Scientists at UCSF combined advanced brain-network modeling, genetics, and imaging to reveal how tau protein travels through ...

"We present dissociative prioritized analysis of dynamics (DPAD), a nonlinear dynamical modeling approach that enables these capabilities with a multisection neural network architecture and training ...

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI.

Various generative AI models, including transformer-based models, GANs, and diffusion models, are trained through different processes involving large-scale data, neural networks, and methods like ...

Researchers trained a generative AI neural network to run the 1993 video game Doom instead of a conventional video game engine.

From chaotic random images generated with neural networks, which Google made accessible to the general public with Deep Dream in 2015, the journey went to almost photo-realistic images of the ...

Just as deep neural networks, transformers and diffusion models all made the leap from research curiosities to widespread deployment, features and principles from these other models will be seized ...

A recurrent neural network-based framework to non-linearly model behaviorally relevant neural dynamics by Ingrid Fadelli, Phys.org Editors' notes ...