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 …

Universality is a key hypothesis in mechanistic interpretability–that different models learn
similar features and circuits when trained on similar tasks. In this work, we study the …

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

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

Deep neural networks (DNNs) have had extraordinary successes in classifying
photographic images of objects and are often described as the best models of biological …

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 …

Recent scholarship has brought attention to the fact that there often exist multiple models for
a given prediction task with equal accuracy that differ in their individual-level predictions or …

Deep neural networks provide the current best models of visual information processing in
the primate brain. Drawing on work from computer vision, the most commonly used networks …