Search Results for author:
Found 4 papers, 3 papers with code
Modulating human brain responses via optimal natural image selection and synthetic image generation
Furthermore, aTLfaces and FBA1 had higher activation in response to maximal synthetic images compared to maximal natural images.
Decoding natural image stimuli from fMRI data with a surface-based convolutional network
In this work, we propose a novel approach for this task, which we call Cortex2Image, to decode visual stimuli with high semantic fidelity and rich fine-grained detail.
Personalized visual encoding model construction with small data
Our approach shows the potential to use previously collected, deeply sampled data to efficiently create accurate, personalized encoding models and, subsequently, personalized optimal synthetic images for new individuals scanned under different experimental conditions.
NeuroGen: activation optimized image synthesis for discovery neuroscience
NeuroGen combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation.
