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Nicolai Haug

Student - Faculty of Mathematics and Natural Sciences

Email nicoha@ifi.uio.no

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Visiting address Sem Sælands vei 24 Fysikkbygningen 0371 Oslo

Postal address Postboks 1032 Blindern 0315 Oslo

Other affiliations Department of physics

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Academic interests

I am a PhD candidate in computational neuroscience at Simula Research Laboratory and the University of Oslo (external). My research focuses on the neural mechanisms of navigation and memory, specifically by developing models that replicate hippocampal neural dynamics.

My doctoral studies are funded by SUURPh, a collaborative program between Simula, the University of Oslo, and the University of California, San Diego (UCSD). My research involves close collaboration with several research groups: the bioAI group (Simula/UiO), the Centre for Integrative Neuroplasticity (CINPLA) at UiO, and the Leutgeb and Integrated Systems Neuroengineering (ISN) laboratories at UCSD.

Skaar, Jan-Eirik Welle; Haug, Nicolai & Plesser, Hans Ekkehard (2025). A simplified model of NMDA-receptor-mediated dynamics in leaky integrate-and-fire neurons. Journal of Computational Neuroscience. ISSN 0929-5313. 53(3), p. 475–487. doi: 10.1007/s10827-025-00911-8. Full text in Research Archive Show summary
A model for NMDA-receptor-mediated synaptic currents in leaky integrate-and-fire neurons, first proposed by Wang (J Neurosci, 1999), has been widely studied in computational neuroscience. The model features a fast rise in the NMDA conductance upon spikes in a pre-synaptic neuron followed by a slow decay. In a general implementation of this model which allows for arbitrary network connectivity and delay distributions, the summed NMDA current from all neurons in a pre-synaptic population cannot be simulated in aggregated form. Simulating each synapse separately is prohibitively slow for all but small networks, which has largely limited the use of the model to fully connected networks with identical delays, for which an efficient simulation scheme exists. We propose an approximation to the original model that can be efficiently simulated for arbitrary network connectivity and delay distributions. Our results demonstrate that the approximation incurs minimal error and preserves network dynamics. We further use the approximate model to explore binary decision making in sparsely coupled networks.
Skaar, Jan-Eirik Welle; Haug, Nicolai; Stasik, Alexander Johannes; Einevoll, Gaute & Tøndel, Kristin (2023). Metamodelling of a two-population spiking neural network. PLoS Computational Biology. ISSN 1553-734X. 19(11). doi: 10.1371/journal.pcbi.1011625. Full text in Research Archive Show summary
In computational neuroscience, hypotheses are often formulated as bottom-up mechanistic models of the systems in question, consisting of differential equations that can be numerically integrated forward in time. Candidate models can then be validated by comparison against experimental data. The model outputs of neural network models depend on both neuron parameters, connectivity parameters and other model inputs. Successful model fitting requires sufficient exploration of the model parameter space, which can be computationally demanding. Additionally, identifying degeneracy in the parameters, i.e. different combinations of parameter values that produce similar outputs, is of interest, as they define the subset of parameter values consistent with the data. In this computational study, we apply metamodels to a two-population recurrent spiking network of point-neurons, the so-called Brunel network. Metamodels are data-driven approximations to more complex models with more desirable computational properties, which can be run considerably faster than the original model. Specifically, we apply and compare two different metamodelling techniques, masked autoregressive flows (MAF) and deep Gaussian process regression (DGPR), to estimate the power spectra of two different signals; the population spiking activities and the local field potential. We find that the metamodels are able to accurately model the power spectra in the asynchronous irregular regime, and that the DGPR metamodel provides a more accurate representation of the simulator compared to the MAF metamodel. Using the metamodels, we estimate the posterior probability distributions over parameters given observed simulator outputs separately for both LFP and population spiking activities. We find that these distributions correctly identify parameter combinations that give similar model outputs, and that some parameters are significantly more constrained by observing the LFP than by observing the population spiking activities.

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Pettersen, Markus; Haug, Nicolai; Bergli, Joakim; Surowiec, Thomas & Lepperød, Mikkel (2025). Exponential Maps as an Interpretable Framework for Generating Neural Spatial Representations from First-Principles. Full text in Research Archive
Olsen, Elise; Fjeldstad, Maria; Thon, Sarah; Haug, Nicolai; Lepperød, Mikkel & Sugar, Jørgen [Show all 8 contributors for this article] (2025). Oscillatory Rhythms in the Hippocampus and Entorhinal Cortex During Social Recognition. Full text in Research Archive
Skaar, Jan-Eirik Welle; Haug, Nicolai & Plesser, Hans Ekkehard (2024). A computationally efficient simplification of the Brunel-Wang NMDA model: Numerical approach and first results. Full text in Research Archive
Valverde, Jesús Andrés Espinoza; Müller, Eric; Haug, Nicolai; Schöfmann, Catherine Mia; Linssen, Charl & Senk, Johanna [Show all 32 contributors for this article] (2024). NEST 3.7 (Software). Zenodo. Full text in Research Archive
Graber, Steffen; Mitchell, Jessica; Kurth, Anno Christopher; Terhorst, Dennis; Skaar, Jan-Eirik Welle & Schöfmann, Catherine Mia [Show all 14 contributors for this article] (2024). NEST 3.8 (Software). Zenodo. Full text in Research Archive
Haug, Nicolai; Skaar, Jan-Eirik Welle; Mørk, Håkon; Spreizer, Sebastian; Morrison, Abigail & Schepper, Robin de [Show all 25 contributors for this article] (2023). NEST 3.5 (Software). Zenodo. Full text in Research Archive Show summary
NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons.
Sinha, Ankur; Schepper, Robin de; Pronold, Jari; Mitchell, Jessica; Mørk, Håkon & Babu, Pooja Nagendra [Show all 24 contributors for this article] (2023). NEST 3.4 (Software). Zenodo. Full text in Research Archive Show summary
NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons.
Villamar, Jose; Vogelsang, Jan; Linssen, Charl; Kunkel, Susanne; Kurth, Anno & Schöfmann, Catherine Mia [Show all 28 contributors for this article] (2023). NEST 3.6 (Software). Zenodo. Full text in Research Archive Show summary
NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons.

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Published Sep. 1, 2025 6:00 AM