Akshay V. Jagadeesh

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 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.

The paper shows that face cells in macaques respond to illusory face images based on isolated object parts, not holistic face-like configurations or human faceness ratings.

The paper shows that mice can discriminate textures and that the secondary visual area (LM) encodes texture statistics more efficiently than the primary area (V1), linking stimulus complexity to neural representation and perception.

The paper investigates whether category-selective visual cortex represents objects or texture-like features, finding that models based on these regions fail to distinguish natural objects from scrambled textures.

The paper investigates how attention enhances neural representations of attended stimuli across the brain and finds that residual correlations with ventral temporal cortex are strengthened.