In the era of Artificial Intelligence Generated Content (AIGC), conditional multimodal
synthesis technologies (eg, text-to-image) are dynamically reshaping the natural content …

Patterns of brain activity contain meaningful information about the perceived world. Recent
decades have welcomed a new era in neural analyses, with computational techniques from …

Current perceptual similarity metrics operate at the level of pixels and patches. These
metrics compare images in terms of their low-level colors and textures, but fail to capture mid …

High-performing neural networks for vision have dramatically advanced our ability to
account for neural data in biological systems. Recently, further improvement in performance …

Studying the neural basis of human dynamic visual perception requires extensive
experimental data to evaluate the large swathes of functionally diverse brain neural …

Object vision is commonly thought to involve a hierarchy of brain regions processing
increasingly complex image features, with high-level visual cortex supporting object …

Deep neural networks have reached human-level performance on many computer vision
tasks. However, the objectives used to train these networks enforce only that similar images …

The human ventral visual stream has a highly systematic organization of object information,
but the causal pressures driving these topographic motifs are highly debated. Here, we use …

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 …

In the past five years, the use of generative and foundational AI systems has greatly
improved the decoding of brain activity. Visual perception, in particular, can now be decoded …