New research from the University of St Andrews, the University of Copenhagen and Drexel University has developed AI ...

Neural networks have been powering breakthroughs in artificial intelligence, including the large language models that are now being used in a wide range of applications, from finance, to human ...

Mathematical models have long provided a robust framework for understanding the intricate processes underlying visual perception and neural processing. By combining principles from differential ...

Veronika Koren talks about pursuing a theory of neural coding that doesn’t fit a simple narrative, and the resilience it took to see it through.

The proposed RC method with generalized readout is based on generalized synchronization, offering enhanced accuracy and robustness while preserving the simplicity and efficiency of conventional RC.

A new study suggests that the brain processes information with high efficiency by synchronizing the physical wiring of neural networks with the varying speeds of local brain activity. Published in ...

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 ...

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...

OpenAI researchers are experimenting with a new approach to designing neural networks, with the aim of making AI models easier to understand, debug, and govern. Sparse models can provide enterprises ...