As research into photonic computing progresses, scientists seek to optimize the performance of optical computing devices by making purpose-specific changes to their design. A team led by Bo Wu and ...

Knowledge graph completion (KGC) aims to fill in missing entities and relations within knowledge graphs (KGs) to address their incompleteness. Most existing KGC models suffer from knowledge coverage ...

Predictive models of neural activity have a long tradition in neuroscience. Such models have many uses, from making predictions of responses to new stimuli, to developing normative and prescriptive ...

The study's authors note that small neural networks—simplified versions of the neural networks typically used in commercial AI applications—can predict the choices of animals much better than ...

A team at Carnegie Mellon University is helping kids understand artificial intelligence with a soft, squishy, LED-lit neural network. "Everyone, even middle schoolers, needs to know a little about ...

Theories of attractor dynamics have been successful at capturing several brain functions 5, including motor planning 6 and neural representations of space 7,8. Attractors are a set of states towards ...

Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like telling a leaf apart from a rock. But they have struggled to build ...

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 ...

The study's authors note that small neural networks—simplified versions of the neural networks typically used in commercial AI applications—can predict the choices of animals much better than ...