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Artificial intelligence accurately charts sleep stages without intrusive brain sensors

Researchers have developed an artificial intelligence model capable of tracking a person’s sleep stages using only three ...
eLife

Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex

Single neurons in mouse sensorimotor cortex are organized by their activity features into distinct subpopulations with area-spanning footprints whose boundaries align closely with anatomical and ...
IEEE

Multi-Classification of Brain Tumor Images Using Deep Neural Network

Abstract: Brain tumor classification is a crucial task to evaluate the tumors and make a treatment decision according to their classes. There are many imaging techniques used to detect brain tumors.
GitHub

ResNeXt: Aggregated Residual Transformations for Deep Neural Networks

This repository contains a Torch implementation for the ResNeXt algorithm for image classification. The code is based on fb.resnet.torch. ResNeXt is a simple, highly modularized network architecture ...

The circuit that lets your brain think and see

Nuttida Rungratsameetaweemana is challenging a story neuroscience has told for decades. According to the conventional account, our eyes collect raw information and relay it through a series of nerves ...
News-Medical.Net

Sleep protects complex neural networks from drifting into states of permanent instability

Every animal that has ever been studied closely, from the fruit fly to the philosopher, surrenders each day to a state that ...
Tech Xplore

New smart bicycle can tell when riders mean to turn—and when they may be falling

Two-wheeled vehicles with conventional stability-control systems must lean to change direction, making it difficult for rider ...

Cochlea network model reveals how inner ear may sort sound from noise

Over 70 million people in the U.S. are impacted by hearing loss, and age-related hearing loss is the second most common ...
AlphaGalileo

AdapGNN: A New Framework Enhances Explainability of Graph Neural Networks for Molecular Property Prediction

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...
IEEE

Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Abstract: Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional ...