Peter Ford Dominey

This study uses EEG and a humanoid robot to predict human and robot movement during motor learning, showing neural correlates of learning and decoding movement parameters.

The paper uses structured reservoir computing to model how white matter connectivity influences narrative processing speed in different cortical areas.

The paper introduces a neurocomputational model of narrative event segmentation that replicates human-like hierarchical segmentation patterns observed in fMRI studies.

The paper investigates neural dynamics during semantic processing of visual images and sentences, finding common patterns in EEG responses to sequential coherence.

The paper models how the prefrontal cortex uses hippocampal place-cell replay to generate efficient navigation sequences through reward-modulated reinforcement learning.