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 …

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 …

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 …

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

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

Inferior temporal (IT) cortex in human and nonhuman primates serves visual object
recognition. Computational object-vision models, although continually improving, do not yet …

Two ongoing movements in human cognitive neuroscience have researchers shifting focus
from group-level inferences to characterizing single subjects, and complementing tightly …

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 …