Frontiers

Predicting Neural Activity: Understanding Brain Structure and Dynamics through Generative Modeling and Causal Analysis

Understanding how the brain anticipates future states and transmits or reconstructs information remains a central challenge in neuroscience. This Research ...

New AI uncovers hidden patterns in biomedical knowledge graphs

A new artificial intelligence (AI) method called BioPathNet helps researchers systematically search large biological data ...
IEEE

Dynamical Causal Graph Neural Network for EEG Emotion Recognition

Abstract: Recently, topological graphs based on structural or functional connectivity of brain network have been utilized to construct graph neural networks (GNN) for Electroencephalogram (EEG) ...
Frontiers

Multimodal graph neural networks in healthcare: a review of fusion strategies across biomedical domains

1 Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA, United States 2 Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States ...
Hosted on MSN

Deep neural network from scratch in Python – fully connected feedforward tutorial

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...
Hosted on MSN

Understanding forward propagation in neural networks with Python – step by step

Learn how forward propagation works in neural networks using Python! This tutorial explains the process of passing inputs through layers, calculating activations, and preparing data for ...
IEEE

Uncertainty-Aware Graph Neural Networks: A Multihop Evidence Fusion Approach

Abstract: Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features. Existing GNNs, unfortunately, fail to account for the uncertainty of ...