Holger Frimmel

Encoding neural representations of time-continuous stimulus-response transformations in the human brain with advanced deep neural networks

This study uses advanced deep neural networks to predict brain activity during time-continuous stimulus-response tasks, showing that more advanced DNNs provide better predictive features and align more closely with brain organization.

Advances in deep reinforcement learning enable better predictions of human behavior in time-continuous tasks

This study uses advanced deep reinforcement learning models to predict human motor responses in time-continuous tasks, finding that more complex models achieve higher prediction accuracy.