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 …

Convolutional neural networks (CNNs) were inspired by early findings in the study of
biological vision. They have since become successful tools in computer vision and state-of …

Reconstructing visual experiences from human brain activity offers a unique way to
understand how the brain represents the world, and to interpret the connection between …

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 …

Deep learning algorithms trained to predict masked words from large amount of text have
recently been shown to generate activations similar to those of the human brain. However …

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

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 …

Perception and perceptual decision-making are strongly facilitated by prior knowledge about
the probabilistic structure of the world. While the computational benefits of using prior …

Neuroscience research is undergoing a minor revolution. Recent advances in machine
learning and artificial intelligence research have opened up new ways of thinking about …

Abstract Systems neuroscience seeks explanations for how the brain implements a wide
variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to …