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 …

Highlights•Trade-off between sampling individual variation versus experimental
variation.•Different studies have allocated resources differently.•We argue that wide …

Reconstructing visual experiences from human brain activity offers a unique way to
understand how the brain represents the world, and to interpret the connection between …

Originally inspired by neurobiology, deep neural network models have become a powerful
tool of machine learning and artificial intelligence. They can approximate functions and …

Reconstructing human vision from brain activities has been an appealing task that helps to
understand our cognitive process. Even though recent research has seen great success in …

The mental contents of perception and imagery are thought to be encoded in hierarchical
representations in the brain, but previous attempts to visualize perceptual contents have …

Neuroscientific studies exploring real-world dynamic perception often overlook the influence
of continuous changes in narrative content. In our research, we utilize machine learning …

Primates, including humans, can typically recognize objects in visual images at a glance
despite naturally occurring identity-preserving image transformations (eg, changes in …

Deep neural networks (DNNs) have recently been applied successfully to brain decoding
and image reconstruction from functional magnetic resonance imaging (fMRI) activity …

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 …