Chengxu Zhuang

This paper develops a computational model of mouse visual cortex, finding that self-supervised contrastive learning produces better matches to mouse cortex than supervised methods, suggesting a resource-efficient, general-purpose visual system.

This paper shows that unsupervised neural networks can model primate ventral visual stream responses as well as supervised methods, with brain-like representations from limited data.