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 …

Understanding the functional organization of higher visual cortex is a central focus in
neuroscience. Past studies have primarily mapped the visual and semantic selectivity of …

It has been reported that threatening and non-threatening visual stimuli can be distinguished
based on the multi-voxel patterns of haemodynamic activity in the human ventral visual …

A challenging goal of neural coding is to characterize the neural representations underlying
visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was …

What comparing deep neural networks can teach us about human vision | Nature Machine
Intelligence Skip to main content Thank you for visiting nature.com. You are using a browser …

Remarkably, human brains have the ability to accurately perceive and process the real-
world size of objects, despite vast differences in distance and perspective. While previous …

Integrating information from multiple modalities is arguably one of the essential prerequisites
for grounding artificial intelligence systems with an understanding of the real world. Recent …

Humans effortlessly make quick and accurate perceptual decisions about the nature of their
immediate visual environment, such as the category of the scene they face. Previous …

Recurrent neural networks (RNNs) have yielded promising results for both recognizing
objects in challenging conditions and modeling aspects of primate vision. However, the …

We report an investigation of the neural processes involved in the processing of faces and
objects of brain-lesioned patient PS, a well-documented case of pure acquired …