Auguste Schulz

The paper introduces a VAE-based method to model conditional distributions between neural and behavioral data, validated on fly and monkey datasets.

The paper investigates how mice distinguish self-motion from object motion using virtual reality and neural recordings from the superior colliculus, finding that different layers of the superior colliculus encode these signals differently.

The paper introduces AutoMIND, a method for inverse modeling of neural circuits, revealing dynamic-dependent invariances in parameter configurations across different neural dynamics.

This study shows inhibitory synaptic plasticity generates novelty responses in a network model by altering inhibition levels based on stimulus familiarity.