Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like ...

Researchers have long been interested in how humans and animals make decisions by focusing on trial-and-error behavior ...

The strength of certain neural connections can predict how well someone can learn math, and mildly electrically stimulating these networks can boost learning, according to a study published in the ...

Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a ...

Artist Terence Broad makes AI produce images without any training data at all.

That takes a huge amount of time and energy—during his NeurIPS talk, Petersen said that training his networks takes hundreds of times longer than training conventional neural networks on GPUs.

Inspired by microscopic worms, Liquid AI’s founders developed a more adaptive, less energy-hungry kind of neural network. Now the MIT spin-off is revealing several new ultraefficient models.

Training algorithm breaks barriers to deep physical neural networks Date: December 7, 2023 Source: Ecole Polytechnique Fédérale de Lausanne Summary: Researchers have developed an algorithm to ...

“These are neural networks that can stay adaptable, even after training,” Hasani says in the video, which appeared online in January. When you train these neural networks, they can still adapt ...