A key objective of several neuroscience studies is to understand and model how the dynamics of distinct populations of neurons give rise to specific human and animal behaviors. Many existing methods ...

Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate with ...

For decades, researchers have been trying to understand the biological roots of autism spectrum disorder (ASD), a common ...

A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-infectious-recovered-vaccinated (SEIRV) model was developed to enhance the understanding of the temporal evolution ...

This study presents a neural network-based framework for COVID-19 transmission prediction and healthcare resource optimization. The model achieves high prediction accuracy by integrating ...

Neurologists use millisecond-level M/EEG tracking to prove the human brain and AI language models organize and predict language using parallel processing principles.

Researchers build fleeting memory transformers with human-like memory decay, proving memory limits help AI learn grammar ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...

The Russian government has greenlighted a new bill designed to regulate and support the development of artificial ...

Adrian de Wynter is an AI scientist at Microsoft and a researcher at the University of York. In addition to studying the ...

Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...

A hunk of material bustles with electrons, one tickling another as they bop around. Quantifying how one particle jostles others in that scrum is so complicated that, beginning in the 1990s, physicists ...