Matthias Nau

Cognitive neuroscientist at the Kavli Institute for Systems Neuroscience, Trondheim, Norway

Keywords: space & memory - vision & eye movements - fMRI - machine learning

Constructing space from moving gaze

I am a postdoctoral fellow in the lab of Christian Doeller at the Kavli Institute for Systems Neuroscience in Trondheim (Norway) and the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig (Germany). I study the links between human vision, viewing behavior and memory.

I am especially interested in how we perceive and memorize space, and how our knowledge about the world shapes the way we see and interact with it. I address these questions using neuroimaging and eye tracking combined with psychophysical and virtual reality experiments. My work is complemented by machine learning to characterize brain activity in sensory and memory regions and how they interact. I also enjoy methods development to push the boundaries of how we study these processes altogether. Read more about my research here.

I look forward to soon be starting as a postdoctoral fellow in the lab of Christopher Baker at the National Institute of Mental Health in Bethesda (USA).


Since 01/2020 - Postdoctoral researcher
Kavli Institute for Systems Neuroscience, Centre for Neural Computation, NTNU, Trondheim, Norway.
PI: Christian F. Doeller

09/2016 - 01/2020 - PhD Candidate
Kavli Institute for Systems Neuroscience, Centre for Neural Computation, NTNU, Trondheim, Norway.
PI: Christian F. Doeller

04/2016 - 08/2016 - Research Assistant
Donders Institute for Brain, Cognition & Behaviour, Nijmegen, The Netherlands.
PI: Christian F. Doeller

11/2014 - 03/2016 - Research Assistant
Werner Reichardt Centre for Integrative Neuroscience, Tübingen, Germany.
PI: Andreas Bartels

11/2014 - Master of Science
Neurobiology, University of Tübingen, Tübingen, Germany.

Since 04/2019 - Guest researcher
Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

11/2014 - 03/2016 - Guest researcher
Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.

Publications & Preprints

Nau M., Navarro Schröder T., Frey M., Doeller C.F. (2020). Behavior-dependent directional tuning in the human visual-navigation network.
Nature Communications

Frey M., Nau M. (2020). Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net. Springer: Lecture Notes in Computer Science

Nau M. (2020). Perception & the cognitive map: deriving a stable world from visual inputs.
Dissertation - NTNU Open

Bellmund J.L.S., Ruiter T.A., Nau M., Doeller C.F. (2020).
Deforming the metric of cognitive maps distorts memory.
Nature Human Behavior

Navarro Schröder T., Towse B.W., Nau M., Burgess N., Barry C., Doeller C.F. (2020). Environmental anchoring of grid-like representations minimizes spatial uncertainty during navigation.

Frey M., Tanni S., Perrodin C., O'Leary A., Nau M. Kelly J., Banino, A., Doeller C.F., Barry, C. (2019).
DeepInsight: a general framework for interpreting wide-band neural activity. BioRxiv

Nau M. (2019).
Functional imaging of the medial temporal lobe -
A neuroscientist's guide to fMRI pulse sequence optimization. Open Science Framework

Nau M. & Julian J.B., Doeller C.F. (2018).
How the brain's navigation system shapes our visual experience.* Trends in Cognitive Sciences
* Featured cover article

Nau M., Navarro Schröder T., Bellmund J.L.S., Doeller C.F. (2018). Hexadirectional coding of visual space in human entorhinal cortex.* Nature Neuroscience
* News & Views article by Killian N.J. & Buffalo E.A.

Nau M., Schindler A., Bartels A. (2018).
Real-motion signals in human early visual cortex.

Functional imaging of the human medial temporal lobe.
A neuroscientist's guide to fMRI pulse sequence optimization

The medial temporal lobe (MTL) is difficult to image with fMRI due to magnetic field inhomogeneities and low signal-to-noise ratios. Here, I compiled some information about fMRI pulse sequences and how they affect your data, along with a few tips on how to get a good signal in the MTL. Find it here on Open Science Framework!

Behavioral encoding modeling

This code creates, fits & tests an encoding model of virtual head direction using simulated fMRI voxel time courses.

It can be easily adapted for other behavioral domains & imaging techniques (MEG, 2p-imaging...) to study the neural underpinnings of behavior across species.


HERE you find my fMRI crash course slides, covering the MR-imaging basics, data preprocessing, the general linear model and simple analyses.

HERE you find my lecture slides on visual field mapping.