Jacob S. Prince

The paper shows that leading neural network models of primate visual cortex differ in their ability to control neural firing using parametric stimulus perturbations, revealing discrepancies in how models and neurons parameterize visual space.

This paper introduces GSN, a method to disentangle signal and noise in neural responses, showing it improves dimensionality estimates in fMRI data.

This paper shows that large language models can predict visual brain responses in humans and non-human primates, suggesting that language-aligned models capture general visual patterns rather than language-specific features.

This paper introduces contrastive coding as a framework explaining category-selective regions in the human brain using neural network analyses.

The paper shows that face cells in macaques respond to non-faces in a way that predicts face selectivity, challenging the idea that face cells are exclusively face-specific.

The paper identifies two food-selective regions in the ventral visual cortex using fMRI and natural images, confirming food selectivity across multiple datasets and analysis methods.

This study identifies neural mechanisms of human conscious visual perception using report-independent methods and shows involvement of large-scale subcortical and cortical networks.

GLMsingle improves single-trial fMRI response estimates using custom HRFs, noise regressors, and ridge regression, enhancing reliability and decoding performance across datasets.

Across 224 vision models scored against fMRI responses, architecture and training objective barely mattered; the images a model was trained on mattered most, and linear re-weighting blurs differences.

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