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8 results for “Neuromorphic Computing”
Resistive switching in benzylammonium-based Ruddlesden–Popper layered hybrid perovskites for non-volatile memory and neuromorphic computing
<p><span>Structural, optoelectronic, and supplementary characterisation data for “</span><span>Resistive Switching in Benzylammonium-Based Ruddlesden-Popper Layered Hybrid Perovskites for Non-Volatile Memory and Neuromorphic Computing ”</span><span>, DOI:</span><span>10.1039/d3ma00618b</span><span>.</span></p>
Controllable temporal dynamics of titanium oxide memristor for analog time-based neuromorphic computing: Dataset
<p>Dataset used to produce graphs related to the temporal behavior of the Pt/TiO/Au memristors.</p>
Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2
<p>This data set contains all resources for the research project "<span>Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2" (published in Nature Communications (2024)).</span></p>
Hydrophobically gated memristive nanopores for neuromorphic computing
<p>The file named "experimental.zip" has the .abf files of the different electrophysiology experiments realized on the engineered FraC.</p><p>The file named "model_pore.zip" has the initial condition, the LAMMPS file to run the RMD simulations as well as the files required to compute the free energy, P1 and P2.</p><p>The file named "frac_md.zip" has the files to run the FraC simulations, as well as the files necessary to compute the free energy, P1 and P2.</p><p> </p>
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>
Data and code for the publication 'Fully Non-Linear Neuromorphic Computing with Linear Wave Scattering'
<p>This repository contains the source code for the paper <a href="https://arxiv.org/abs/2308.16181" rel="nofollow">https://arxiv.org/abs/2308.16181</a> on nonlinear neuromorphic computing via linear wave scattering as well as the source data for the figures in the paper.</p> <p>The idea behind this work is to send optical waves through a linear scattering system like an array of waveguides and optical resonators. These optical resonators or other elements may have tuneable parameters. These tuneable parameters now serve two functions in trying to use the system to solve a machine-learning task: Some of the parameters can be used to inject the input (e.g. images to be classified). Other parameters are trainable and will be slowly updated during training. The code given here simulates physical scattering setups, observes the scattering response for many different training samples, and updates the trainable parameters via gradient descent to minimize the deviation from the desired target output for the training samples. Evaluation of the scattering response as well as calculation of the gradients is done using jax, and training updates are implemented via jax or optax.</p> <p>See the two subdirectories for the code used in handwritten-digit recognition (a scaled-down version of MNIST) and for fashion-MNIST (with many more neurons and trainable parameters). This code can be run directly to reproduce the results shown in the figures (although a GPU is advisable). To run the code, you need to install jax and optax (and tensorflow for importing data sets).</p>
Wearable Epileptic Seizure Prediction and Alert Glasses Based on Neuromorphic Computing
ClinicalTrials.gov study NCT07139457. IPD Sharing: NO. Countries: 1. Publications: 0.
Neuromorphic nanowire netoworks for conformable reservoir computing
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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.