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 …

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 …

How do neural populations code for multiple, potentially conflicting tasks? Here we used
computational simulations involving neural networks to define" lazy" and" rich" coding …

Advances in artificial intelligence have inspired a paradigm shift in human neuroscience,
yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide …

Originally inspired by neurobiology, deep neural network models have become a powerful
tool of machine learning and artificial intelligence. They can approximate functions and …

Our capacity to jointly represent information about the world underpins our social
experience. By leveraging one individual's brain activity to model another's, we can measure …

The human visual system is an intricate network of brain regions that enables us to
recognize the world around us. Despite its abundant lateral and feedback connections …

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

Humans have long been fascinated by the opportunities afforded through augmentation.
This vision not only depends on technological innovations but also critically relies on our …