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12 results for “Reservoir Computing”
Replication Data for: Achieving Liquid Processors by Colloidal Suspensions for Reservoir Computing
<p>Overview</p> <p>This README provides instructions for accessing and managing the SQLite database digits_channel_A.db, digits_channel_B.db, and digits_channel_C.db which contains the recordings for all the channels A, B, and C of the digital oscilloscope. The database includes a table named digits_data with information about each recording, including metadata and audio data.</p> <p><br>Database Schema<br>Table: digits_data</p> <p> id: INTEGER PRIMARY KEY<br> A unique identifier for each record.</p> <p> person_id: INTEGER<br> Identifier for the person associated with the recorded digit.</p> <p> digit: INTEGER<br> The digit recorded (0-9).</p> <p> repeat: INTEGER<br> The repeat count of the recording.</p> <p> audio: BLOB<br> Binary Large Object to store audio data.</p> <p>Prerequisites</p> <p> Python 3.x<br> sqlite3 library (included with Python standard library)</p>
A computer vision pipeline for predicting zoonotic reservoir distribution
<p>Shared here is the complete project repository of code and data supporting the manuscript, "Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision."</p> <p>Note that all project subdirectories (with the exception of the "Data" subdirectory) were compressed into single .zip files and thus need to be decompressed upon download. The "Data" subdirectory, which contains numerous large data files, was stored as a split zip archive to facilitate upload to Zenodo and consists of the files "Data.z01", "Data.z02", "Data.z03", and "Data.zip". These files may need to be decompressed using dedicated archiver software (such as The Unarchiver [https://theunarchiver.com/] on macOS). The "Data" subdirectory should be ~17 GB upon decompression. Note that the entire project repository is ~40 GB when uncompressed.</p>
In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks - Dataset
<p>This is the dataset of "<em>In materia</em> reservoir computing with a fully memristive architecture based on self-organizing nanowire networks"</p>
In materia implementation strategies of physical reservoir computing with memristive nanonetworks - Dataset
<p>This is the dataset of "In materia implementation strategies of physical reservoir computing with memristive nanonetworks"</p>
Connectome-based reservoir computing with the conn2res toolbox
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Experimental signals and processed data used in the research work "On-chip phonon-magnon reservoir for neuromorphic computing"
<p>The data set includes the raw experimental data, processed experimental data, and numerically modeled dependencies in the respective folders:</p><p>The raw experimental data (magnon readout, as measured) are given for all processed signals presented in the respective figures (folders 'Figure2', 'Figure3' and 'Sup Figure1') and used for the ANN training (folder '3x3Sets & AugmentedVisualShapes'). </p><p>The signals presented in the folders '\3x3Sets & AugmentedVisualShapes\3x3SET##' are named following the scheme shown in Fig. 3a. The signals in the folder \3x3Sets & AugmentedVisualShapes\RandomizedShapes' are simulated using the procedure described in the Methods section as "Drawing of randomized visual shapes". The sets of calculated statistical parameters used for the shapes' recognition are in the folder ''\3x3Sets & AugmentedVisualShapes\Parameters'</p><p>The waveforms for the trajectories formed by randomly selected 4, 5, 6, and 7 discrete positions and their statistical parameters are presented in the corresponding folder. </p>
Supporting data for "Scalable wavelength-multiplexing photonic reservoir computing"
<p>Supporting data for "Scalable wavelength-multiplexing photonic reservoir computing"</p>
Computational data of "Sodium Reservoir in the Deep Lower Mantle"
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Versatile and Robust Reservoir Computing with PWM-Driven Heterogenous R-C Circuits
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Dataset for: Biological neurons act as generalization filters in reservoir computing
<p>This dataset contains the fluorescence traces from calcium imaging.<br> The data are provided in csv format, wherein the value represents relative fluorescent unit (⊿F/F) of neuron i (arranged in columns) at t-th frame (arranged in rows).<br> The time resolution of the recording is 50 ms/frame.</p> <p>A journal paper is to be published in PNAS with full details of materials and methods.<br> https://www.pnas.org/doi/abs/10.1073/pnas.2217008120</p> <p><br> File naming convention <br> ・"Spon.csv" is a data obtained in a spontaneous activity recording.<br> ・The other file s are named according to the delivered stimulus.<br> ・For the static pattern recognition task, "StimA", "StimB", and "StimC" correspond to the stimulation of the top, middle, and bottom regions in Figure 1B.<br> ・"_half" and "_quarter" refers to stimulations with reduced areas, described in Figure S2A.<br> ・For the speech recognition task, "f1" and "m1" represent the gender of the speakers, and "zero", "one", ... are spoken digits.<br> ・"_reverse" refers to a stimulation to a temporally-reversed input. </p>
Raw data for Reservoir computing on silicon platform with a ferroelectric field-effect transistor
<p>Raw data of publication "Reservoir computing on silicon platform with a ferroelectric field-effect transistor"</p>
Neuromorphic nanowire netoworks for conformable reservoir computing
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ScienceDex guides
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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.