In a separate post, Behrouz claimed that based on internal testing on the BABILong benchmark (needle-in-a-haystack approach), ...

Researchers developed COMET, a deep learning framework that leverages electronic health records and omics data to improve ...

Two new neural network designs promise to make AI models more adaptable and efficient, potentially changing how artificial ...

Researchers applied the mathematical theory of synchronization to clarify how recurrent neural networks (RNNs) generate predictions, revealing a certain map, based on the generalized synchronization, ...

Nvidia provided in-depth details on the various neural rendering techniques that will be unveiled with the RTX 50-series later this month. Here's everything you'll need to know about DLSS 4, Multi ...

This work models reinforcement-learning experiments using a recurrent neural network. It examines if the detailed credit assignment necessary for back-propagation through time can be replaced with ...

AI applications like ChatGPT are based on artificial neural networks that, in many respects, imitate the nerve cells in our ...

However, the neural networks—computer models inspired by the human brain—behind these ... Liboni et al, Image segmentation ...

This important work substantially advances our understanding of episodic memory by proposing a biologically plausible mechanism through which hippocampal barcode activity enables efficient memory ...

As neuro-AI research advances, it holds the potential to revolutionize our understanding of intelligence by seamlessly blending human and artificial cognition in ways never before imagined.

Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog ...