Artificial neural networks (ANNs) are essential tools in machine learning that have drawn
increasing attention in neuroscience. Besides offering powerful techniques for data analysis …

A central challenge for cognitive science is to explain how abstract concepts are acquired
from limited experience. This has often been framed in terms of a dichotomy between …

Whether current or near-term AI systems could be conscious is a topic of scientific interest
and increasing public concern. This report argues for, and exemplifies, a rigorous and …

The performance of deep learning models in NLP and other fields of machine learning has
led to a rise in their popularity, and so the need for explanations of these models becomes …

Extracting roads from complex high-resolution remote sensing images to update road
networks has become a recent research focus. How to apply the contextual spatial …

Recent years have seen a proliferation of attention mechanisms and the rise of Transformers
in Natural Language Generation (NLG). Previously, state-of-the-art NLG architectures such …

Neural network models are potential tools for improving our understanding of complex brain
functions. To address this goal, these models need to be neurobiologically realistic …

Recent advances in deep learning have allowed artificial intelligence (AI) to reach near
human-level performance in many sensory, perceptual, linguistic, and cognitive tasks. There …

The central theme of this review is the dynamic interaction between information selection
and learning. We pose a fundamental question about this interaction: How do we learn what …

Motivation: Peptides have recently emerged as promising therapeutic agents against
various diseases. For both research and safety regulation purposes, it is of high importance …