vemundss

• Pettersen, Markus Borud; Schøyen, Vemund Sigmundson; Malthe-Sørenssen, Anders & Lepperød, Mikkel Elle (2025). Learning place cells and remapping by decoding the cognitive map. eLIFE. 13. doi: 10.7554/elife.99302. Full text in Research Archive Show summary

  Hippocampal place cells are known for their spatially selective firing and are believed to encode an animal’s location while forming part of a cognitive map of space. These cells exhibit marked tuning curves and rate changes when an animal’s environment is sufficiently manipulated, in a process known as remapping. Place cells are accompanied by many other spatially tuned cells, such as border cells and grid cells, but how these cells interact during navigation and remapping is unknown. In this work, we build a normative place cell model wherein a neural network is tasked with accurate position reconstruction and path integration. Motivated by the notion of a cognitive map, the network’s position is estimated directly from its learned representations. To obtain a position estimate, we propose a non-trainable decoding scheme applied to network output units, inspired by the localized firing patterns of place cells. We find that output units learn place-like spatial representations, while upstream recurrent units become boundary-tuned. When the network is trained to perform the same task in multiple simulated environments, its place-like units learn to remap like biological place cells, displaying global, geometric, and rate remapping. These remapping abilities appear to be supported by rate changes in upstream units. While the model does not learn grid-like units, its place unit centers form clusters organized in a hexagonal lattice in open fields. When we decode the center locations of CA1 place fields in mice, we find preliminary evidence of a similar clustering tendency. This suggests a potential mechanism for the interaction between place cells, border cells, and grid cells. Our model provides a normative framework for learning spatial representations previously reserved for biological place cells, providing new insight into place cell field formation and remapping.

• Schøyen, Vemund Sigmundson; Beshkov, Kosio; Pettersen, Markus Borud; Hermansen, Erik; Holzhausen, Konstantin & Malthe-Sørenssen, Anders [Show all 8 contributors for this article] (2025). Hexagons all the way down: grid cells as a conformal isometric map of space. PLoS Computational Biology. ISSN 1553-734X. 21(2). doi: 10.1371/journal.pcbi.1012804. Full text in Research Archive Show summary

  Grid cells in the entorhinal cortex are known for their hexagonal spatial activity patterns and are thought to provide a neural metric for space, and support path integration. In this study, we further investigate grid cells as a metric of space by optimising them for a conformal isometric (CI) map of space using a model based on a superposition of plane waves. By optimising the phases within a single grid cell module, we find that the module can form a CI of two-dimensional flat space with phases arranging into a regular hexagonal pattern, supporting an accurate spatial metric. Additionally, we find that experimentally recorded grid cells exhibit CI properties, with one example module showing a phase arrangement similar to the hexagonal pattern observed in our model. These findings provide computational and preliminary experimental support for grid cells as a CI-based spatial representation. We also examine other properties that emerge in CI-optimised modules, including consistent energy expenditure across space and the minimal cell count required to support unique representation of space and maximally topologically persistent toroidal population activity. Altogether, our results suggest that grid cells are well-suited to form a CI map, with several beneficial properties arising from this organisation.

• Schøyen, Vemund; Pettersen, Markus; Holzhausen, Konstantin; Fyhn, Marianne; Malthe-Sørenssen, Anders & Lepperød, Mikkel (2023). Coherently remapping toroidal cells but not Grid cells are responsible for path integration in virtual agents. iScience. Full text in Research Archive
• Schøyen, Vemund Sigmundson; Pettersen, Markus Borud; Holzhausen, Konstantin; Fyhn, Marianne; Malthe-Sørenssen, Anders & Lepperød, Mikkel Elle (2023). Coherently remapping toroidal cells but not Grid cells are responsible for path integration in virtual agents. iScience. 26(11), p. 1–17. doi: 10.1016/j.isci.2023.108102. Full text in Research Archive Show summary

  It is widely believed that grid cells provide cues for path integration, with place cells encoding an animal’s location and environmental identity. When entering a new environment, these cells remap concurrently, sparking debates about their causal relationship. Using a continuous attractor recurrent neural network, we study spatial cell dynamics in multiple environments. We investigate grid cell remapping as a function of global remapping in place-like units through random resampling of place cell centers. Dimensionality reduction techniques reveal that a subset of cells manifest a persistent torus across environments. Unexpectedly, these toroidal cells resemble band-like cells rather than high grid score units. Subsequent pruning studies reveal that toroidal cells are crucial for path integration while grid cells are not. As we extend the model to operate across many environments, we delineate its generalization boundaries, revealing challenges with modeling many environments in current models.

View all works in NVA

• Pettersen, Markus; Schøyen, Vemund; Østby, Mattis & Lepperød, Mikkel (2024). Self-Supervised Grid Cells Without Path Integration. Full text in Research Archive
• Thon, Sarah Kriebel; Olsen, Elise Reppe; Fjeldstad, Maria Perona; Schøyen, Vemund Sigmundson; Lepperød, Mikkel Elle & Christensen, Ane Charlotte [Show all 8 contributors for this article] (2024). Social and spatial codes in the hippocampus. Full text in Research Archive
• Olsen, Elise Reppe; Fjeldstad, Maria Perona; Thon, Sarah Kriebel; Schøyen, Vemund Sigmundson; Lepperød, Mikkel Elle & Sugar, Jørgen Afseth [Show all 8 contributors for this article] (2024). Exploring social and spatial coding in the lateral and medial entorhinal cortex. Full text in Research Archive
• Fjeldstad, Maria Perona; Lepperød, Mikkel Elle; Schøyen, Vemund Sigmundson; Christensen, Ane Charlotte; Grødem, Sverre & Fyhn, Marianne [Show all 7 contributors for this article] (2024). Theta modulation of grid cells in the medial entorhinal cortex in a novel environment task. Full text in Research Archive
• Lynnebakken, Hilde; Lepperød, Mikkel & Schøyen, Vemund (2023). Hvordan holder hjernen orden på hvor du er? Ny kunnskap skal gi bedre KI. Full text in Research Archive
• Schøyen, Vemund; Pettersen, Markus; Holzhausen, Konstantin; Malthe-Sørenssen, Anders & Lepperød, Mikkel (2022). Navigating Multiple Environments with Emergent Grid Cell Remapping. Full text in Research Archive
• Schøyen, Vemund; Pettersen, Markus; Holzhausen, Konstantin; Malthe-Sørenssen, Anders & Lepperød, Mikkel (2022). Place and grid cell navigation in multiple environments. Full text in Research Archive
• Pettersen, Markus; Schøyen, Vemund; Malthe-Sørenssen, Anders & Lepperød, Mikkel (2022). Emergent Place Cells and Remapping in a Recurrent Neural Network Model. Full text in Research Archive
• Schøyen, Vemund Sigmundson; Pettersen, Markus Borud; Holzhousen, Konstantin; Fyhn, Marianne; Malthe-Sørenssen, Anders & Lepperød, Mikkel Elle (2022). Coherently Remapping Toroidal Cells But Not Grid Cells are Responsible for Path Integration in Virtual Agents. BioRxiv. ISSN 0362-4331. doi: 10.1101/2022.08.18.504379. Full text in Research Archive
• Thon, Sarah Kriebel; Christensen, Ane Charlotte; Fjeldstad, Maria P; Schøyen, Vemund Sigmundson; Olsen, Elise Reppe & Lepperød, Mikkel Elle [Show all 8 contributors for this article] (2022). Social and spatial codes in hippocampus CA2 and entorhinal cortex. Full text in Research Archive
• Schøyen, Vemund Sigmundson; Hagen, Per Espen; Midtgaard, Øivind & Warakagoda, Narada Dilp (2022). Automatic Detection of Seafloor Pipelines with Deep Learning. Hydro International. ISSN 1385-4569. 26(1), p. 10–13. Full text in Research Archive
• Schøyen, Vemund Sigmundson; Warakagoda, Narada Dilp & Midtgaard, Øivind (2021). Seafloor Pipeline Detection With Deep Learning. Full text in Research Archive

View all works in NVA