Ghislain St-Yves

The paper examines how geometric properties of concept manifolds in human visual cortex and DNNs affect classification accuracy, finding differences in the key geometric factors driving performance.

The paper introduces an imagery transformation framework to compare visual and mental imagery brain activity, finding that imagery patterns are transformed versions of visual patterns with fewer active dimensions.

This study trains neural networks to predict human visual cortex activity and finds they can learn non-hierarchical representations, challenging the idea that visual areas function in a strict hierarchical sequence.

The paper introduces a large-scale 7T fMRI dataset of natural scenes to advance cognitive and computational neuroscience research.

The paper uses voxel-to-voxel models to analyze unexplained variance in BOLD responses and finds it is structured within and across visual areas but not across brains.

The paper investigates brain activity during mental imagery and finds it mirrors visual processing through an echo effect in hierarchical encoding models.

This paper uses GANs conditioned on brain activity to reconstruct images from fMRI data, showing they can recover basic stimulus outlines despite noise and limited data.

The paper introduces the feature-weighted receptive field (fwRF) model, which balances expressiveness, interpretability, and scalability for encoding complex feature spaces, and demonstrates its application to visual neuroimaging data.