Abstract Feedforward Convolutional Neural Networks (ffCNNs) have become state-of-the-art
models both in computer vision and neuroscience. However, human-like performance of …

Classically, visual processing is described as a cascade of local feedforward computations.
Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such …

In the human visual system, visible objects are recognized by features, which can be
classified into local features that are based on their simple components (ie, line segment …

Here we examine the plausibility of deep convolutional neural networks (CNNs) as a
theoretical framework for understanding biological vision in the context of image …

Crowding is a visual effect suffered by humans, in which an object that can be recognized in
isolation can no longer be recognized when other objects, called flankers, are placed close …

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 …

Highlights•Boundary or texture-based strategies alone cannot support visual object
recognition.•Neurons in area V4 jointly encode a shape boundary and the associated …

When deep convolutional neural networks (CNNs) are trained “end-to-end” on raw data,
some of the feature detectors they develop in their early layers resemble the representations …

Abstract Deep Convolutional Neural Networks (CNNs) are gaining traction as the
benchmark model of visual object recognition, with performance now surpassing humans …

Convolutional neural networks (CNNs) have achieved very high object categorization
performance recently. It has increasingly become a common practice in human fMRI …