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 …

Inferences made about objects via vision, such as rapid and accurate categorization, are
core to primate cognition despite the algorithmic challenge posed by varying viewpoints and …

The notion of 'interpretability'of artificial neural networks (ANNs) is of growing importance in
neuroscience and artificial intelligence (AI). But interpretability means different things to …

Little is known about how the brain computes the perceived aesthetic value of complex
stimuli such as visual art. Here, we used computational methods in combination with …

Convolutional neural networks show promise as models of biological vision. However, their
decision behaviour, including the facts that they are deterministic and use equal numbers of …

Exploiting data invariances is crucial for efficient learning in both artificial and biological
neural circuits. Understanding how neural networks can discover appropriate …

Deep convolutional neural networks (DCNNs) and the ventral visual pathway share vast
architectural and functional similarities in visual challenges such as object recognition …

A hallmark of human object perception is sensitivity to the holistic configuration of the local
shape features of an object. Deep convolutional neural networks (DCNNs) are currently the …

The visual system extracts meaning from retinal inputs and computes representations rich
enough to guide actions, inform world models, and allow verbal communication. Classic …

Three large-scale networks are considered essential to cognitive flexibility: the ventral and
dorsal attention (VANet and DANet) and salience (SNet) networks. The ventrolateral …