cv

Basics

Name Nicolai Haug
Label Doctoral research fellow
Url https://nicolossus.github.io/
Summary A Norwegian-born computational neuroscientist.

Work

  • 2023 - Present

    Oslo, Norway

    Doctoral research fellow
    Simula Research Laboratory
    PhD project focusing on the neural mechanisms of navigation and memory. The research involves developing models to replicate hippocampal neural dynamics, particularly through generative predictive coding in neurons such as place and grid cells during navigational tasks.
  • 2022 - 2023

    Ås, Norway

    Head engineer in scientific computing
    Norwegian University of Life Sciences (NMBU)
    Project position within the Human Brain Project. Primarily work on development of the cutting-edge brain simulation tool NEST (NEural Simulation Tool) in an international team. My focus is on implementing and optimizing methods for brain-scale constructions on peta- and exascale computers.
  • 2020 - 2021

    Oslo, Norway

    Research assistant
    Center for Computing in Science Education, University of Oslo
    Developed course material for an introductory course in Python programming and computational modeling in biosciences.
  • 2019 - 2021

    Oslo, Norway

    Teaching assistant
    University of Oslo
    Teaching assistant in courses on programming and computational modeling at both an introductory and intermediate bachelor level. In addition, I was a teaching assistant on machine learning and data analysis at master's level.

Education

  • 2019 - 2021

    Oslo, Norway

    M.Sc. in Computational Science
    University of Oslo
    • Computational neuroscience
    • Computational physics
    • Machine learning
    • Bayesian data analysis
    • High-performance computing
  • 2015 - 2018

    Oslo, Norway

    B.Sc. in Physics
    University of Oslo
    • Classical physics
    • Modern physics
    • Scientific computing
    • Biophysics

Publications

  • 2023.11.30
    Metamodelling of a two-population spiking neural network
    PLoS Computational Biology
    Biophysical simulations of neuronal networks exhibit complex dynamics, and the parameterization of the neurons and synapses in the network shape the model's behavior. These simulations are generally computationally intensive, especially when fitting models to experimental data. This study explores various metamodelling techniques, which are faster data-driven approximations, to model the population spiking activities and local field potential generated by a two-population recurrent network model. By inverting these metamodels, the study reliably identifies parameter combinations that produce specific simulation outputs.

Skills

Scientific programming
Physical simulations
Statistical data analysis
Machine learning
Deep learning
Data visualization
Scientific writing
Modern software engineering

Languages

Norwegian
Native speaker
English
Fluent
German
Elementary

Interests

Scientific computing
Biophysics
Computational neuroscience
Computational physics
NeuroAI
Deep generative models
Predictive coding
Representation learning
Neuronal network simulation
Bayesian data analysis
Reproducible research
Simulation-based inference

References

Professor John Doe
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