Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …

Brain–computer interfaces—which allow direct communication between the brain and
external computers—have potential applications in neuroscience, medicine and virtual …

Reconstructing visual experiences from human brain activity offers a unique way to
understand how the brain represents the world, and to interpret the connection between …

In this paper, we explore a novel knowledge-transfer task, termed as Deep Model
Reassembly (DeRy), for general-purpose model reuse. Given a collection of heterogeneous …

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust
understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in …

Abstract reasoning is a key ability for an intelligent system. Large language models (LMs)
achieve above-chance performance on abstract reasoning tasks, but exhibit many …

Deep learning algorithms trained to predict masked words from large amount of text have
recently been shown to generate activations similar to those of the human brain. However …

Neuroscientists have long characterized the properties and functions of the nervous system,
and are increasingly succeeding in answering how brains perform the tasks they do. But the …

During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses
are embedded within multilayered networks, making it difficult to determine the effect of an …

A few years ago, the first CNN surpassed human performance on ImageNet. However, it
soon became clear that machines lack robustness on more challenging test cases, a major …