Predictive coding offers a potentially unifying account of cortical function--postulating that the
core function of the brain is to minimize prediction errors with respect to a generative model …

Highlights•Neural network models of vision are dominated by feedforward architectures.•
Biological vision, by contrast, exhibits abundant recurrent processing.•The computational …

Distinct scientific theories can make similar predictions. To adjudicate between theories, we
must design experiments for which the theories make distinct predictions. Here we consider …

Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to
be able to predict and decode cortical responses to natural images or videos. Here, we …

Recent work has shown that convolutional neural networks (CNNs) trained on image
recognition tasks can serve as valuable models for predicting neural responses in primate …

Sound source localization in visual scenes aims to localize objects emitting the sound in a
given image. Recent works showing impressive localization performance typically rely on …

Reconstructing natural images and decoding their semantic category from fMRI brain
recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI …

This work presents a novel method of exploring human brain-visual representations, with a
view towards replicating these processes in machines. The core idea is to learn plausible …

Visual perception involves the rapid formation of a coarse image representation at the onset
of visual processing, which is iteratively refined by late computational processes. These …

Deep neural networks have long been criticized for lacking the ability to perform analogical
visual reasoning. Here, we propose a neural network model to solve Raven's Progressive …