Frederik Rogge is a PhD Candidate at the Centre for Integrative Neuroplasticity (CINPLA), Department of Biosciences, UiO. He is also part of the CompSci doctoral program. He will be defending his thesis for the degree of PhD, which was submitted for evaluation on the 13th of November of 2025.
From the CompSci team we wish you good luck and all the best in your future endeavors!
→ Read more about the disputation in the website of the Department of Biosciences
Programme
10:15 – Trial lecture: "A single neuron’s value – what does modern neuroscience tell us about coding principles in the cortex?"
13:15 – Disputation: "Normative and experimental analyses of learning-dependent representations in the hippocampal-parahippocampal system"
This event is open for everyone. No registration needed.
PhD candidate
Research group | CINPLA
Main supervisor | Marianne Fyhn
Co-supervisor | Anders Malthe-Sørenssen
Affiliation | Department of Biosciences, UiO
Contact | frederik.rogge@ibv.uio.no
Adjudication committee
Associate Professor Bernhard Englitz, Radboud University, The Netherlands
Project leader Pieter Michiel Goltstein, Max Planck Institute for Biological Intelligence, Germany
Principal Investigator Erna Dadvydova, Department of Biosciences, University of Oslo, Norway
Chair of defense
Professor Jonas Paulsen, Department of Biosciences, University of Oslo, Norway
Short summary
Normative and experimental analyses of learning-dependent representations in the hippocampal-parahippocampal system
The brain is thought to build a cognitive map of both physical and abstract spaces, helping us navigate the world, organize knowledge, and form new memories. In this thesis, I investigated key brain subregions to uncover the principles underlying the emergence of neural representations. I focused on the general properties of the neural code, how visual inputs are integrated, and how these representations reorganize through learning. To address these questions, I combined large-scale chronic two-photon calcium imaging in mice with normative modeling in artificial neural networks.
We show that neurons in the medial entorhinal cortex (MEC) develop task-relevant tuning during learning of a purely visual association task, pointing to a broader role beyond its classical link to spatial coding. In parallel, selective disruption of specific neuron types in the postrhinal cortex (POR) impaired learning of the same task, demonstrating their causal contribution. Furthermore, using artificial neural networks, we uncovered computational principles governing how spatial representations emerge and transform. Finally, we revealed dissociable contributions of POR and MEC to visually guided navigation, highlighting their distinct roles.
Together, these results shed new light on the emergence of neural representations in different brain regions and their transformation through learning.
