Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We
propose that to accelerate progress in AI, we must invest in fundamental research in …

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake,
behaving human brain. By tracking whole-brain signals across a diverse range of cognitive …

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a
driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted …

Large language models (LLMs) have been transformative. They are pretrained foundational
models that are self-supervised and can be adapted with fine-tuning to a wide range of …

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 …

Most existing forecasting systems are memory-based methods, which attempt to mimic
human forecasting ability by employing various memory mechanisms and have progressed …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

Mounting evidence has highlighted the implementation of big data handling and
management in the healthcare industry to improve the clinical services. Various private and …

Replay in the brain has been viewed as rehearsal or, more recently, as sampling from a
transition model. Here, we propose a new hypothesis: that replay is able to implement a form …