Existing models propose that primate visual cortex is divided into two functionally distinct
pathways. The ventral pathway computes the identity of an object; the dorsal pathway …

A rich behavioral literature has shown that human object recognition is supported by a
representation of shape that is tolerant to variations in an object's appearance …

Brains contain networks of interconnected neurons and so knowing the network architecture
is essential for understanding brain function. We therefore mapped the synaptic-resolution …

The feed-forward architectures of recently proposed deep super-resolution networks learn
representations of low-resolution inputs, and the non-linear mapping from those to high …

The human visual system is an intricate network of brain regions that enables us to
recognize the world around us. Despite its abundant lateral and feedback connections …

Delineation of functional topography is critical to the evolving understanding of the
cerebellum's role in a wide range of nervous system functions. We used data from the …

Hierarchical feature extractors such as Convolutional Networks (ConvNets) have achieved
impressive performance on a variety of classification tasks using purely feedforward …

Deep neural networks are powerful tools in learning sophisticated but fixed mapping rules
between inputs and outputs, thereby limiting their application in more complex and dynamic …

Functional correspondences between deep convolutional neural networks (DCNNs) and the
mammalian visual system support a hierarchical account in which successive stages of …

Humans are remarkably adept at perceiving and understanding complex real-world scenes.
Uncovering the neural basis of this ability is an important goal of vision science …