A central goal of neuroscience is to understand the representations formed by brain activity
patterns and their connection to behaviour. The classic approach is to investigate how …

The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history.
In more recent times, however, communication and collaboration between the two fields has …

Contextual embeddings, derived from deep language models (DLMs), provide a continuous
vectorial representation of language. This embedding space differs fundamentally from the …

Abstract Objects can be characterized according to a vast number of possible criteria (such
as animacy, shape, colour and function), but some dimensions are more useful than others …

To learn how cognition is implemented in the brain, we must build computational models
that can perform cognitive tasks, and test such models with brain and behavioral …

Convolutional neural networks (CNNs) are increasingly used to model human vision due to
their high object categorization capabilities and general correspondence with human brain …

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard
practice in analyzing fMRI data. Although decoding methods have been extensively applied …

To date, fMRI research has been concerned primarily with evincing generic principles of
brain function through averaging data from multiple subjects. Given rapid developments in …

An extensive body of empirical research has revealed remarkable regularities in the
acquisition, organization, deployment, and neural representation of human semantic …

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis
of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto-and …