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N. (Niklas) Müller

PhD candidate
Faculty of Social and Behavioural Sciences
Programme group Brain and Cognition
Photographer: Onbekend

Visiting address
  • Nieuwe Achtergracht 129
Postal address
  • Postbus 15915
    1001 NK Amsterdam
Contact details
  • Profile

    I have a strong background in computational modelling. Using techniques from computer science and artificial intelligence I study perceptual processes in the human brain. I combine data from neuroimaging (EEG, neurophysiology, etc.) with behavioural measures (eye-tracking, response times, etc.) to build computational models of the dynamics neural dynamics of perceptual processes in the human (primate) brain.
    Specifically, during my PhD I will investigate the role of pre-cortical processes on visual perception. Introducing computations from pre-cortical structures into the training pipeline of deep convolutional neural network will probe the necessity of structures like the retina or the LGN for visual tasks like object recognition.

    Expertise and research fields

    • Computational Neuroscience
    • Visual Perception
    • Data Analysis
    • Deep Neural Networks

    Relevant links

    I am part of both, the Brain and Cognition Group at the Psychology institute as well as the VIS Lab at the Computer Science Institute.

  • Research

    Research methods

    • EEG
    • fMRI
    • Eye-Tracking
    • Computational Modelling / Deep Neural Networks

    Current research projects


    • Elucidating the core computational principles underlying visual recognition of natural scenes | Using EEG data we investigate the role of pre-cortical processing using deep neural networks
    • Eye-tracking during Inspection of Natural Image Scenes while Performing Cognitive Tasks | Eye movements recordings shed light on the information content of natural scenes. We investigate their merit in training a DCNN to perform visual tasks 
    • High-Quality Natural Scenes can Replace Careful Data Augmentation for Making DCNNs more Human-Like | Using high-quality natural images with biologically inspired processing might help DCNNs to be more human-like

    Current collaborations

    UvA Data Science Centre

  • Teaching & PhD Supervision
    • BA: Building Brains with AI
    • MA: Cognitive AI and Neural Networks
  • Publications


    • Müller, N., Groen, I. I. A., & Scholte, H. S. (2023). Pre-Training on High-Quality Natural Image Data Reduces DCNN Texture Bias. In 2023 Conference on Cognitive Computational Neuroscience (pp. 371-374). CCN.
    This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list.
  • Ancillary activities
    No ancillary activities