Publications
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Danieli, Krubeal & Lepperød, Mikkel Elle
(2026).
A bio-inspired minimal model for non-stationary K-armed bandits.
Biological cybernetics.
ISSN 0340-1200.
120,
p. 1–18.
doi:
10.1007/s00422-026-01037-5.
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While reinforcement learning algorithms have made significant progress in solving multi-armed bandit problems, they often lack biological plausibility in architecture and dynamics. Here, we propose a bio-inspired neural model based on interacting populations of rate neurons, drawing inspiration from the orbitofrontal cortex and anterior cingulate cortex. Our model reports robust performance across various stochastic bandit problems, matching the effectiveness of standard algorithms such as Thompson Sampling and UCB. Notably, the model exhibits adaptive behavior: employing greedy strategies in low-uncertainty situations while increasing exploratory behavior as uncertainty rises. Through evolutionary optimization, the model’s hyperparameters converged to values that align with the principles of synaptic mechanisms, particularly in terms of synapse-dependent neural activity and learning rate adaptation. These findings suggest that biologically-inspired computational architectures can achieve competitive performance while providing insights into neural mechanisms of decision-making under uncertainty.
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Kvalsund, Mia-Katrin Ose; Ellefsen, Kai Olav; Glette, Kyrre; Pontes-Filho, Sidney & Lepperød, Mikkel Elle
(2026).
Sensor movement drives emergent attention and scalability in active neural cellular automata.
Neural Networks.
ISSN 0893-6080.
200.
doi:
10.1016/j.neunet.2026.108798.
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Nymoen, Ingeborg; Rogge, Frederik Sebastian; Aasen, Anna Hjertvik; Grødem, Sverre; Malthe-Sørenssen, Anders & Lepperød, Mikkel Elle
[Show all 9 contributors for this article]
(2026).
Task and Behavior-Related Variables Are Encoded by the Postrhinal and Medial Entorhinal Cortex During Non-Spatial Associative Learning.
eLIFE.
14.
doi:
10.7554/elife.106095.1.
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The medial entorhinal cortex (MEC) is pivotal in spatial computations and episodic memory. In particular, an animal’s position can be decoded from the activity of entorhinal grid cells. However, it remains elusive whether MEC could play a more general role in different types of associative learning and how the representations develop during the learning process. It has been shown that the postrhinal cortex (POR), which is directly connected to MEC, integrates visual stimuli with salient outcomes. Here, we use a non-spatial visual association task to investigate whether MEC neurons represent low-level visual cues during learning. Using a Go/NoGo visual association task, we recorded neural activity in MEC and POR throughout the learning phase as mice associated drifting gratings with rewarded, aversive, or neutral outcomes. Our findings reveal that the neural tuning curves in both the POR and MEC change with the learning of the task. From the start of training, the POR neurons exhibited response tuning to the visual cues, and the tuning was stable to cue orientations during learning. In contrast, MEC neurons did not initially respond very strongly to visual cues but developed a robust tuning toward the rewarded trials. While the MEC’s representation of visual information was limited, it encoded other task elements. A large fraction of the neurons formed distinct functional clusters that were either activated or suppressed by reward-related behavior. Remarkably, these clusters segregated anatomically in MEC and maintained strong within-cluster correlations before and after training. Notably, although the same functional clusters were apparent in the POR, they did not show any anatomical structure as in the MEC. Task reversal induced significant changes in network responses across both regions, with a decrease in overall task-responsive neurons but a slight increase in stimulus representation. Strikingly, information about the choice to lick emerged with learning in both brain areas, and most significantly within the functional cell clusters representing reward consumption and plus-cue stimulus. Our results demonstrate that although neurons in MEC and POR develop behavior-modulated tuning during learning of a non-spatial visual association task, the MEC exhibits stronger within-cluster correlations and anatomical organization. Conversely, the POR population exhibits less structural organization and more specific stimulus-tuning, which is reflective of being a higher visual association area. Our findings reveal that the MEC can encode task– and behavior-related variables beyond spatial information.
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Guichard, Etienne; Reimers, Felix; Kvalsund, Mia; Lepperød, Mikkel Elle & Nichele, Stefano
(2025).
ARC-NCA: Towards Developmental Solutions to the Abstraction and Reasoning Corpus.
Artificial Life Conference Proceedings.
doi:
10.1162/isal.a.833.
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The Abstraction and Reasoning Corpus (ARC), later renamed ARC-AGI, poses a fundamental challenge in artificial general intelligence (AGI), requiring solutions that exhibit robust abstraction and reasoning capabilities across diverse tasks, while only few (with median count of three) correct examples are presented. While ARC-AGI remains very challenging for artificial intelligence systems, it is rather easy for humans. This paper introduces ARC-NCA, a developmental approach leveraging standard Neural Cellular Automata (NCA) and NCA enhanced with hidden memories (EngramNCA) to tackle the ARC-AGI benchmark. NCAs are employed for their inherent ability to simulate complex dynamics and emergent patterns, mimicking developmental processes observed in biological systems. Developmental solutions may offer a promising avenue for enhancing AI’s problem-solving capabilities beyond mere training data extrapolation. ARC-NCA demonstrates how integrating developmental principles into computational models can foster adaptive reasoning and abstraction.
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Pontes-Filho, Sidney; Nichele, Stefano & Lepperød, Mikkel Elle
(2025).
Reservoir Computing with Evolved Critical Neural Cellular Automata.
In Witkowski, Olaf; Adams, Alyssa M.; Sinapayen, Lana; Baltieri, Manuel & Khosravy, Mahdi (Ed.),
ALIFE 2025: Ciphers of Life: Proceedings of the Artificial Life Conference 2025.
MIT Press.
doi:
10.1162/isal.a.832.
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Criticality is a behavioral state in dynamical systems that is known to present the highest computation capabilities, i.e., information transmission, storage, and modification. Therefore, such systems are ideal candidates as a substrate for reservoir computing, a subfield in artificial intelligence. Our choice of a substrate is a cellular automaton (CA) governed by an artificial neural network, also known as neural cellular automaton (NCA). We apply evolution strategy to optimize the NCA to achieve criticality, demonstrated by power law distributions in structures called avalanches. With an evolved critical NCA, the substrate is tested for reservoir computing. Our evaluation of the substrate is performed with two benchmarks, 5-bit memory task and image classification of handwritten digits. The result of the 5-bit memory task achieved a perfect score and the system managed to remember all 5 bits. The result for the image classification task matched and sometimes surpassed the performance of the best elementary CA for this task. Moreover, the proposed critical NCA may operate as a self-organized critical system, due to its robustness to extreme initial conditions.
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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.
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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.
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Beshkov, Kosio; Verhellen, Jonas & Lepperød, Mikkel Elle
(2025).
Isometric representations in neural networks improve robustness.
Scientific Reports.
15.
doi:
10.1038/s41598-025-20619-0.
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Artificial and biological agents are unable to learn given completely random and unstructured data. The structure of data is encoded in the distance or similarity relationships between data points. In the context of neural networks, the neuronal activity within a layer forms a representation reflecting the transformation that the layer implements on its inputs. In order to utilize the structure in the data in a truthful manner, such representations should reflect the input distances and thus be continuous and isometric. Supporting this statement, findings in neuroscience propose that generalization and robustness are tied to neural representations being continuously differentiable. Furthermore, representations of objects have the capacity of being hierarchical. Combined together, these two conditions imply that neural networks need to both preserve the distances between inputs as well as have the capacity to apply cuts at different resolutions, corresponding to different levels of a hierarchy. During cross-entropy classification, the metric and structural properties of network representations are usually broken both between and within classes. To achieve and study this behavior, we train neural networks to perform classification while simultaneously maintaining the metric structure within each class at potentially different levels of a hierarchy, leading to continuous and isometric within-class representations. We show that such network representations turn out to be a beneficial component for making accurate and robust inferences about the world. We come up with a network architecture that facilitates hierarchical manipulation of internal neural representations. We verify that our isometric regularization term improves the robustness to adversarial attacks on MNIST and CIFAR10. Finally, we use toy datasets and show that the learned map is isometric everywhere, except around decision boundaries.
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Holzhausen, Konstantin; Haga, Mia Merlid; Torvik, Håkon Olav; Malthe-Sørenssen, Anders & Lepperød, Mikkel Elle
(2025).
Exploring biologically inspired mechanisms of adversarial robustness.
Neural Computing & Applications.
ISSN 0941-0643.
37,
p. 9331–9342.
doi:
10.1007/s00521-025-11019-6.
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Backpropagation-optimized artificial neural networks, while precise, lack robustness, leading to unforeseen behaviors that affect their safety. Biological neural systems do solve some of these issues already. Unlike artificial models, biological neurons adjust connectivity based on neighboring cell activity. Understanding the biological mechanisms of robustness can pave the way toward building trustworthy and safe systems. Robustness in neural representations is hypothesized to correlate with the smoothness of the encoding manifold. Recent work suggests power law covariance spectra, which were observed studying the primary visual cortex of mice, to be indicative of a balanced trade-off between accuracy and robustness in representations. Here, we show that unsupervised local learning models with winner takes all dynamics learn such power law representations, providing upcoming studies a mechanistic model with that characteristic. Our research aims to understand the interplay between geometry, spectral properties, robustness, and expressivity in neural representations. Hence, we study the link between representation smoothness and spectrum by using weight, Jacobian and spectral regularization while assessing performance and adversarial robustness. Our work serves as a foundation for future research into the mechanisms underlying power law spectra and optimally smooth encodings in both biological and artificial systems. The insights gained may elucidate the mechanisms that realize robust neural networks in mammalian brains and inform the development of more stable and reliable artificial systems.
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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.
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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.
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Pettersen, Markus; Rogge, Frederik & Lepperød, Mikkel
(2024).
Search Learning Place Cell Representations and Context-Dependent Remapping.
Advances in Neural Information Processing Systems.
ISSN 1049-5258.
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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.
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Sønstebø, Jakob; Brunborg, Herman & Lepperød, Mikkel
(2023).
Spikeometric: Linear Non-Linear Cascade Spiking Neural Networks with Pytorch Geometric.
Journal of Open Source Software (JOSS).
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Lepperød, Mikkel Elle; Stöber, Tristan Manfred; Hafting, Torkel; Fyhn, Marianne & Kording, Konrad Paul
(2023).
Inferring causal connectivity from pairwise recordings and optogenetics.
PLoS Computational Biology.
ISSN 1553-734X.
19(11).
doi:
10.1371/journal.pcbi.1011574.
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To understand the neural mechanisms underlying brain function, neuroscientists aim to quantify causal interactions between neurons, for instance by perturbing the activity of neuron A and measuring the effect on neuron B. Recently, manipulating neuron activity using light-sensitive opsins, optogenetics, has increased the specificity of neural perturbation. However, using widefield optogenetic interventions, multiple neurons are usually perturbed, producing a confound—any of the stimulated neurons can have affected the postsynaptic neuron making it challenging to discern which neurons produced the causal effect. Here, we show how such confounds produce large biases in interpretations. We explain how confounding can be reduced by combining instrumental variables (IV) and difference in differences (DiD) techniques from econometrics. Combined, these methods can estimate (causal) effective connectivity by exploiting the weak, approximately random signal resulting from the interaction between stimulation and the absolute refractory period of the neuron. In simulated neural networks, we find that estimates using ideas from IV and DiD outperform naïve techniques suggesting that methods from causal inference can be useful to disentangle neural interactions in the brain.
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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.
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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.
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Kamps, Marc de; Lepperød, Mikkel & Lai, Yi
(2022).
MIIND: A Population-Level Neural Simulator Incorporating Stochastic Point Neuron Models.
In Jaeger, Dieter & Jung, Ranu (Ed.),
Encyclopedia of Computational Neuroscience.
Springer Science+Business Media B.V..
ISSN 9781461466741.
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Osborne, Hugh; Lai, Yi Ming; Lepperød, Mikkel Elle; Sichau, David; Deutz, Lukas & Kamps, Marc de
(2021).
MIIND : A Model-Agnostic Simulator of Neural Populations.
Frontiers in Neuroinformatics.
15,
p. 1–29.
doi:
10.3389/fninf.2021.614881.
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MIIND is a software platform for easily and efficiently simulating the behaviour of interacting populations of point neurons governed by any 1D or 2D dynamical system. The simulator is entirely agnostic to the underlying neuron model of each population and provides an intuitive method for controlling the amount of noise which can significantly affect the overall behaviour. A network of populations can be set up quickly and easily using MIIND's XML-style simulation file format describing simulation parameters such as how populations interact, transmission delays, post-synaptic potentials, and what output to record. During simulation, a visual display of each population's state is provided for immediate feedback of the behaviour and population activity can be output to a file or passed to a Python script for further processing. The Python support also means that MIIND can be integrated into other software such as The Virtual Brain. MIIND's population density technique is a geometric and visual method for describing the activity of each neuron population which encourages a deep consideration of the dynamics of the neuron model and provides insight into how the behaviour of each population is affected by the behaviour of its neighbours in the network. For 1D neuron models, MIIND performs far better than direct simulation solutions for large populations. For 2D models, performance comparison is more nuanced but the population density approach still confers certain advantages over direct simulation. MIIND can be used to build neural systems that bridge the scales between an individual neuron model and a population network. This allows researchers to maintain a plausible path back from mesoscopic to microscopic scales while minimising the complexity of managing large numbers of interconnected neurons. In this paper, we introduce the MIIND system, its usage, and provide implementation details where appropriate.
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Lepperød, Mikkel Elle; Christensen, Ane Charlotte; Lensjø, Kristian Kinden; Buccino, Alessio Paolo; Yu, Jai & Fyhn, Marianne
[Show all 7 contributors for this article]
(2021).
Optogenetic pacing of medial septum parvalbumin-positive cells disrupts temporal but not spatial firing in grid cells.
Science Advances.
7(19).
doi:
10.1126/sciadv.abd5684.
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Grid cells in the medial entorhinal cortex (MEC) exhibit remarkable spatial activity patterns with spikes coordinated by theta oscillations driven by the medial septal area (MSA). Spikes from grid cells progress relative to the theta phase in a phenomenon called phase precession, which is suggested as essential to create the spatial periodicity of grid cells. Here, we show that optogenetic activation of parvalbumin-positive (PV+) cells in the MSA enabled selective pacing of local field potential (LFP) oscillations in MEC. During optogenetic stimulation, the grid cells were locked to the imposed pacing frequency but kept their spatial patterns. Phase precession was abolished, and speed information was no longer reflected in the LFP oscillations but was still carried by rate coding of individual MEC neurons. Together, these results support that theta oscillations are not critical to the spatial pattern of grid cells and do not carry a crucial velocity signal.
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Christensen, Ane Charlotte; Lensjø, Kristian Kinden; Lepperød, Mikkel Elle; Dragly, Svenn-Arne; Sutterud, Hallvard & Blackstad, Jan Sigurd
[Show all 8 contributors for this article]
(2021).
Perineuronal nets stabilize the grid cell network.
Nature Communications.
12.
doi:
10.1038/s41467-020-20241-w.
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Grid cells are part of a widespread network which supports navigation and spatial memory. Stable grid patterns appear late in development, in concert with extracellular matrix aggregates termed perineuronal nets (PNNs) that condense around inhibitory neurons. It has been suggested that PNNs stabilize synaptic connections and long-term memories, but their role in the grid cell network remains elusive. We show that removal of PNNs leads to lower inhibitory spiking activity, and reduces grid cells’ ability to create stable representations of a novel environment. Furthermore, in animals with disrupted PNNs, exposure to a novel arena corrupted the spatiotemporal relationships within grid cell modules, and the stored representations of a familiar arena. Finally, we show that PNN removal in entorhinal cortex distorted spatial representations in downstream hippocampal neurons. Together this work suggests that PNNs provide a key stabilizing element for the grid cell network.
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Lepperød, Mikkel Elle; Dragly, Svenn-Arne; Buccino, Alessio Paolo; Mobarhan, Milad; Malthe-Sørenssen, Anders & Hafting, Torkel
[Show all 7 contributors for this article]
(2020).
Experimental Pipeline (Expipe): A Lightweight Data Management Platform to Simplify the Steps From Experiment to Data Analysis.
Frontiers in Neuroinformatics.
14.
doi:
10.3389/fninf.2020.00030.
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As experimental neuroscience is moving toward more integrative approaches, with a variety of acquisition techniques covering multiple spatiotemporal scales, data management is becoming increasingly challenging for neuroscience laboratories. Often, datasets are too large to practically be stored on a laptop or a workstation. The ability to query metadata collections without retrieving complete datasets is therefore critical to efficiently perform new analyses and explore the data. At the same time, new experimental paradigms lead to constantly changing specifications for the metadata to be stored. Despite this, there is currently a serious lack of agile software tools for data management in neuroscience laboratories. To meet this need, we have developed Expipe, a lightweight data management framework that simplifies the steps from experiment to data analysis. Expipe provides the functionality to store and organize experimental data and metadata for easy retrieval in exploration and analysis throughout the experimental pipeline. It is flexible in terms of defining the metadata to store and aims to solve the storage and retrieval challenges of data/metadata due to ever changing experimental pipelines. Due to its simplicity and lightweight design, we envision Expipe as an easy-to-use data management solution for experimental laboratories, that can improve provenance, reproducibility, and sharing of scientific projects.
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Kamps, Marc de; Lepperød, Mikkel Elle & Lai, Yi Ming
(2019).
Computational geometry for modeling neural populations: From visualization to simulation.
PLoS Computational Biology.
ISSN 1553-734X.
15(3),
p. 1–41.
doi:
10.1371/journal.pcbi.1006729.
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The importance of a mesoscopic description level of the brain has now been well established. Rate based models are widely used, but have limitations. Recently, several extremely efficient population-level methods have been proposed that go beyond the characterization of a population in terms of a single variable. Here, we present a method for simulating neural populations based on two dimensional (2D) point spiking neuron models that defines the state of the population in terms of a density function over the neural state space. Our method differs in that we do not make the diffusion approximation, nor do we reduce the state space to a single dimension (1D). We do not hard code the neural model, but read in a grid describing its state space in the relevant simulation region. Novel models can be studied without even recompiling the code. The method is highly modular: variations of the deterministic neural dynamics and the stochastic process can be investigated independently. Currently, there is a trend to reduce complex high dimensional neuron models to 2D ones as they offer a rich dynamical repertoire that is not available in 1D, such as limit cycles. We will demonstrate that our method is ideally suited to investigate noise in such systems, replicating results obtained in the diffusion limit and generalizing them to a regime of large jumps. The joint probability density function is much more informative than 1D marginals, and we will argue that the study of 2D systems subject to noise is important complementary to 1D systems.
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Buccino, Alessio Paolo; Lepperød, Mikkel Elle; Dragly, Svenn-Arne; Häfliger, Philipp; Fyhn, Marianne & Hafting, Torkel
(2018).
Open source modules for tracking animal behavior and closed-loop stimulation based on Open Ephys and Bonsai.
Journal of Neural Engineering.
ISSN 1741-2560.
15,
p. 1–12.
doi:
10.1088/1741-2552/aacf45.
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Objective: A major goal in systems neuroscience is to determine the causal relationship between neural activity and behavior. To this end, methods that combine monitoring neural activity, behavioral tracking, and targeted manipulation of neurons in closed-loop are powerful tools. However, commercial systems that allow these types of experiments are usually expensive and rely on non-standardized data formats and proprietary software which may hinder user-modifications for specific needs. In order to promote reproducibility and data-sharing in science, transparent software and standardized data formats are an advantage. Here, we present an open source, low-cost, adaptable, and easy to set-up system for combined behavioral tracking, electrophysiology, and closed-loop stimulation. Approach: Based on the Open Ephys system (www.open-ephys.org) we developed multiple modules to include real-time tracking and behavior-based closed-loop stimulation. We describe the equipment and provide a step-by-step guide to set up the system. Combining the open source software Bonsai (bonsai-rx.org) for analyzing camera images in real time with the newly developed modules in Open Ephys, we acquire position information, visualize tracking, and perform tracking-based closed-loop stimulation experiments. To analyze the acquired data we provide an open source file reading package in Python. Main results: The system robustly visualizes real-time tracking and reliably recovers tracking information recorded from a range of sampling frequencies (30–1000 Hz). We combined electrophysiology with the newly-developed tracking modules in Open Ephys to record place cell and grid cell activity in the hippocampus and in the medial entorhinal cortex, respectively. Moreover, we present a case in which we used the system for closed-loop optogenetic stimulation of entorhinal grid cells. Significance: Expanding the Open Ephys system to include animal tracking and behavior-based closed-loop stimulation extends the availability of high-quality, low-cost experimental setup within standardized data formats serving the neuroscience community.
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Aasebø, Ida E. J.; Lepperød, Mikkel Elle; Stavrinou, Maria; Nøkkevangen, Sandra; Einevoll, Gaute & Hafting, Torkel
[Show all 7 contributors for this article]
(2017).
Temporal processing in the visual cortex of the awake and anesthetized rat.
eNeuro.
4:e0059-17.2017(4),
p. 1–26.
doi:
10.1523/ENEURO.0059-17.2017.
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The activity pattern and temporal dynamics within and between neuron ensembles are essential features of information processing and believed to be profoundly affected by anesthesia. Much of our general understanding of sensory information processing, including computational models aimed at mathematically simulating sensory information processing, rely on parameters derived from recordings conducted on animals under anesthesia. Due to the high variety of neuronal subtypes in the brain, population-based estimates of the impact of anesthesia may conceal unit- or ensemble-specific effects of the transition between states. Using chronically implanted tetrodes into primary visual cortex (V1) of rats, we conducted extracellular recordings of single units and followed the same cell ensembles in the awake and anesthetized states. We found that the transition from wakefulness to anesthesia involves unpredictable changes in temporal response characteristics. The latency of single-unit responses to visual stimulation was delayed in anesthesia, with large individual variations between units. Pair-wise correlations between units increased under anesthesia, indicating more synchronized activity. Further, the units within an ensemble show reproducible temporal activity patterns in response to visual stimuli that is changed between states, suggesting state-dependent sequences of activity. The current dataset, with recordings from the same neural ensembles across states, is well suited for validating and testing computational network models. This can lead to testable predictions, bring a deeper understanding of the experimental findings and improve models of neural information processing. Here, we exemplify such a workflow using a Brunel network model.
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Lensjø, Kristian Kinden; Lepperød, Mikkel Elle; Dick, Gunnar; Hafting, Torkel & Fyhn, Marianne
(2017).
Removal of perineuronal nets unlocks juvenile plasticity through network mechanisms of decreased inhibition and increased gamma Activity.
Journal of Neuroscience.
ISSN 0270-6474.
37(5),
p. 1269–1283.
doi:
10.1523/JNEUROSCI.2504-16.2016.
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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.
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Danieli, Krubeal; Fyhn, Marianne & Lepperød, Mikkel
(2024).
Neuromodulated online cognitive maps for reinforcement learning.
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Pettersen, Markus; Schøyen, Vemund; Østby, Mattis & Lepperød, Mikkel
(2024).
Self-Supervised Grid Cells Without Path Integration.
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Pettersen, Markus Borud; Rogge, Frederik Sebastian & Lepperød, Mikkel Elle
(2024).
Learning place cell representations and context-dependent remapping.
doi:
https:/neurips.cc/virtual/2024/poster/96470.
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Hippocampal place cells are known for their spatially selective firing patterns, which has led to the suggestion that they encode an animal's location. However, place cells also respond to contextual cues, such as smell. Furthermore, they have the ability to remap, wherein the firing fields and rates of cells change in response to changes in the environment. How place cell responses emerge, and how these representations remap is not fully understood. In this work, we propose a similarity-based objective function that translates proximity in space, to proximity in representation. We show that a neural network trained to minimize the proposed objective learns place-like representations. We also show that the proposed objective is easily extended to include other sources of information, such as context information, in the same way. When trained to encode multiple contexts, networks learn distinct representations, exhibiting remapping behaviors between contexts. The proposed objective is invariant to orthogonal transformations. Such transformations of the original trained representation (e.g. rotations), therefore yield new representations distinct from the original, without explicit relearning, akin to remapping. Our findings shed new light on the formation and encoding properties of place cells, and also demonstrate an interesting case of representational reuse.
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Storehaug, Ida Torkjellsdatter; Lepperød, Mikkel Elle & Wahl, Andreas Solberg
(2024).
#145 - Fiskesprut, Frankrike varmest i universet, og fri vilje.
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Tre raringer fekter med funfacts. Med spyd, bly og elektroencefalografi.
Raringer:
Ida Torkjellsdatter Storehaug
Mikkel E. Lepperød
Andreas Wahl (@andreas__wahl)
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Storehaug, Ida Torkjellsdatter; Lepperød, Mikkel Elle & Wahl, Andreas Solberg
(2024).
#148 - Orme-smoothie, død i verdensrommet, og sugekopp på øyet.
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Tre raringer møtes for å fekte med facts! Et eggeglass fylt med barberskum, ormen som spiser minner, og i verdensrommet er det ingen som vet hvordan du dør.
Raringer:
Mikkel E. Lepperød,
Ida Torkjellsdatter Storehaug,
Andreas Wahl (@andreas__wahl)
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Einevoll, Gaute & Lepperød, Mikkel Elle
(2024).
#19: On neuro-AI on the boat - part 1 of 2 - with Paul Middlebrooks, Ken Harris, Andreas Tolias,
Mikkel Lepperød.
[Internet].
Podcast "Theoretical Neuroscience Podcast".
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Einevoll, Gaute & Lepperød, Mikkel Elle
(2024).
#20: On neuro-AI on the boat - part 2 of 2 - with Paul Middlebrooks, Cristina Savin, Tim Vogels,
Mikkel Lepperød.
[Internet].
Podcast "Theoretical Neuroscience Podcast".
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Danieli, Krubeal; Fyhn, Marianne & Lepperød, Mikkel
(2023).
Online and memory-driven navigation through neuromodulation-based synaptic plasticity.
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Lynnebakken, Hilde; Lepperød, Mikkel & Schøyen, Vemund
(2023).
Hvordan holder hjernen orden på hvor du er? Ny kunnskap skal gi bedre KI.
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Riegler, Michael; Lepperød, Mikkel & Røstad, Lillian
(2023).
Norge som fanebærer for etikk i en uforutsigbar framtid.
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Kvalsund, Mia-Katrin; Ellefsen, Kai; Glette, Kyrre; Pontes-Filho, Sidney & Lepperød, Mikkel
(2023).
The distributed neocortex: How neuroscience can inspire distributed AI systems.
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Tarigopula, Homa; Malthe-Sørenssen, Anders & Lepperød, Mikkel Elle
(2023).
Model-based meta-reinforcement learning for alchemy.
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Meta-learning emphasises the ‘learning to learn’ paradigm where the similar characteristics between the tasks generate a common basis bearing relevance that extends beyond the trained tasks and acts as an inductive bias that helps the learning agent to quickly generalize to unseen tasks from the same distribution. For example during navigation, understanding the underlying structure of the environment can aid inferences when going back to an explored state from an unexplored point of view. Recent work by Whittington et al 2020 has shown that the underlying graph defining the structure once learnt is reusable across different environments consisting of different sensory observations, thus forming an invariant feature basis. This can be extended to the relational inference scenarios encountered by reinforcement learning (RL) agents. A recently introduced benchmark ‘Alchemy' aims at challenging meta-RL by providing both a convenient interface and an optimal solution for assessing agent efficiency. The meta-RL agent navigating the alchemy environment is challenged with understanding the latent causal structure to excel at this task with maximum reward. We aim to train model-based meta-RL agents to solve alchemy, by introducing useful inductive bias for capturing the underlying structure of explored environments. We hypothesize that training such an agent, in addition to improving the meta-RL knowledge base, may help shed light on the interactions between Prefrontal cortex and Entorhinal cortex. Here, we present preliminary results from initial studies that show promising performance on the task compared to the two deep RL agents (VMPO, IMPALA) previously deployed to solve Alchemy.
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Holzhausen, Konstantin; Lepperød, Mikkel & Malthe-Sørenssen, Anders
(2022).
Robustness of neural networks trained with biologically inspired local learning rules.
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Schøyen, Vemund; Pettersen, Markus; Holzhausen, Konstantin; Malthe-Sørenssen, Anders & Lepperød, Mikkel
(2022).
Navigating Multiple Environments with Emergent Grid Cell Remapping.
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Schøyen, Vemund; Pettersen, Markus; Holzhausen, Konstantin; Malthe-Sørenssen, Anders & Lepperød, Mikkel
(2022).
Place and grid cell navigation in multiple environments.
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Rogge, Frederik; Malthe-Sørenssen, Anders; Fyhn, Marianne & Lepperød, Mikkel
(2022).
On the Structural Sensitivity of the Successor Representation.
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Pettersen, Markus; Schøyen, Vemund; Malthe-Sørenssen, Anders & Lepperød, Mikkel
(2022).
Emergent Place Cells and Remapping in a Recurrent Neural Network Model.
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Targopula, Homa; Malthe-Sørenssen, Anders & Lepperød, Mikkel
(2022).
Model-based meta reinforcement learning for alchemy.
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Tarigopula, Homa; Malthe-Sørenssen, Anders & Lepperød, Mikkel Elle
(2022).
Model based meta reinforcement learning for alchemy.
doi:
https:/fensforum.org/wp-content/uploads/2023/01/FENS2022.pdf.
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Meta-learning emphasises the ‘learning to learn’ paradigm where the similar characteristics between the tasks generate a common basis bearing relevance that extends beyond the trained tasks and acts as an inductive bias that helps the learning agent to quickly generalize to unseen tasks from the same distribution. For example during navigation, understanding the underlying structure of the environment can aid inferences when going back to an explored state from an unexplored point of view. Recent work by Whittington et al 2020 has shown that the underlying graph defining the structure once learnt is reusable across different environments consisting of different sensory observations, thus forming an invariant feature basis. This can be extended to the relational inference scenarios encountered by reinforcement learning (RL) agents. A recently introduced benchmark ‘Alchemy' aims at challenging meta-RL by providing both a convenient interface and an optimal solution for assessing agent efficiency. The meta-RL agent navigating the alchemy environment is challenged with understanding the latent causal structure to excel at this task with maximum reward. We aim to train model-based meta-RL agents to solve alchemy, by introducing useful inductive bias for capturing the underlying structure of explored environments. We hypothesize that training such an agent, in addition to improving the meta-RL knowledge base, may help shed light on the interactions between Prefrontal cortex and Entorhinal cortex. Here, we present preliminary results from initial studies that show promising performance on the task compared to the two deep RL agents (VMPO, IMPALA) previously deployed to solve Alchemy.
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Rogge, Frederik Sebastian; Nymoen, Ingeborg; Lepperød, Mikkel Elle; Hafting, Torkel; Fyhn, Marianne & Lensjø, Kristian
(2022).
Learning flexibility in parahippocampal networks.
doi:
https:/fensforum.org/wp-content/uploads/2023/01/FENS2022.pdf.
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Mammals show remarkable flexibility of behaviors, and have the ability to abstract specific rules and learning sets into generalized low dimensional structures that can be applied in new conditions. Recent work suggests that this generalization could take place in parahippocampal regions, in particular in the entorhinal cortex (EC). Here, the medial part (MEC) seems to extract the structure of state space irrespective of modality (e.g. spatial or sound). The lateral part (LEC) receives input about complex features of objects and multimodal sensory information from association cortices, including the postrhinal cortex (POR). This puts the EC in a unique position to function as an integration center for higher order representations. To investigate the role of the entorhinal cortex in abstracting and generalizing task content, we have established a set of simple association tasks for mice that enable both rule switching and change of sensory modality. We train mice in these tasks over multiple sessions and record large neuronal populations in the EC during and after training using two-photon imaging. To this end, we utilize custom right-angle prism assemblies that cover either the MEC, or POR and LEC. We hypothesize that both MEC and LEC will display learning-induced changes in the low dimensional neural representations over time, with consistent structures that are used and transferred across different tasks. Our preliminary data from mice trained in a visual association task indicate that neurons in both regions develop representations of task structure with training.
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Lepperød, Mikkel Elle; Stöber, Tristan; Hafting, Torkel; Fyhn, Marianne & Kording, Konrad
(2022).
Inferring causal connectivity from pairwise recordings and optogenetics.
BioRxiv.
ISSN 0362-4331.
doi:
10.1101/463760.
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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.
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Nichele, Stefano; Sæbo, Solve; Pettersen, Klas Henning & Lepperød, Mikkel Elle
(2022).
NeuroAI - A strategic opportunity for Norway and Europe.
Nordic Machine Intelligence (NMI).
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Beshkov, Kosio; Verhellen, Jonas & Lepperød, Mikkel Elle
(2022).
Manifold Propagation through Recurrent Neural Networks.
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Christensen, Ane Charlotte; Lensjø, Kristian Kinden; Lepperød, Mikkel Elle; Fyhn, Marianne & Hafting, Torkel
(2017).
The role of extracellular matrix molecules for spatial representations in medial entorhinal cortex.
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Wigestrand, Mattis B; Aasebø, Ida E. J.; Tully, Annabelle; Lepperød, Mikkel Elle; Hafting, Torkel & Fyhn, Marianne
(2016).
Lateral hypothalamic outputs control innate defensive responses.
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When facing a threat, animals display defensive behaviors that are critical for survival. The hypothalamus is believed to mediate such defensive responses, but the prevailing view includes a medial/lateral separation, where medial hypothalamus is thought to influence defensive responses, while the lateral hypothalamic area (LHA) regulates feeding and arousal. Here we identify two distinct neural populations in the LHA with surprising survival functions in rats. The two neural populations, defined by their output projections to either the periaqueductal gray (PAG) or the lateral habenula (LHb) have distinct anatomical placements along the anterior-posterior axis of the LHA and express distinct biochemical markers. Optogenetic stimulation of the two LHA populations robustly induced opposite active- or passive defensive responses. Further, chemogenetic silencing of LHA and optogenetic silencing of the LHA-PAG projection disrupted innate defensive responses to a predator-like looming shadow. Using single-unit recordings in the downstream LHb and PAG, we show that the LHA-LHb projection is largely excitatory, while the LHA-PAG projection is mainly inhibitory. We are currently using a miniaturized microscope to image the calcium activity of the two LHA populations during threat processing in behaving rats. Overall, our findings identify a novel LHA circuit that is critical for appropriate defensive responses to innate visual threats. The location of the LHA defense circuits, intermingled within circuitry that governs feeding, suggests that this circuit may provide rapid cross-talk between circuits that govern foraging- and anti-predator behaviors.
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Tennøe, Simen; Halnes, Geir; Lepperød, Mikkel Elle; Feinberg, Jonathan; Einevoll, Gaute & Langtangen, Hans Petter
(2016).
UncertainPy: A Python toolbox for uncertainty quantification in neural and neural-network models.
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Lensjø, Kristian Kinden; Lepperød, Mikkel Elle; Dick, Gunnar; Hafting, Torkel & Fyhn, Marianne
(2016).
Removal of perineuronal nets affects information processing and plasticity in the primary visual cortex.
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Lepperød, Mikkel Elle; Lensjø, Kristian Kinden; Christensen, Ane Charlotte; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute
(2015).
Building an entorhinal cortex network.
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Aasebø, Ida E. J.; Hafting, Torkel; Stavrinou, Maria; Sandra, Nøkkevangen; Lepperød, Bjørn Mikkel Elle & Fyhn, Marianne
(2015).
Temporal processing in the visual cortex in the awake and anesthetized rat.
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Christensen, Ane Charlotte; Lensjø, Kristian Kinden; Lepperød, Bjørn Mikkel Elle; Fyhn, Marianne & Hafting, Torkel
(2015).
The role of extracellular matrix molecules for spatial representations and plasticity in
entorhinal cortex.
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Lensjø, Kristian Kinden; Lepperød, Bjørn Mikkel Elle; Dick, Gunnar; Hafting, Torkel & Fyhn, Marianne
(2015).
Removal of perineuronal nets in the visual cortex leads to a reduced inhibition and mimics critical period plasticity.
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Rogge, Frederik Sebastian; Fyhn, Marianne; Malthe-Sørenssen, Anders; Lepperød, Mikkel Elle & Lensjø, Kristian
(2026).
Normative and Experimental Analyses of Learning-Dependent Representations in the Hippocampal-Parahippocampal System.
Universitetet i Oslo.
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The brain is thought to build a cognitive map of both physical and abstract spaces, helping us navigate the world, organize knowledge, and form new memories. In this thesis, I investigated key brain subregions to uncover the principles underlying the emergence of neural representations. I focused on the general properties of the neural code, how visual inputs are integrated, and how these representations reorganize through learning. To address these questions, I combined large-scale chronic two-photon calcium imaging in mice with normative modeling in artificial neural networks.
We show that neurons in the medial entorhinal cortex (MEC) develop task-relevant tuning during learning of a purely visual association task, pointing to a broader role beyond its classical link to spatial coding. In parallel, selective disruption of specific neuron types in the postrhinal cortex (POR) impaired learning of the same task, demonstrating their causal contribution. Furthermore, using artificial neural networks, we uncovered computational principles governing how spatial representations emerge and transform. Finally, we revealed dissociable contributions of POR and MEC to visually guided navigation, highlighting their distinct roles.
Together, these results shed new light on the emergence of neural representations in different brain regions and their transformation through learning.
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Published
July 2, 2025 11:08 AM
- Last modified
July 2, 2025 11:08 AM
