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The human brain is well equipped to turn arbitrary sensory signals into meaningful percepts. But how exactly is the interpretation of such incoming signals shaped by prior knowledge? What is a viable neural architecture within a cortical column for integrating bottom-up input with top-down predictions? And what constitutes the representational space of predictions along cortical hierarchies? In my research, I use laminar fMRI and deep learning models to answer these questions.
Before joining the Predictive Brain Lab, I completed my PhD at the intersection of Neuroscience, Linguistics and Machine Learning in the lab of Jonas Obleser. As a trained philologist and psychologist, I investigated how hierarchical event memory enables predictive processing in naturalistic speech comprehension, and soon started using artificial neural networks to better understand the underlying gating mechanisms in temporo-parietal cortex.