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 …

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

Neural decoding can be conceptualized as the problem of mapping brain responses back to
sensory stimuli via a feature space. We introduce (i) a novel experimental paradigm that …

Generative adversarial networks (GANs) enable computers to learn complex data
distributions and sample from these distributions. When applied to the visual domain, this …

Can we obtain insights about the brain using AI models? How is the information in deep
learning models related to brain recordings? Can we improve AI models with the help of …

The recent spectacular success of machine learning in the sciences points to the emergence
of a new artificial intelligence trading zone. The epistemological implications of this trading …

The field of brain-to-stimuli reconstruction has seen significant progress in the last few years,
but techniques continue to be subject-specific and are usually tested on a single dataset. In …

Reconstructing natural images from fMRI recordings is a challenging task of great
importance in neuroscience. The current architectures are bottlenecked because they fail to …

A challenging goal of neural coding is to characterize the neural representations underlying
visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was …

Reconstruction of perceived faces from brain signals is a hot topic in brain decoding and an
important application in the field of brain-computer interfaces. Existing methods do not fully …