Jasmine L. Hect

The paper uses deep neural networks to model neural responses in human auditory cortex and finds gradients of processing complexity and integration timescales across brain regions.

This study uses intracranial recordings to map speech processing latencies and task effects across the human forebrain, revealing hierarchical and parallel information flow and task-related neural responses.

The paper uses human intracerebral recordings to show that voice is encoded categorically in STG and STS, not by acoustic features, and this selectivity increases along the auditory hierarchy.

The paper develops a CNN for automated fetal brain masking in rs-fMRI data, achieving good performance across different scanners and populations.