p> <p>This README provides essential information about the codebase for the manuscript titled "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses." The code in this repository is self-contained and is expected to run smoothly given the appropriate versions of the required libraries/packages.</p> <p>## Directory structure and execution details</p> <p>### R code</p> <p>- Main Figures 2A-2D, 3A-3C, and 4A-4E, as well as supplemental figures S2A-S2H, S3A-S3E, S4L, and S5A-S5I, were generated using R. Execute the `R_figs_master.r` script located in the `r_code` directory.<br> - All figures will be saved within the `r_code/code_generated_figures` directory.<br> - Note: Exact UMAP representations might vary across different hardware and operating systems, likely due to an issue with the UWOT package ([Reference Issue](https://github.com/satijalab/seurat/issues/5514)). If figures appear outside their designated plot ranges, set "FixAxes" to 'FALSE' in the `single_cell_variables.r` script.</p> <p>### MATLAB code</p> <p>- Main figures 1B, 1D-1F, and 6A-6H, as well as supplemental figures S1A-S1J and S6A-S6I, were generated using MATLAB (version 9.11.0.1809720 (R2021b) Update 1). Execute the `get_the_figs_matlab.m` script located in the `matlab_code` directory.<br> - All figures will be saved within the `matlab_code/code_generated_figures` directory.<br> - Required: [fca_readfcs, version 2020.06.22](https://ch.mathworks.com/matlabcentral/fileexchange/9608-fca_readfcs).</p> <p>### Python code</p> <p>- Figures 5B-5F panels were generated using Python (version 3.6.8). Run the `fig_5_analysis_code.py` script located in the `python_code` directory.<br> - All figures will be saved within the `python_code/code_generated_figures` directory.<br> - The preprocessed images located in `python_code/data_repository/Adamts2_processed`, `python_code/data_repository/Agmat_processed`, and `python_code/data_repository/Baz1a_processed` were generated using the ImageJ macro `python_code/cropped_to_processed_macro.ijm` from the raw images in `python_code/data_repository/Adamts2_cropped`, `python_code/data_repository/Agmat_cropped`, and `python_code/data_repository/Baz1a_cropped`.</p> <p>## Supplementary code (for reference only as raw data is not included)</p> <p>### Mapping code and genome construction code</p> <p>- Initial processing of Single-cell RNA-sequencing was performed with Cell Ranger, coordinated by the Python script:<br> `python_code/mapping_and_genome_construction/single_cell_mapping_pipeline.py`. Some components of this script are deprecated and were primarily used to pass .fastq files to Cell Ranger and organize the outputs.<br> - A custom genome was constructed to account for the expression of CaMPARI2 in the single-cell RNA-sequencing dataset:<br> `python_code/mapping_and_genome_construction/campari2_genome_construction.py`.<br> - Processing of Bulk RNA-sequencing, either single or paired-end, was executed through Python:<br> `python_code/mapping_and_genome_construction/bulk_single_end_mapping.py` and `python_code/mapping_and_genome_construction/bulk_paired_end_mapping.py`.<br> - A custom genome was constructed to account for the expression of various artificial promoter viruses:<br> `python_code/mapping_and_genome_construction/bulk_seq_genome_construction.py`.</p>
ND250 as a prediction error signal in orthographic processing: insights from the comparison of handwritten and printed words
<p><span>This dataset contains electroencephalography (EEG) recordings and behavioral data from a study investigating the neural mechanisms of visual word recognition in native Chinese speakers. The study used a color decision task, where participants viewed printed and handwritten Chinese single-character words varying in lexical frequency (high-frequency vs. low-frequency). The primary aim was to examine the N250 ERP component, a 250-ms difference in brain activity observed between certain word types, and determine whether it reflects activation of the orthographic lexicon or a prediction error signal during orthographic processing. The findings suggest that the N250 is related to prediction error, providing support for the Interactive Account of orthographic processing.</span></p>
Dataset from Makino H. and Suhaimi A. Distributed representations of temporally accumulated reward prediction errors in the mouse cortex.
<p>Dataset from the paper:</p> <p>Makino H. and Suhaimi A. Distributed representations of temporally accumulated reward prediction errors in the mouse cortex.</p> <p>Each variable is described in Description.pdf.</p> <p>Analysis code is available at <a href="https://github.com/HiroshiMakinoLaboratory/RPEAccumulation" target="_blank" rel="noopener">https://github.com/HiroshiMakinoLaboratory/RewardPredictionErrorAccumulation</a>.</p>
Action Prediction Error: a value-free dopaminergic teaching signal that drives stable learning - Behavioral dataset
<p>Behavioral data to reproduce figures of this paper: https://doi.org/10.1101/2022.09.12.507572</p> <p>See Github repository: https://github.com/HernandoMV/APE_paper</p>
Predictive coding and internal error correction in speech production
Open the record for dataset details and reuse information.
Data from: Predicted tracking error triggers catch-up saccades during smooth pursuit
For foveated animals, visual tracking of moving stimuli requires the synergy between saccades and smooth pursuit eye movements. Deciding to trigger a catch-up saccade during pursuit influences the quality of visual input. This decision is a trade-off between tolerating sustained position error when no saccade is triggered or a transient loss of vision during the saccade due to saccadic suppression. Although catch-up saccades have been extensively investigated, it remains unclear how the trigger decision is made by the brain. de Brouwer et al (2002) demonstrated that catch-up saccades were less likely to occur when the expected time to foveate a target using pursuit alone is between 40 and 180ms into the future, referred to as the smooth zone. However, this descriptive result lacks a mechanistic explanation for how the trigger decision is made. More recently, we proposed a decision model (Coutinho et al., 2018) that relies on a probabilistic estimation of predicted position error (PEpred) during visual tracking. To test the model predictions, we investigated how human participants combined predicted position error, retinal slip, and the uncertainty in those estimates to make trigger decisions. We found a significant effect of the pre-saccadic magnitude of PEpred on trigger time and occurrence of catch-up saccades. To test the role of uncertainty, we blurred the moving target which led to longer and more variable saccade trigger times and more smooth pursuit trials, consistent with model predictions. As predicted by our model, large PEpred (>10deg) produced early saccades regardless of the level of uncertainty while saccades preceded by small PEpred (<10deg) were significantly modulated by high uncertainty. Our model also predicted increased signal dependent noise as retinal slip increases, which resulted in longer saccade trigger times and more smooth trials. In conclusion, the data supports our hypothesized role of PEpred in deciding when to trigger a catch-up saccade during smooth pursuit while taking into account uncertainty in sensory estimates.
Multiple systems in macaques for tracking prediction errors and other types of surprise
<p>Data and code to reproduce the figures and major analyses in</p> <p>Grohn J, Schüffelgen U, Neubert F-X, Verhagen L, Sallet J, Kolling N, Rushworth MFS. Multiple systems in macaques for tracking prediction errors and other types of surprise. PLOS Biology. 2020.</p>
Data from: Detection error influences both temporal seroprevalence predictions and risk factors associations in wildlife disease models
Understanding the prevalence of pathogens in invasive species is essential to guide efforts to prevent transmission to agricultural animals, wildlife, and humans. Pathogen prevalence can be difficult to estimate for wild species due to imperfect sampling and testing (pathogens may not be detected in infected individuals and erroneously detected in individuals that are not infected). The invasive wild pig (Sus scrofa, also referred to as wild boar and feral swine) is one of the most widespread hosts of domestic animal and human pathogens in North America. We developed hierarchical Bayesian models that account for imperfect detection to estimate the seroprevalence of five pathogens (porcine reproductive and respiratory syndrome virus, pseudorabies virus, Influenza A virus in swine, Hepatitis E virus, and Brucella spp.) in wild pigs in the United States using a dataset of over 50,000 samples across nine years. To assess the effect of incorporating detection error in models, we also evaluated models that ignored detection error. Both sets of models included effects of demographic parameters on seroprevalence. We compared our predictions of seroprevalence to 40 published studies, only one of which accounted for imperfect detection. We found a range of seroprevalence among the pathogens with a high seroprevalence of pseudorabies virus, indicating significant risk to livestock and wildlife. Demographics had mostly weak effects, indicating that other variables may have greater effects in predicting seroprevalence. Models that ignored detection error led to different predictions of seroprevalence as well as different inferences on the effects of demographic parameters. Our results highlight the importance of incorporating detection error in models of seroprevalence and demonstrate that ignoring such error may lead to erroneous conclusions about the risk associated with pathogen transmission. When using opportunistic sampling data to model seroprevalence and evaluate risk factors, detection error should be included.
Intracranial and behavioral data from "Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex"
<p>Preprocessed intracranial EEG and behavioral data from Hoy, Quiroga-Martinez, et al. manuscript titled "Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex" published in Nature Communications (2023). Source data files for figures are included as well.</p>
Error Analysis of Kernel EDMD for Prediction and Control in the Koopman Framework
<p>This repository contains code and data to re-create the numerical results shown in</p> <p>"Error analysis of kernel EDMD for prediction and control in the Koopman framework"</p> <p> <a href="http://arxiv.org/abs/2312.10460">http://arxiv.org/abs/2312.10460</a></p> <p>Please see the README file for detailed description of the codes in this repository.</p>
Data for 'Cooperative thalamocortical circuit mechanism for sensory prediction errors'
<p>Data and code for 'Cooperative thalamocortical circuit mechanism for sensory prediction errors'</p> <p>Nature (2024)</p> <p>DOI: 10.1038/s41586-024-07851-w</p>
Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality [Dataset]
<p>The calcium imaging data of the telencephalon of head-tethered adult zebrafish during GO/NOGO tasks in the virtual reality environment and the behavior data were deposited.</p> <p>The codes to process the neural activity data by calcium imaging to perform Non-negative Matrix Factorization </p> <p>For details, see "Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality" Torigoe et al., Nature Communications in press.</p>
Nigrostriatal dopamine signals sequence-specific action-outcome prediction errors
<p>Data for Hollon et al. (2021) Nigrostriatal dopamine signals sequence-specific action-outcome prediction errors. <em>Current Biology</em></p>
Prediction error, prior certainty, or belief updating: P3a component function in temporal Bayesian inference
<p>Data for "<strong>Prediction error</strong><strong>, prior certainty, or belief updating: P3a component function in temporal Bayesian inference</strong>"</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.