Colin Conwell

Monkey See, Model Knew: Large Language Models Accurately Predict Visual Brain Responses in Humans AND Non-Human Primates

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.

Universality of representation in biological and artificial neural networks

This study shows that high-performing ANNs and brains converge on similar representations, and identifies stimuli that modulate model-to-brain alignment.

What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines?

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.

Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex

The paper benchmarks deep neural networks on mouse visual cortex data to assess their ability to model neural activity and resolve discrepancies in previous findings.