Neural modeling and simulation are foundational tools in computational neuroscience, enabling researchers to explore how neural systems process information, ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

To address these limitations, we introduce a novel framework: the Molecular Merged Hypergraph Neural Network (MMHNN). MMHNN ...

A hybrid approach to AI is powering Amazon’s Rufus shopping assistant and cutting-edge warehouse robots.

Addiction is a complex and multifaceted condition that encompasses physiological, psychological, and environmental factors. Advancements in human ...

Researchers have discovered that people with Alzheimer’s show unusually high neural flexibility—the constant reorganizing of brain networks—even while resting.

Over the course of their lives, humans can sometimes acquire fear responses to specific stimuli, animals, objects or ...

CMS employed machine learning to probe rare Higgs decays into charm quarks. The search produced the most stringent limits so far. The Higgs boson, first observed at the Large Hadron Collider (LHC) in ...

Teacher Shen Yaping, as the main speaker of this training, brought great expectations due to her background. As a certified mentor from Tsinghua ... force of AI technology lies in deep learning and ...

This important work sets out to identify the neural substrates of associative fear responses in adult zebrafish. Through a compelling and innovative paradigm and analysis, the authors suggest brain ...