Yizhen Zhang

This study investigates how the brain encodes auditory word forms in speech processing, finding distinct neural resets at word boundaries and temporal scaffolding for variable word lengths.

The paper investigates neural activity in the human precentral gyrus during speech tasks, finding neurons that encode both listening and speaking phases, including mirror and bridge neurons.

This paper uses a variational autoencoder to disentangle sources of variation in resting state fMRI data and shows that latent representations can reliably identify subjects from a large group.

This study maps cortical representations of semantic relations using fMRI data, showing that semantic categories and relations are encoded in overlapping patterns across brain networks.

This paper uses a variational autoencoder to model and decode fMRI activity in the visual cortex, finding lower prediction accuracy compared to CNNs but successful decoding of latent variables.

The paper investigates how task-evoked functional connectivity differs from resting-state connectivity and finds that spontaneous networks, not task-evoked ones, mainly explain these differences.

This paper introduces a deep recurrent neural network to model process memory in visual processing and shows it better predicts cortical responses to natural movies than a standard CNN.

This study investigates how musicians process musical imagery compared to actual music perception, finding distinct network interactions involving Wernicke’s area.