Martin Kampman, recipient of the Byers Award for outstanding research by faculty members in the middle of their careers, ...

According to Dr. Karolina Armonaitė, a neuroscientist from Kaunas University of Technology in Lithuania, a more precise ...

The potential for these kinds of machines to reshape computer processing, increase energy efficiency, and revolutionize medical testing has scientists excited. But when do we consider these cells to ...

Existing three-dimensional (3D) neuronal culture technology has limitations in brain research due to the difficulty of ...

Brain-inspired chips can slash AI energy use by as much as 100-fold, but the road to mainstream deployment is far from ...

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light ...

In this paper, we study the generalization capabilities of geometric graph neural networks (GNNs). We consider GNNs over a geometric graph constructed from a finite set of randomly sampled points over ...

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.

The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional ...

Graph drawing techniques have been developed in the last few years with the purpose of producing esthetically pleasing node-link layouts. Recently, the employment of differentiable loss functions has ...