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 …

The human brain achieves visual object recognition through multiple stages of linear and
nonlinear transformations operating at a millisecond scale. To predict and explain these …

The sciences of biological and artificial intelligence are ever more intertwined. Neural
computational principles inspire new intelligent machines, which are in turn used to advance …

Reconstructions of visual perception from brain activity have improved tremendously, but the
practical utility of such methods has been limited. This is because such models are trained …

Decades of experimental research based on simple, abstract stimuli has revealed the
coding principles of the ventral visual processing hierarchy, from the presence of edge …

Investigation of the visual system has mainly relied on a-priori hypotheses to restrict
experimental stimuli or models used to analyze experimental data. Hypotheses are an …

Cognitive neuroscience aims to develop computational models that can accurately predict
and explain neural responses to sensory inputs in the cortex. Recent studies attempt to …

The release of large datasets and developments in AI have led to dramatic improvements in
decoding methods that reconstruct seen images from human brain activity. We evaluate the …

Humans can extract high‐level spatial features from visual signals, but spatial
representations in the brain are complex and remain unclear. The unsupervised capsule …

Two recent developments have accelerated progress in image reconstruction from human
brain activity: large datasets that offer samples of brain activity in response to many …