Thinking outside the box to fabricate customized 3D neural chips

Cultured neural tissues have been widely used as a simplified experimental model for brain research. However, existing ...
IEEE

Physics-Informed Neural Networks for Estimation of Scattered Sound Fields with Boundary Condition

Abstract: Sound field measurement helps us to understand the sound field, which is difficult to understand only by hearing. Recently, to reduce the measurement points, methods for estimating sound ...
powerelectronicsnews

Modeling SiC MOSFETs with Neural Networks

A recent Nature study shows that separated artificial neural networks can accurately model SiC MOSFETs using minimal training data. Silicon carbide MOSFETs are increasingly replacing traditional ...
Medical Xpress

Rethinking how our brains build the neural networks underlying motor memories

For every motor skill you've ever learned, whether it's walking or watchmaking, there is a small ensemble of neurons in your brain that makes that movement happen. Our brains trigger these ...
Frontiers

Convolutional neural networks decode finger movements in motor sequence learning from MEG data

1 Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia 2 Department of Learning, Data Analytics and Technology, Section Cognition, Data and ...
GitHub

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...
GitHub

Neural Network from Scratch

This project implements a neural network from scratch to classify handwritten digits using the MNIST dataset. The neural network is built using Python and utilizes libraries such as NumPy and ...
IEEE

Multi-Channel Disentangled Graph Neural Networks With Different Types of Self-Constraints

Abstract: Graph Neural Network (GNN) is a popular semi-supervised graph representation learning method, whose performance strongly relies on the quality and quantity of labeled nodes. Given the ...