Neural networks have emerged as a powerful framework for addressing complex problems across numerous scientific domains. In particular, the interplay between neural network models and constraint ...

Recent advances at the intersection of neural networks and inverse scattering problems have transformed traditional approaches to imaging and material characterisation. Inverse scattering involves ...

Mark R. Anderson of Strategic News Service, the Future in Review Conferences and Pattern Computer. Popular methods of artificial intelligence have an “explainability problem” — the inability to see ...

Article reviewed by Grace Lindsay, PhD from New York University. Scientists design ANNs to function like neurons. 6 They write lines of code in an algorithm such that there are nodes that each contain ...

Binary digits and circuit patterns forming a silhouette of a head. Neural networks and deep learning are closely related artificial intelligence technologies. While they are often used in tandem, ...

Generative artificial intelligence (AI) — such as ChatGPT and Dalle-2 — is undoubtedly one of the most groundbreaking and discussed technologies in recent history. Its applications and related issues ...

For decades, dopamine has been celebrated in neuroscience as the quintessential "reward molecule"—a chemical herald of ...

The target article (Pater 2019) proposes to use neural networks to model learning within existing grammatical frameworks. This is easier said than done. There is a fundamental gap to be bridged that ...

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. Neural networks are the ...

As the name suggests, neural networks are inspired by the brain. A neural network is designed to mimic how our brains work to ...