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Found 5 papers, 3 papers with code
Brain2Music: Reconstructing Music from Human Brain Activity
The process of reconstructing experiences from human brain activity offers a unique lens into how the brain interprets and represents the world.
Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputs
The reconstruction of visual experience from human brain activity is an area that has particularly benefited: the use of deep learning models trained on large amounts of natural images has greatly improved its quality, and approaches that combine the diverse information contained in visual experiences have proliferated rapidly in recent years.
High-Resolution Image Reconstruction With Latent Diffusion Models From Human Brain Activity
Here, we propose a new method based on a diffusion model (DM) to reconstruct images from human brain activity obtained via functional magnetic resonance imaging (fMRI).
Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments
In this paper, we develop a new method for cross-areal interaction analysis that uses the rich task or stimulus parameters to reveal how and what types of information are shared by different neural populations.
Demixed shared component analysis of neural population data from multiple brain areas
This yields interpretable components that express which variables are shared between different brain regions and when this information is shared across time.
Neurons and Cognition
