Meenakshi Khosla

The paper tests whether fMRI food selectivity reflects food properties or other image features, finding that responses are driven by contextual and material features rather than food alone.

The paper uses hypothesis-neutral models to show that higher visual areas exhibit category selectivity without explicit training on those categories.

The paper shows that conclusions about neural network similarity to brain alignment depend heavily on the chosen similarity measure.

The paper identifies privileged representational axes in both biological and artificial neural networks, showing alignment between DCNNs and perceptual cortices and benefits of axis-sensitive coding.

This study uses data-driven voxel decomposition to identify neural selectivities in human visual cortex, revealing a strong food-selective component not previously identified.

This paper introduces BrainSurfCNN, a surface-based CNN that predicts task-based contrast maps from resting-state fMRI, achieving state-of-the-art accuracy and generalization.

This study shows deep neural networks can predict cortical responses to naturalistic stimuli across large brain areas beyond visual and auditory regions.