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

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-the-art …

Attention is the important ability to flexibly control limited computational resources. It has been
studied in conjunction with many other topics in neuroscience and psychology including …

10.7554/eLife.38105.001 How does attentional modulation of neural activity enhance
performance? Here we use a deep convolutional neural network as a large-scale model of the …

Physical features of sensory stimuli are fixed, but sensory perception is context dependent.
The precise mechanisms that govern contextual modulation remain unknown. Here, we …

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 neural networks adapt and learn in diverse behavioral contexts. Artificial neural
networks (ANNs) have exploited biological properties to solve complex problems. However, …

Connecting neural activity to function is a common aim in neuroscience. How to define and
conceptualize function, however, can vary. Here I focus on grounding this goal in the specific …

Complex cognitive behaviors, such as context-switching and rule-following, are thought to
be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be …

Convolutional neural networks (CNNs) have proven effective for image processing tasks,
such as object recognition and classification. Recently, CNNs have been enhanced with …