Matthias Nau

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

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



Perception and the cognitive map

The brain integrates our perceptual experiences into stable and unified representations of the world, a cognitive mapping process that manifests in our memories and guides our everyday behavior. Cognitive maps are thought to reside in the hippocampal formation, a mnemonic brain structure in the mediotemporal lobe (MTL) known to represent space. Many of the processing steps necessary to form cognitive maps however occur much earlier in the cortical hierarchy.


My long term goal is to understand how the brain derives the unified panorama we experience from sensory inputs, how we store it in our memories, and how these in turn affect the way we perceive and interact with the world.


In my PhD work, I combine functional magnetic resonance imaging (3T & 7T-fMRI), eye tracking and computational modeling to examine how human viewing behavior and brain activity along the visual streams relate to mnemonic and cognitive map-like processing in the MTL. Tightly-controlled psychophysical experiments and naturalistic virtual reality help me to address this question from multiple angles, complemented by machine learning to characterize and map the tuning of neural populations across the brain.





For a stable representation of the world to be formed, the brain must first tease apart whether incoming sensory signals were induced by changes in the environment or self-induced by our own movements. This processing step is fundamental to transforming visual information into a self-motion invariant code. In a recent study, my co-authors and I found a whole network of regions engaged in this process, including the earliest visual cortices in the brain (Nau et al. 2018, NeuroImage).

In another study, we examined a related but higher-order mechanism in the MTL, known to map space during navigation. We asked whether the same MTL-mechanism represents visual space as well, hence where we are looking rather than where we are. A critical neural component here are entorhinal grid cells: neurons that fire at different locations tessellating space with a hexagonal grid. We refined fMRI-proxy measures for grid-cell population activity during navigation, and adapted it to a viewing task, to show that the human entorhinal cortex indeed represents visual space with a grid-cell like code (Nau et al. 2018, Nature Neuroscience).

These results yield exciting implications, many of which we discussed in our review article (Nau et al. 2018, Trends Cogn. Sci). Most importantly, it shows that MTL mechanisms support domain-general computations in the brain, not limited to navigation, and that viewing behavior and visual paradigms enable a powerful read-out of these high-level cognitive computations. This review also provides an overview on visual and gaze related processing in the MTL, how it interacts with activity in sensory cortices and the computational challenges these codes might solve. We propose that viewing and navigation are guided by a common MTL mechanism that allows us to explore the world.

Now, I work on several follow-up ideas, for example by developing behavioral encoding models to study directional tuning in the human navigation network and how it changes during learning (Nau et al. 2019, BioRxiv) or by probing how visual memory signals emanating from the MTL impact perceptual processing in upstream brain areas. Please see the Publications & Preprints section for more and recent work. Thank you for visiting this website!


Biography

01/2020 - PhD Viva - January 24, 2020

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

Guest researcher at the Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

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

Guest researcher at the Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.

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


Publications & Preprints

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

Nau M. & Navarro Schröder T., Frey M., Doeller C.F. (2019). Behavior-dependent directional tuning in the human visual-navigation network.
BioRxiv

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

Frey M., Nau M. (2019).
Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net. arXiv

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.
NeuroImage

Navarro Schröder T., Towse B.W., Nau M., Burgess N., Barry C., Doeller C.F. (2018). Entorhinal cortex minimises uncertainty for optimal behaviour.
BioRxiv


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!

Lecture slides

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.