Ke Zhou

This paper trains recurrent neural networks to mimic human spatial attention dynamics across different cue types, revealing shared low-dimensional manifolds and sparse coding mechanisms.

This study uses machine learning to analyze neural signatures of feature and spatial attention, finding shared and distinct brain networks involved in each process.

This study investigates how precision of contextual predictions modulates social attention using a hierarchical Bayesian model and fMRI, showing that attention allocation is adjusted based on prediction error precision.

The paper shows that deep convolutional neural networks trained for facial identity recognition develop expression-selective units with human-like perception characteristics when exposed to facial expressions.

The paper constructs a hybrid SOM model to explain the topological organization of object selectivities in the ventral temporal cortex using a wiring cost minimization principle.

This paper identifies a whole-brain functional connectivity network linked to non-verbal number acuity and arithmetic skills using connectome-based predictive modeling on resting-state fMRI data.

This study uses qMRI and fMRI to find that early second language acquisition enhances microstructural cortical development in bilinguals.