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 …

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

Biological and artificial information processing systems form representations that they can
use to categorize, reason, plan, navigate, and make decisions. How can we measure the …

Deep neural network models of sensory systems are often proposed to learn
representational transformations with invariances like those in the brain. To reveal these …

Modular and distributed coding theories of category selectivity along the human ventral
visual stream have long existed in tension. Here, we present a reconciling framework …

To solve the spatial problems of mapping, localization and navigation, the mammalian
lineage has developed striking spatial representations. One important spatial representation …

Recognition of objects from sensory stimuli is essential for survival. To that end, sensory
networks in the brain must form object representations invariant to stimulus changes, such …

The human ventral visual stream has a highly systematic organization of object information,
but the causal pressures driving these topographic motifs are highly debated. Here, we use …

Reconstructing perceived natural images from fMRI signals is one of the most engaging
topics of neural decoding research. Prior studies had success in reconstructing either the …

Sketching is a powerful tool for creating abstract images that are sparse but meaningful.
Sketch understanding poses fundamental challenges for general-purpose vision algorithms …