Katja Seeliger

This paper introduces gaze-aware encoding models for fMRI data that improve performance with fewer parameters by incorporating eye-tracking data from naturalistic viewing conditions.

The paper introduces Brain2Pix, a method to reconstruct naturalistic videos from brain activity using fully convolutional networks and retinotopic mappings, showing significant improvement over existing techniques.

The paper introduces a framework for system identification in neuroscience using cortical information flow, demonstrating its ability to estimate meaningful neural computations from fMRI data.

This paper uses generative adversarial networks to reconstruct natural images from brain activity, achieving 67.2% and 66.4% identification rates in behavioral tests.

The paper uses CNNs to model visual object recognition in MEG data, showing a feedforward sweep across the visual hierarchy and achieving high decoding accuracy.

The paper proposes using reinforcement learning neural networks to model cognitive processes by linking network dynamics to human behavior and neural data in a probabilistic categorization task.