eLife

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images

Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...
IEEE

Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image Classification

Abstract: Graph Neural Networks (GNNs) have emerged as a promising solution for few-shot hyperspectral image (HSI) classification. However, existing GNN-based approaches face critical limitations in ...
eLife

State-dependent brain responsiveness, from local circuits to the whole brain

This correspondence between brain state and brain responsiveness (statedependent responses) is outlined at different scales from the cellular and circuit level, to the mesoscale and macroscale level.
IEEE

Survey on Graph Neural Networks

Graph Neural Networks (GNNs) have become a powerful tool in order to learn from graph-structured data. Their ability to capture complex relationships and dependencies within graph structures, allows ...