Mario Michiels

This paper introduces two novel behavioral tasks to study human habits, showing that intra-block reversals and real-world avoidance scenarios better capture habitual behavior than traditional methods.

This study investigates the neural basis of habit formation by analyzing brain activity during goal-directed response switching, identifying key brain regions and their interactions.

The paper introduces FGES-Merge, a method for learning large gene regulatory networks from brain data, achieving high accuracy and speed while providing interpretable results.

This paper compares neural circuits for laboratory and everyday-life habits using fMRI meta-analysis, finding common striatal substrates in posterior and anterior putamen.

BayeSuites provides a scalable web framework for Bayesian networks in neuroscience, demonstrating its capabilities with genomic data examples.

The paper predicts electrophysiological features of single neurons using their morphology with a machine learning model, integrating with biophysical modeling tools.