Researchers studying the correspondences between Deep Neural Networks (DNNs) and
humans often give little consideration to severe testing when drawing conclusions from …

It has been proposed that, when processing a stream of events, humans divide their
experiences in terms of inferred latent causes (LCs) to support context-dependent learning …

Experience replay (ER) is a widely-adopted neuroscience-inspired method to perform
lifelong learning. Nonetheless, existing ER-based approaches consider very coarse memory …

Recently acquired memories are reactivated in the hippocampus during sleep, an initial step
for their consolidation,–. This process is concomitant with the hippocampal reactivation of …

Large Language Models (LLMs) have demonstrated remarkable capabilities in performing
tasks across various domains without needing explicit retraining. This capability, known as …

Knowledge and data fusion is a vital research hotspot in current artificial intelligence. The
fusion of data-driven and knowledge-driven would be able to organically combine implicit …

Continual learning (CL) refers to an agent's capability to learn from a continuous stream of
data and transfer knowledge without forgetting old information. One crucial aspect of CL is …

Arrhythmias can pose a significant threat to cardiac health, potentially leading to serious
consequences such as stroke, heart failure, cardiac arrest, shock, and sudden death. In …

Continual learning in deep neural networks often suffers from catastrophic forgetting, where
representations for previous tasks are overwritten during subsequent training. We propose a …

Continual learning (CL) refers to an agent's capability to learn from a continuous stream of
data and transfer knowledge without forgetting old information. One crucial aspect of CL is …