cv
Basics
Name | Nicolai Haug |
Label | Doctoral research fellow |
Url | https://nicolossus.github.io/ |
Summary | A Norwegian-born computational neuroscientist. |
Work
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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.
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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.
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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.
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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
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2019 - 2021 Oslo, Norway
M.Sc. in Computational Science
University of Oslo
- Computational neuroscience
- Computational physics
- Machine learning
- Bayesian data analysis
- High-performance computing
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2015 - 2018 Oslo, Norway
B.Sc. in Physics
University of Oslo
- Classical physics
- Modern physics
- Scientific computing
- Biophysics
Publications
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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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