Graph Neural Networks (GNNs) have emerged as a powerful class of models for learning from graph-structured data, capturing complex relational patterns across nodes and edges. However, their inherent ...

(Boston)—Recently, there has been convergence of thought by researchers in the fields of memory, perception, and neurology that the same neural circuitry that produces conscious memory of the past not ...

Researchers from the Icahn School of Medicine at Mount Sinai have uncovered the first direct evidence that deep brain ...

Researchers Dr. Yuval Hart and Oded Wertheimer from the Psychology department and the Edmond and Lily Safra Center for Brain Science (ELSC) at The Hebrew University of Jerusalem have developed a new ...

A new study uses deep linear networks to prove that language undergoes iterated learning to become structured and learnable.

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 ...

By elucidating the neural basis of individual differences in fear plasticity, this study highlights the central role of brain states in stress adaptation. "Our work provides new insights into arousal ...

The initial research papers date back to 2018, but for most, the notion of liquid networks (or liquid neural networks) is a new one. It was “Liquid Time-constant Networks,” published at the tail end ...

Social memory - the ability to recognize familiar individuals and distinguish them from strangers - is fundamental to social cognition. Deficits in social memory are hallmarks of multiple ...

Special Operations veterans suffering from traumatic brain injuries and posttraumatic stress disorder experienced notable ...