Kasper Vinken

The paper shows that macaque ventral stream neurons respond to images with a simple-to-complex selectivity progression over time, mirroring the visual hierarchy.

The paper maps functional correspondence between macaque and human cortex using natural scene responses, resolving ambiguities in cross-species alignment.

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 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.

This study compares object recognition strategies between rats and humans using a computational framework, finding significant differences in how each species processes visual stimuli.

The paper shows that visual representations become more stable along rodent visual processing hierarchies, with intrinsic dynamics contributing to this stability.

This paper compares rodent object vision with convolutional deep neural networks, finding that mid-level layers suffice and challenging assumptions about complex representations.

This study introduces a deep neural network model with a fatigue mechanism to capture adaptation dynamics in neurophysiology and perception, showing it accounts for neural and behavioral adaptation properties.