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Found 8 papers, 1 papers with code
Applicability of scaling laws to vision encoding models
(2) How does the prediction accuracy across the visual cortex vary with the parameter size of the vision models?
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).
Voluntary control of semantic neural representations by imagery with conflicting visual stimulation
The successful control of the feedback images demonstrated that the semantic vector inferred from electrocorticograms became closer to the vector of the imagined category, even while watching images from different categories.
Brain-mediated Transfer Learning of Convolutional Neural Networks
Thus, our BTL provides a framework to improve the generalization ability of machine-learning feature representations and enable machine learning to estimate human-like cognition and behavior, including individual variability.
Describing Semantic Representations of Brain Activity Evoked by Visual Stimuli
The results demonstrate that the proposed model can decode brain activity and generate descriptions using natural language sentences.
