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80 results for “neural coding”
The neural segmentation of linguistically relevant rhythmic patterns: Data and Code
<p>Materials used in "The neural segmentation of linguistically relevant rhythmic patterns".</p><ul><li>EEG dataset</li><li>Stimuli</li><li>Code used for experiment control (task)</li><li>Code used for statistical modeling</li><li>Code used for EEG and acoustic analyses</li><li>Code used for data visualization</li></ul>
Code and partial data used in "Vertically recurrent neural networks for sub-grid parameterization"
<p>This repository contains the RNN training and evaluation code used in the paper<em> Vertically recurrent neural networks for sub-grid parameterization</em></p> <p> </p> <ul> <li> The radiative transfer emulation data can be accessed with through a Climetlab plugin (<a href="https://pypi.org/project/climetlab-maelstrom-radiation/">Climetlab-maelstrom-radiation</a>). <p>Datasets are downloaded and explained in the demo notebook here <a href="https://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynb" rel="nofollow">https://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynb</a></p> In addition the full training and testing code used in the paper is uploaded here (<em>pu-maelstrom-radiation.tar.gz</em>).</li> <li> </li> </ul> <p>Three parameterization problems from earlier studies are also included (we have modified the code from these papers to incorporate RNNs): </p> <ul> <li>non-orographic gravity wave drag (<a href="https://doi.org/10.1029/2021MS002477">Chantry et al. 2021</a>) <ul> <li>Based on TensorFlow</li> <li>This repository uses the <em>CliMetLab </em>plugin and<strong> downloads the data from the European Weather Cloud</strong></li> </ul> </li> <li>non-local parameterization (<a href="https://doi.org/10.1029/2022MS002984">Wang et al. 2022</a>) <ul> <li>The new code is based on TensorFlow, so you'll need both PyTorch and TensorFlow to run everything</li> <li><strong>See original paper for data access</strong></li> </ul> </li> <li>moist physics (Han et al. <a href="https://doi.org/10.1029/2022MS003508">2023</a>, <a href="https://doi.org/10.1029/2020MS002076">2020</a>) <ul> <li>Based on TensorFlow and PyTorch. This one has the most additions, e.g. code to generate a TensorFlow TFRecord dataset from the raw netCDF data archived in the original paper, autoregressive training and experimental model architectures in PyTorch</li> <li><strong>See original paper for data access</strong></li> </ul> </li> </ul> <p>Each of the code repos (unpack the tars) have an updated README.</p> <p>References:</p> <table> <tbody> <tr> <td> <div>Chantry, M., Hatfield, S., Dueben, P., Polichtchouk, I., & Palmer, T. (2021). Machine learning emulation of gravity wave drag in numerical weather forecasting. <em>Journal of Advances in Modeling Earth Systems</em>, <em>13</em>(7), e2021MS002477</div> <div> </div> <div> <div>Han, Y., Zhang, G. J., Huang, X., & Wang, Y. (2020). A moist physics parameterization based on deep learning. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>(9), e2020MS002076.</div> </div> <div> </div> <div>Han, Y., Zhang, G. J., & Wang, Y. (2023). An ensemble of neural networks for moist physics processes, its generalizability and stable integration. <em>Journal of Advances in Modeling Earth Systems</em>, <em>15</em>(10), e2022MS003508</div> <div> </div> <div>Wang, P., Yuval, J., & O’Gorman, P. A. (2022). Non‐local parameterization of atmospheric subgrid processes with neural networks. <em>Journal of Advances in Modeling Earth Systems</em>, <em>14</em>(10), e2022MS002984.</div> </td> </tr> <tr></tr> </tbody> </table> <div> </div>
Principal component decomposition of acoustic and neural representations of time-varying pitch reveals adaptive efficient coding of speech covariation patterns
<p>Data and scripts reported by Llanos and colleagues in their Brain and Language study </p>
Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES)
<p>Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES). Detailed description of the files in README.txt.</p>
Polyconvex inelastic Constitutive Artificial Neural Networks: Source code and data
<p>This dataset contains the source code of the polyconvex extension of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:</p> <p>Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl.<em> Polyconvex inelastic Constitutive Artificial Neural Networks.</em></p> <p> </p> <p><strong>Results:</strong> Discovering a model for the polymer VHB 4910 subjected to cyclic loading</p> <p>Here, we investigate the ability of the polyconvex iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.</p> <p>The experimental data are taken from the literature:</p> <p>Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. <em>Computational Materials Science</em>, <em>59</em>, 65-74.</p> <p><a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a></p>
Data and code for training neural network parameterizations from an near-global aqua-planet simulation
<p>This commit contains the code, coarse-grained data, processed training data, neural network models, and coupled NN-GCM simulations. It can be extracted by running</p> <pre><code>tar xzf <archive></code></pre> <p>While this archive contains code (it is slightly out of date). This is the up-to-date code: <a href="https://zenodo.org/record/3248586">https://zenodo.org/record/3248586</a></p> <p>Move the "nn", "debiased", and "data" folders from this archive into that code directory.</p> <p> </p> <p> </p>
Data in support of the article "Orthogonal neural codes for speech in the infant brain"
<p>Each subject has a dedicated .zip directory containing:</p> <p>[A] the EEG data in the original .raw format (recorded and converted from .mff files with the NetStation 5.3 software);</p> <p>[B] an event file with 6 columns reporting in order:</p> <ul> <li><strong>the onset time of the events (i.e. syllables) in samples</strong></li> <li>zeros (only useful when working with MNE Python)</li> <li>event codes (useful when working with MNE Python)</li> <li><strong>the id of the event</strong><strong> (i.e. syllable: e.g. ‘bi_f’ where the last letter corresponds to speaker’s gender)</strong></li> <li>the name of the precise speech token presented, specifying its duration (in ms)</li> <li>inter-stimulus-intervals (ISI, i.e. time lag in ms from syllable offset to next onset)</li> </ul> <p><em>Additional material</em></p> <p>The .txt file contains the <em>xyz</em> coordinates of the prototype EEG net employed. Being a custom and unique net, please note that such coordinates are approximate (and not perfectly symmetrical). In order to use them it is necessary to drop the channels E125 to E128 (EOG), not employed for the experiment.</p> <p>In scripts.zip there is a demonstration of how to use the event file (with MNE Python) to construct epochs and Python codes for the decoding analyses reported in the paper.</p> <p><em>Useful to know:</em></p> <p>For the original publication, EEG data was pre-processed with a preliminary version of <a href="https://www.biorxiv.org/content/10.1101/2021.05.21.445085v1">APICE</a> (see the Methods section of the paper for more details). A more advanced version of the pre-processing pipeline is now available <a href="https://github.com/neurokidslab/eeg_preprocessing">here</a>.</p>
Code and extensive data for training neural networks for radiation, used in "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0""
<p>Data and code used in a paper submitted to JAMES titled :<em> Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System</em></p> <p>1) The files <strong>ml_training_*.7z</strong> contain extensive datasets (in NetCDF format) for training neural network versions of the RRTMGP gas optics scheme as described in the paper. The datasets are read by <a href="https://github.com/peterukk/rte-rrtmgp-nn/blob/main/examples/rrtmgp-nn-training/ml_train.py">ml_train.py.</a></p> <p>2) The ML datasets were in turn generated using the input profiles (in NetCDF format) inside <strong>inputs_to_RRTMGP.zip </strong>by running the Fortran programs <code>rrtmgp_sw_gendata_rfmipstyle.F90 and rrtmgp_lw_gendata_rfmipstyle.F90 </code>in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em>, which call the RRTMGP gas optics scheme, The input profiles contain <strong>millions of columns, hundreds of perturbation experiments (including hypercube-sampled gas concentrations), are derived from several different data sources (including CAMS reanalysis, GCM, and CKDMIP-MMM), and span present-day, preindustrial, and future atmospheric conditions.</strong> They could be used to generate training data for developing emulators of the full RTE+RRTMGP radiation scheme, not just gas optics (see nn_dev on the <a href="https://github.com/peterukk/rte-rrtmgp-nn">RTE+RRTMGP-NN repository on Github</a>, used in a previous paper where different emulation methods were compared)</p> <p>3) The Fortran and Python code used for data generation and NN training are found in<a href="https://github.com/peterukk/rte-rrtmgp-nn/tree/main/examples/rrtmgp-nn-training"> <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em> </a>on the main branch on Github; <strong>an archived version is also included here </strong>(<strong>rte-rrtmgp-nn-2.0.zip</strong>). See the readme in the above sub-directory for further information.</p> <p> </p>
Data from: Fine-grained neural coding of bodies and body parts in human visual cortex
Open the record for dataset details and reuse information.
Code and data for "An integrated microwave neural network for broadband computation and communication"
<div> <div> </div> </div> <div> <div> <div> <div> <div> <div> <p>This repository contains code and data used in the presentation of results in the article "An integrated microwave neural network for broadband computation and communication". The contents of the zipped files are:</p> <ul> <li><strong>Spectrum Analyzer Outputs (Datasets and ML scripts for digital emulation, radar and signal encoding classification.zip)</strong>: Reduced-bandwidth outputs used to train the backend for results presented in Figs. 3 and 4 and Supplementary Fig. 3.</li> <li><strong>Simulation Code (Coupled mode simulation of integrated MNN.zip) </strong>: For modeling the coupled MNN system shown in Fig. 2 and Extended Figs. 4 and 5.</li> <li><strong>Radar Signal Simulation (Training data and code for simulating dynamic targets in simulated airspace.zip)</strong>: Code to simulate received baseband signals from radar targets.</li> </ul> <p>Each folder contains readme files on how to run the code and analyze data.</p> <p>Please install a recent Python release (https://www.python.org/downloads/) and a recent release of MATLAB (https://www.mathworks.com/help/install/) to run the code. No non-standard hardware is required. </p> </div> </div> </div> </div> </div> </div>
MATLAB Codes for: A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults
<p><strong>Description:</strong></p> <p>This repository contains the MATLAB codes used in our paper [1] on fault diagnosis of inter-shaft aircraft bearings, published by MDPI Machines. The codes encompass all the necessary materials to reproduce the findings outlined in the paper. </p> <p><strong>Dataset Access:</strong></p> <p>The dataset utilized in this study is available under request from the authors of reference [8] in our paper. To obtain the dataset, please follow the instructions provided by the respective authors.</p> <p><strong>Data Format:</strong></p> <p>The dataset is saved in '*.npy' 3D variable format. To reproduce this study, it is necessary to transform these variables to '.mat' format since the codes are implemented in MATLAB. You can find the codes for transferring the 3D '*.npy' files to '*.mat' files here [<a href="../records/10184606">here</a>]</p> <p>We appreciate your interest in our work.</p> <p>[1] Berghout, Tarek, Toufik Bentrcia, Wei Hong Lim, and Mohamed Benbouzid. 2023. "A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults" <em>Machines</em> 11, no. 12: 1089. https://doi.org/10.3390/machines11121089</p>
Data and code from "Nanoporous graphene-based thin-film microelectrodes for in vivo high-resolution neural recording and stimulation"
<p>Data and code for reproducing the main results of the paper "Nanoporous graphene-based thin-film microelectrodes for in vivo high-resolution neural recording and stimulation".</p>
Dataset and codes: Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network
Open the record for dataset details and reuse information.
Dataset related to "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes"
<p>This is an accompanying dataset to the article with the title "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes" (DOI: <a href="https://doi.org/10.1101/2024.03.15.585196">10.1101/2024.03.15.585196</a>). It contains structural and activity data related to the morphologically detailed model of the rat somatosensory cortex (<a href="https://doi.org/10.1016/j.cell.2015.09.029" target="_blank" rel="noopener">Markram et al., 2015</a>), refered to as "BBP" data in the article. Specificaly, following data items are included:</p> <p><strong>Simulation data:</strong> <code>simulation.xz</code></p> <p><em>"Reliability" protocol:</em></p> <p>Separate folders <code>BlobStimReliability_O1v5-SONATA_<circuit-name></code> with simulation data using the baseline and all manipulated connectomes respectively (see Technical info below), each of which containing:</p> <ul> <li><code>working_dir/connectome.h5</code>: Connectivity matrix in <code>ConnectivityMatrix</code> format, which can be loaded using <a href="https://github.com/BlueBrain/ConnectomeUtilities" target="_blank" rel="noopener">ConnectomeUtilities</a>.</li> <li><code>working_dir/raw_spikes_exc_<sim>.npy</code>: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column). One file for each of the 10 simulations with different simulator seeds, i.e., <sim> is 0..9.</li> <li><code>working_dir/stim_stream.npy</code>: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.</li> <li><code>working_dir/time_windows.npy</code>: Time windows in ms in numpy .npy format, corresponding to the stimulus train.</li> <li><code>working_dir/processed_data_store.h5</code>: Data store in HDF5 format with preprocessed spike signals (e.g., required for <em>Gaussian kernel reliability</em> computations), which contains... <ul> <li><code>spike_signals_exc</code>: Group of simulations datasets "sim_0" to "sim_9", each of which is an array of size <#gids x #t_bins> and contains binned spike signals filtered with a Gaussian kernel.</li> <li><code>sigma</code>: Sigma in ms of Gaussian kernel used for smoothing.</li> <li><code>gids</code>: List of excitatory neuron GIDs.</li> <li><code>t_bins</code>: List of time bins in ms.</li> <li><code>firing_rates</code>: Average firing rates per simulation (0..9; rows) and (excitatory) neuron GID (columns); average firing rates were computed as the inverse of the mean inter-spike interval per neuron.</li> </ul> </li> </ul> <p><em>"Classification" protocol:</em></p> <p>Single folder <code>Toposample_O1v5-SONATA</code> with simulation data using the baseline connectome, stored in a format compatible with the <a href="https://github.com/JasonPSmith/TriDy" target="_blank" rel="noopener">TriDy</a> (<a href="https://doi.org/10.1162/netn_a_00228">Conceição et al., 2022</a>) and <a href="https://github.com/BlueBrain/topological_sampling/tree/merge_samples" target="_blank" rel="noopener">TopoSampling</a> (<a href="https://doi.org/10.1371/journal.pone.0261702">Reimann et al., 2022</a>) pipelines, containing:</p> <ul> <li><code>toposample_input/connectivity.npz</code>: Sparse connectivity matrix in Compressed Sparse Column format , which can be loaded using <code>scipy.sparse.load_npz</code>.</li> <li><code>toposample_input/neuron_info.pickle</code>: Pandas dataframe in pickle format, which can be loaded using <code>pandas.read_pickle</code>, containing additional information about each neuron.</li> <li><code>toposample_input/raw_spikes_exc.npy</code>: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column).</li> <li><code>toposample_input/stim_stream.npy</code>: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.</li> <li><code>toposample_input/time_windows.npy</code>: Time windows in ms in numpy .npy format, corresponding to the stimulus train.</li> </ul> <p> </p> <p><strong>Classification data:</strong> <code>classification.xz</code></p> <p>Selected neighborhoods and classification results for the "PCA" method (<a href="https://github.com/BlueBrain/topological_sampling/tree/merge_samples" target="_blank" rel="noopener">TopoSampling</a> pipeline) as well as the "network_based" method using active subnetworks (<a href="https://github.com/JasonPSmith/TriDy" target="_blank" rel="noopener">TriDy</a> pipeline, using <a href="https://github.com/jlazovskis/TriDy-tools" target="_blank" rel="noopener">TriDy-tools</a> wrapper), stored as:</p> <p><em>"PCA" method:</em></p> <ul> <li><code>PCA/community_database_PCA.pkl</code>: Pandas dataframe in pickle format, containing the binary selection of 50 neighborhood centers (neuron GIDs; rows) for the different selection parameters (columns).</li> <li><code>PCA/classification_results_PCA.pkl</code>: Pandas dataframe in pickle format, containing the classification accuracies for all selection parameters (rows) and 6 cross-validation folds plus mean (columns).</li> </ul> <p><em>"network_based" method:</em></p> <ul> <li><code>network_based/selections_reliability.pkl</code>: Pandas dataframe in pickle format, containing different combinations of first/second selection parameters for the <em>double selection procedure</em> (rows) together with neuron indices (w.r.t. the EXC subcircuit) of the corresponding 50 neighborhood centers (chief0..49; columns).</li> <li><code>network_based/partition_reliability.npy</code>: Partition indices in numpy .npy format required to launch the pipeline using <a href="https://github.com/jlazovskis/TriDy-tools" target="_blank" rel="noopener">TriDy-tools</a>, which is an array of 50 neuron indices (w.r.t. the full circuit!) of the neighborhood centers (columns) for each combination of selections as in <code>selections_reliability.pkl</code> (rows).</li> <li><code>network_based/results/...</code>: Subfolder containing a list of pickle files with the classification results using different featurization parameters, as indicated by the filename. Each file contains a Pandas dataframe with the classification accuracies and numbers of (non-zero) features (columns) for each combination of selections as in <code>selections_reliability.pkl</code> (rows).</li> </ul> <p> </p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Data and code for "Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity"
<p>Data and code for "Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity"</p> <p>Nature Communications; 2024 Nov 24;15(1):10185. doi: 10.1038/s41467-024-54742-9.</p>
Data and code from "Full bandwidth electrophysiology of seizures and epileptiform activity enabled by flexible graphene micro-transistor depth neural probes"
<p>Data and python code for reproducing the main results of the paper "Full bandwidth electrophysiology of seizures and epileptiform activity enabled by flexible graphene micro-transistor depth neural probes."</p>
Pre-trained neural network model for pruning example code
<p>Pre-trained neural network models for example codes of neural network pruning.</p> <p>Example pruning codes are published in "https://github.com/FujitsuResearch/automatic_pruning".</p>
Generating Realistic Vulnerabilities via Neural Code Editing: An Empirical Study
<p>Using a commonly used synthetic dataset and one real-world dataset, we investigate the potential and gaps of three state-of-the-art neural code editors (Graph2Edit, Hoppity, SequenceR) for DL-based realistic vulnerability data generation, and two state-of-the-art vulnerability detectors (Devign, ReVeal) to evaluate the effectiveness of the generated realistic vulnerability data.</p> <p>Once the users have Docker installed download the Docker image "neural_editors_vulgen_docker.tar.xz".</p> <p>Then, check the README.md for detailed steps of reproducing the experiments.</p> <p>Besides, we also provide the simple package of the artifact "neural_editors_vulgen.zip". The raw data of our experiments is also provided in this simple package. However, using it to reproduce the experiments requires the users to set up the enviroments and dependencies for all the five tools, which is not recommanded.</p>
Raw neural codes and binsizes for the paper 'Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus'
<p>Raw optimal spiking codes and binsizes that maximise information theoretic quantities for the figures of the paper 'Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus' (PLoS Comput Biol. 2024 Feb 20;20(2):e1010706) . Additional data will be added in the coming months.</p>
Distinct neural population code and causal roles of primate caudate nucleus in multimodal decision-making
<p>To replicate the results in the paper, you should:</p> <ol> <li>Download and then gunzip the dataset.</li> <li>Download the analysis code at https://github.com/ZacZeng/CN-causally-contributes-to-MSDM.</li> <li>Run code as the README in Git says to get figures in the preprint paper (<a href="https://www.biorxiv.org/content/10.1101/2024.09.03.610907v1">Distinct neural manifolds and critical roles of primate caudate nucleus in multimodal decision-making | bioRxiv</a>).</li> </ol>
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.