Espen Hagen

• Frei, Oleksandr; Hindley, Guy; Shadrin, Alexey; van der Meer, Dennis; Akdeniz, Bayram Cevdet & Hagen, Espen [Vis alle 16 forfattere av denne artikkelen] (2024). Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets. Nature Genetics. ISSN 1061-4036. 56(6), s. 1310–1318. doi: 10.1038/s41588-024-01771-1.
• Akdeniz, Bayram Cevdet ; Frei, Oleksandr; Hagen, Espen; Filiz, Tahir Tekin; Karthikeyan, Sandeep & Pasman, Joëlle [Vis alle 18 forfattere av denne artikkelen] (2024). COSGAP: COntainerized Statistical Genetics Analysis Pipelines. Bioinformatics Advances. ISSN 2635-0041. 4(1). doi: 10.1093/bioadv/vbae067.
• Bakken, Nora R.; Parker, Nadine; Hannigan, Laurie J.; Hagen, Espen; Parekh, Pravesh & Shadrin, Alexey [Vis alle 15 forfattere av denne artikkelen] (2023). Childhood trajectories of emotional and behavioral difficulties are related to polygenic liability for mood and anxiety disorders. medRxiv. doi: 10.1101/2023.11.21.23298804.
• Rimehaug, Atle Eskeland; Stasik, Alexander Johannes; Hagen, Espen; Billeh, Yazan N.; Siegle, Josh H. & Dai, Kael [Vis alle 10 forfattere av denne artikkelen] (2023). Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex. eLIFE. ISSN 2050-084X. 12. doi: 10.7554/eLife.87169. Fulltekst i vitenarkiv
• Holen, Børge; Shadrin, Alexey; Icick, Romain; Filiz, Tahir Tekin; Hindley, Guy Frederick Lanyon & Rødevand, Linn [Vis alle 19 forfattere av denne artikkelen] (2023). Genome-wide analyses reveal novel opioid use disorder loci and genetic overlap with schizophrenia, bipolar disorder, and major depression. Addiction Biology. ISSN 1355-6215. 28(6). doi: 10.1111/adb.13282. Fulltekst i vitenarkiv
• Rimehaug, Atle Eskeland; Stasik, Alexander Johannes; Hagen, Espen; Siegle, Joshua H.; Dai, Kael & Olsen, Shawn R. [Vis alle 9 forfattere av denne artikkelen] (2022). Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex. bioRxiv. ISSN 2692-8205. doi: 10.1101/2022.02.22.481540.
• Akdeniz, Bayram Cevdet ; Frei, Oleksandr; Hagen, Espen; Filiz, Tahir Tekin; Karthikeyan, Sandeep & Pasman, Joëlle [Vis alle 18 forfattere av denne artikkelen] (2022). COGEDAP: A COmprehensive GEnomic Data Analysis Platform. arXiv.org. ISSN 2331-8422.
• Hagen, Espen; Magnusson, Steinn; Ness, Torbjørn Vefferstad; Halnes, Geir; Babu, Pooja N. & Linssen, Charl [Vis alle 8 forfattere av denne artikkelen] (2022). Brain signal predictions from multi-scale networks using a linearized framework. PLoS Computational Biology. ISSN 1553-734X. 18(8). doi: 10.1371/journal.pcbi.1010353. Fulltekst i vitenarkiv
• Hagen, Espen; Chambers, Anna; Einevoll, Gaute; Pettersen, Klas Henning; Enger, Rune & Stasik, Alexander Johannes (2021). RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection. Neuroinformatics. ISSN 1539-2791. 19, s. 493–514. doi: 10.1007/s12021-020-09496-2. Fulltekst i vitenarkiv Vis sammendrag

  Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and behaviour rely on accurate SPW-R detection. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) detection method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The approach contrasts conventional routines that typically relies on hand-crafted, heuristic feature extraction and often laborious manual curation. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events obtained under controlled conditions. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. Its output predictions can be interpreted as time-varying probabilities of SPW-R events for the duration of the inputs. A simple thresholding applied to the output probabilities is found to identify times of SPW-R events with high precision. The non-causal, or bidirectional variant of the proposed algorithm demonstrates consistently better accuracy compared to the causal, or unidirectional counterpart. Reference implementations of the algorithm, named ‘RippleNet’, are open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.

• Næss, Solveig; Halnes, Geir; Hagen, Espen; Hagler, Donald J.; Dale, Anders M. & Einevoll, Gaute [Vis alle 7 forfattere av denne artikkelen] (2021). Biophysically detailed forward modeling of the neural origin of EEG and MEG signals. NeuroImage. ISSN 1053-8119. 225. doi: 10.1016/j.neuroimage.2020.117467. Fulltekst i vitenarkiv
• Næss, Solveig; Halnes, Geir; Hagen, Espen; Hagler, Donald J.; Dale, Anders M. & Einevoll, Gaute [Vis alle 7 forfattere av denne artikkelen] (2020). Biophysical modeling of the neural origin of EEG and MEG signals. bioRxiv. ISSN 2692-8205. doi: 10.1101/2020.07.01.181875.
• Senk, Johanna; Korvasová, Karolína; Schuecker, Jannis; Hagen, Espen; Tetzlaff, Tom & Diesmann, Markus [Vis alle 7 forfattere av denne artikkelen] (2020). Conditions for wave trains in spiking neural networks. Physical Review Research (PRResearch). ISSN 2643-1564. 2(2). doi: 10.1103/PhysRevResearch.2.023174. Fulltekst i vitenarkiv Vis sammendrag

  Spatiotemporal patterns such as traveling waves are frequently observed in recordings of neural activity. The mechanisms underlying the generation of such patterns are largely unknown. Previous studies have investigated the existence and uniqueness of different types of waves or bumps of activity using neural-field models, phenomenological coarse-grained descriptions of neural-network dynamics. But it remains unclear how these insights can be transferred to more biologically realistic networks of spiking neurons, where individual neurons fire irregularly. Here, we employ mean-field theory to reduce a microscopic model of leaky integrate-and-fire (LIF) neurons with distance-dependent connectivity to an effective neural-field model. In contrast to existing phenomenological descriptions, the dynamics in this neural-field model depends on the mean and the variance in the synaptic input, both determining the amplitude and the temporal structure of the resulting effective coupling kernel. For the neural-field model we employ linear stability analysis to derive conditions for the existence of spatial and temporal oscillations and wave trains, that is, temporally and spatially periodic traveling waves. We first prove that wave trains cannot occur in a single homogeneous population of neurons, irrespective of the form of distance dependence of the connection probability. Compatible with the architecture of cortical neural networks, wave trains emerge in two-population networks of excitatory and inhibitory neurons as a combination of delay-induced temporal oscillations and spatial oscillations due to distance-dependent connectivity profiles. Finally, we demonstrate quantitative agreement between predictions of the analytically tractable neural-field model and numerical simulations of both networks of nonlinear rate-based units and networks of LIF neurons.

• Hagen, Espen; Chambers, Anna; Einevoll, Gaute; Pettersen, Klas Henning; Enger, Rune & Stasik, Alexander Johannes (2020). RippleNet: A Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection. bioRxiv. ISSN 2692-8205. doi: 10.1101/2020.05.11.087874. Fulltekst i vitenarkiv Vis sammendrag

  Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. SPW-R detection typically relies on hand-crafted feature extraction, and laborious manual curation is often required. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. The output prediction can be interpreted as the time-varying probability of SPW-R events for the duration of the input. A simple thresholding applied to the output probabilities is found to identify times of events with high precision. The reference implementation of the algorithm, named ‘RippleNet’, is open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.

• Skaar, Jan-Eirik Welle; Stasik, Alexander Johannes; Hagen, Espen; Ness, Torbjørn V & Einevoll, Gaute (2020). Estimation of neural network model parameters from local field potentials (LFPs). PLoS Computational Biology. ISSN 1553-734X. 16(3). doi: 10.1371/journal.pcbi.1007725. Fulltekst i vitenarkiv
• Senk, Johanna; Korvasová, Karolína; Schuekker, Jannis; Hagen, Espen; Tetzlaff, Tom & Diesmann, Markus [Vis alle 7 forfattere av denne artikkelen] (2019). Conditions for wave trains in spiking neural networks. arXiv.org. ISSN 2331-8422. doi: 10.1103/physrevresearch.2.023174.
• Mäki-Marttunen, Tuomo; Kaufmann, Tobias; Elvsåshagen, Torbjørn; Devor, Anna; Djurovic, Srdjan & Westlye, Lars Tjelta [Vis alle 23 forfattere av denne artikkelen] (2019). Biophysical Psychiatry—How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders. Frontiers in Psychiatry. ISSN 1664-0640. 10. doi: 10.3389/fpsyt.2019.00534. Fulltekst i vitenarkiv
• Mäki-Marttunen, Tuomo; Krull, Florian; Bettella, Francesco; Hagen, Espen; Næss, Solveig & Ness, Torbjørn V [Vis alle 16 forfattere av denne artikkelen] (2019). Alterations in schizophrenia-associated genes can lead to increased power in delta oscillations. Cerebral Cortex. ISSN 1047-3211. 29(2), s. 875–891. doi: 10.1093/cercor/bhy291. Fulltekst i vitenarkiv
• Buccino, Alessio Paolo; Hagen, Espen; Einevoll, Gaute; Häfliger, Philipp & Cauwenberghs, Gert (2018). Independent Component Analysis for Fully Automated Multi-Electrode Array Spike Sorting. I Lovell, Nigel (Red.), Annual Conference of the IEEE Engineering in Medicine and Biology Society . IEEE conference proceedings. ISSN 9781538636473. s. 2627–2630. doi: 10.1109/EMBC.2018.8512788.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2018). Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0. Frontiers in Neuroinformatics. ISSN 1662-5196. 12(92), s. 1–36. doi: 10.3389/fninf.2018.00092. Fulltekst i vitenarkiv Vis sammendrag

  Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed. This allows for the development of computational tools implementing forward models grounded in the biophysics underlying electrical and magnetic measurement modalities. LFPy (LFPy.readthedocs.io) incorporated a well-established scheme for predicting extracellular potentials of individual neurons with arbitrary levels of biological detail. It relies on NEURON (neuron.yale.edu) to compute transmembrane currents of multicompartment neurons which is then used in combination with an electrostatic forward model. Its functionality is now extended to allow for modeling of networks of multicompartment neurons with concurrent calculations of extracellular potentials and current dipole moments. The current dipole moments are then, in combination with suitable volume-conductor head models, used to compute non-invasive measures of neuronal activity, like scalp potentials (electroencephalographic recordings; EEG) and magnetic fields outside the head (magnetoencephalographic recordings; MEG). One such built-in head model is the four-sphere head model incorporating the different electric conductivities of brain, cerebrospinal fluid, skull and scalp. We demonstrate the new functionality of the software by constructing a network of biophysically detailed multicompartment neuron models from the Neocortical Microcircuit Collaboration (NMC) Portal (bbp.epfl.ch/nmc-portal) with corresponding statistics of connections and synapses, and compute in vivo-like extracellular potentials (local field potentials, LFP; electrocorticographical signals, ECoG) and corresponding current dipole moments. From the current dipole moments we estimate corresponding EEG and MEG signals using the four-sphere head model. We also show strong scaling performance of LFPy with different numbers of message-passing interface (MPI) processes, and for different network sizes with different density of connections. The open-source software LFPy is equally suitable for execution on laptops and in parallel on high-performance computing (HPC) facilities and is publicly available on GitHub.com.

• Senk, Johanna; Hagen, Espen; Albada, Sacha van & Diesmann, Markus (2018). Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space. arXiv.org. ISSN 2331-8422.
• Senk, Johanna; Carde, Corto; Hagen, Espen; Kuhlen, Torsten W.; Diesmann, Markus & Weyers, Benjamin (2018). VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output. Frontiers in Neuroinformatics. ISSN 1662-5196. 12:75, s. 1–25. doi: 10.3389/fninf.2018.00075. Fulltekst i vitenarkiv
• Senk, Johanna; Korvasová, Karolína; Schuecker, Jannis; Hagen, Espen; Tetzlaff, Tom & Helias, Moritz [Vis alle 7 forfattere av denne artikkelen] (2018). Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and MEG signals with LFPy2.0. arXiv.org. ISSN 2331-8422.
• Lee, Jin Hyung; Carlson, David E.; Shokri Razaghi, Hooshmand; Yao, Weichi; Goetz, Georges A. & Hagen, Espen [Vis alle 10 forfattere av denne artikkelen] (2017). YASS: Yet another spike sorter. Advances in Neural Information Processing Systems. ISSN 1049-5258. 30, s. 4005–4015. doi: 10.1101/151928. Fulltekst i vitenarkiv
• Hagen, Espen; Fossum, Janne- Christine; Pettersen, Klas; Alonso, Jose-Manuel; Swadlow, Harvey A. & Einevoll, Gaute (2017). Focal local field potential signature of the single-axon monosynaptic thalamocortical connection. Journal of Neuroscience. ISSN 0270-6474. 37(20), s. 5123–5143. doi: 10.1523/JNEUROSCI.2715-16.2017. Fulltekst i vitenarkiv
• Hagen, Espen; Dahmen, David; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom & Van Albada, Sacha [Vis alle 9 forfattere av denne artikkelen] (2016). Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks. Cerebral Cortex. ISSN 1047-3211. 26(12), s. 4461–4496. doi: 10.1093/cercor/bhw237. Fulltekst i vitenarkiv Vis sammendrag

  With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.

• Heiberg, Thomas; Hagen, Espen; Halnes, Geir & Einevoll, Gaute (2016). Biophysical Network Modelling of the dLGN Circuit: Different Effects of Triadic and Axonal Inhibition on Visual Responses of Relay Cells. PLoS Computational Biology. ISSN 1553-734X. 12(5). doi: 10.1371/journal.pcbi.1004929. Fulltekst i vitenarkiv
• Hagen, Espen; Ness, Torbjørn; Khosrowshahi, Amir; Sørensen, Christina; Fyhn, Marianne & Hafting, Torkel [Vis alle 8 forfattere av denne artikkelen] (2015). ViSAPy: A Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms. Journal of Neuroscience Methods. ISSN 0165-0270. 245, s. 182–204. doi: 10.1016/j.jneumeth.2015.01.029.
• Linden, Henrik; Hagen, Espen; Leski, Szymon; Norheim, Eivind Skjønsberg; Pettersen, Klas Henning & Einevoll, Gaute Tomas (2014). LFPy: A tool for biophysical simulation of extracellular potentials generated by detailed model neurons. Frontiers in Neuroinformatics. ISSN 1662-5196. 7. doi: 10.3389/fninf.2013.00041.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn Vefferstad & Einevoll, Gaute (2013). LFPy: Multimodal Modeling of Extracellular Neuronal Recordings in Python. I Jaeger, Dieter & Jung, Ranu (Red.), Encyclopedia of Computational Neuroscience. Springer Science+Business Media B.V.. ISSN 978-1461466741. doi: 10.1007/978-1-4614-7320-6_100681-1. Vis sammendrag

  LFPy is an open-source tool for calculating brain signals such as extracellular potentials and magnetic signals from simulated activity in multicompartment neuron models, ranging from single cells to large neuronal networks. The tool is provided as a Python package and relies on the NEURON simulation environment.

• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2013). Multimodal Modeling of Extracellular Neuronal Recordings in Python. I Jaeger, Dieter & Jung, Ranu (Red.), Encyclopedia of Computational Neuroscience. Springer Science+Business Media B.V.. ISSN 978-1461466741. doi: 10.1007/978-1-4614-7320-6. Vis sammendrag

  LFPy is an open-source tool for calculating brain signals such as extracellular potentials and mag- netic signals from simulated activity in multi- compartment neuron models, ranging from single cells to large neuronal networks. The tool is provided as a Python package and relies on the NEURON simulation environment.

• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2013). LFPy: Multimodal Modeling of Extracellular Neuronal Recordings in Python. I Jaeger, Dieter & Jung, Ranu (Red.), Encyclopedia of Computational Neuroscience. Springer Science+Business Media B.V.. ISSN 978-1461466741. doi: https:/doi.org/10.1007/978-1-4614-7320-6.
• Ness, Torbjørn Bækø; Hagen, Espen; Negwer, Moritz; Bakker, Rembrandt; Schubert, Dirk & Einevoll, Gaute Tomas (2012). Modeling extracellular spikes and local field potentials recorded in MEAs. I Stett, Alfred & Zeck, Günther (Red.), Proceedings of the 8th International Meeting on Substrate-Integrated Microelectrode Arrays. University of Tübingen. ISSN 2194-5519.
• Einevoll, Gaute Tomas; Franke, Felix; Hagen, Espen; Pouzat, Chrostophe & Harris, Kenneth D. (2012). Towards reliable spike-train recordings from thousands of neurons with multielectrodes. Current Opinion in Neurobiology. ISSN 0959-4388. 22(1), s. 11–17. doi: 10.1016/j.conb.2011.10.001.

Se alle arbeider i Cristin

• Rimehaug, Atle Eskeland; Stasik, Alexander Johannes; Hagen, Espen; Billeh, Yazan; Siegle, Joshua H. & Dai, Kael [Vis alle 10 forfattere av denne artikkelen] (2022). Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex.
• Hagen, Espen (2022). Primer on LFPy.
• Einevoll, Gaute & Hagen, Espen (2022). Modelling local field potentials (theory and practice).
• Hagen, Espen; Ness, Torbjørn Vefferstad; Halnes, Geir & Einevoll, Gaute (2022). Brain signal predictions from multi-scale networks using a linearized framework.
• Hagen, Espen (2021). LFPy.
• Næss, Solveig; Halnes, Geir; Hagen, Espen; Hagler, Donald J.; Dale, Anders M. & Einevoll, Gaute [Vis alle 7 forfattere av denne artikkelen] (2019). Biophysical modelling of EEG signals.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2019). LFPy: Multimodal Modeling of Extracellular Neuronal Recordings in Python.
• Einevoll, Gaute & Hagen, Espen (2019). Tutorial: Biophysical modeling of extracellular potentials (using LFPy).
• Hagen, Espen; Stasik, Alexander Johannes; Billeh, Yazan N.; Siegle, Joshua H.; Dai, Kael & Rimehaug, Atle Eskeland [Vis alle 14 forfattere av denne artikkelen] (2019). Comparing spikes and the local field potential (LFP) in V1 between experimental data and a comprehensive biophysical model.
• Hagen, Espen (2019). Forward-model based generation of test data with ground truth for spike-sorting algorithms using ViSAPy and LFPy.
• Hagen, Espen; Stasik, Alexander Johannes; Billeh, Yazan; Siegle, Joshua H.; Dai, Kael & Rimehaug, Atle Eskeland [Vis alle 14 forfattere av denne artikkelen] (2019). Comparing spikes and the local field potential (LFP) in V1 between experimental data and a comprehensive biophysical model.
• Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute (2019). Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
• Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute (2019). Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
• Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute (2019). Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
• Næss, Solveig; Ness, Torbjørn V; Hagen, Espen & Einevoll, Gaute (2018). Biophysical modelling of EEG signals from large neural populations.
• Stasik, Alexander Johannes; Mobarhan, Milad; Hagen, Espen & Einevoll, Gaute (2018). Generating Input from LGN for Simulation of Visual Cortex.
• Senk, Johanna; Hagen, Espen; Van albada, Sacha & Diesmann, Markus (2018). A mesoscopic layered cortical network model for spiking activity and local field potentials.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2018). Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and MEG signals with LFPy 2.0.
• Hagen, Espen (2018). Collaborating w. Git.
• Hagen, Espen (2018). Modeling networks of spiking neurons using NEST (v2.14.0).
• Hagen, Espen (2018). Modeling what you can measure with LFPy 2.0 - Primer on LFPy 2.0.
• Hagen, Espen (2018). Primer on LFPy2.0 - CNS 2018 Tutorial T4 part II .
• Skaar, Jan-Eirik Welle; Hagen, Espen; Stasik, Alexander Johannes; Einevoll, Gaute & Ness, Torbjørn V (2018). Estimation of model parameters from LFPs of spiking neuron networks using deep learning.
• von Papen, Michael; Voges, Nicole; Dabrowska, Paulina; Senk, Johanna; Hagen, Espen & Diesmann, Markus [Vis alle 12 forfattere av denne artikkelen] (2018). Quantitative comparison of a mesocircuit model with motor cortical resting state activity in the macaque monkey.
• Berthet, Pierre; Hagen, Espen; Ness, Torbjørn V & Einevoll, Gaute (2018). Selectivity and sensitivity of cortical neurons to electric stimulation using ECoG electrode arrays.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2018). Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and MEG signals with LFPy2.0.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2017). Computing brain signals (CBra): Concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2017). Computing Brain Signals: concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2017). Computing Brain Signals (CBra): Concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
• Senk, Johanna; Korvasová, Karolína; Schuecker, Jannis; Hagen, Espen; Tetzlaff, Tom & Diesmann, Markus [Vis alle 7 forfattere av denne artikkelen] (2017). Conditions for traveling waves in spiking neural networks obtained from a rigorous mapping to a neural-field model.
• Voges, Nicole; Dabrovska, Paulina; Senk, Johanna; Hagen, Espen; Riehle, Alexa & Brochier, Thomas [Vis alle 7 forfattere av denne artikkelen] (2017). Characterization of resting state dynamics in monkey motor cortex.
• Lee, Jin Hyung; Carlson, David E.; Shokri Razaghi, Hooshmand; Yao, Weichi; Goetz, Georges A. & Hagen, Espen [Vis alle 10 forfattere av denne artikkelen] (2017). YASS: Yet Another Spike Sorter.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn Vefferstad & Einevoll, Gaute (2017). Computing Brain Signals: Concurrent simulation of network activity, extracellular electric poten- tials and magnetic fields.
• Hagen, Espen (2017). Primer on LFPy: CNS 2017 Tutorial T4 part II.
• Hagen, Espen (2017). Modeling networks of spiking neurons using NEST (v2.12.0).
• Hagen, Espen (2017). collaborating with git.
• Hagen, Espen (2017). Primer on LFPy.
• Hagen, Espen (2017). Primer on Python & NEURON.
• Hagen, Espen; Dahmen, David; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom & Van albada, Sacha [Vis alle 9 forfattere av denne artikkelen] (2015). Hybrid scheme for modeling local field potentials from point-neuron networks.
• Hagen, Espen; Dahmen, David; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom & Van albada, Sacha [Vis alle 9 forfattere av denne artikkelen] (2015). Hybrid scheme for modeling local field potentials from point-neuron networks.
• Dahmen, David; Hagen, Espen; Stavrinou, Maria; Lindén, Henrik; Tetzlaff, Tom & van Albada, Sacha [Vis alle 9 forfattere av denne artikkelen] (2014). Computing local-field potentials based on a point-neuron network model of cat V1.
• Dahmen, David; Hagen, Espen; Stavrinou, Maria; Lindén, Henrik; Tetzlaff, Tom & van Albada, Sacha [Vis alle 9 forfattere av denne artikkelen] (2014). From spiking point-neuron networks to LFPs, a hybrid approach.
• Heiberg, Thomas; Hagen, Espen; Halnes, Geir; Plesser, Hans Ekkehard & Einevoll, Gaute Tomas (2013). Biophysically detailed network model for area summation in LGN relay cells.
• Hagen, Espen; Ness, Torbjørn Bækø; Khosrowshahi, Amir; Sørensen, Christina; Fyhn, Marianne & Hafting, Torkel [Vis alle 8 forfattere av denne artikkelen] (2013). Modeling of extracellular spike signatures.
• Hagen, Espen; Stavrinou, Maria; Linden, Henrik; Dahmen, David; Tetzlaff, Tom & Albada, Sacha van [Vis alle 9 forfattere av denne artikkelen] (2013). Hybrid scheme for modeling LFPs from spiking cortical network models.
• Dahmen, David; Hagen, Espen; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom & Albada, Sacha van [Vis alle 9 forfattere av denne artikkelen] (2013). From spiking point-neuron networks to LFPs: a hybrid approach.
• Ness, Torbjørn Bækø; Negwer, Moritz; Hagen, Espen; Schubert, Dirk; Bakker, Rembrandt & Einevoll, Gaute Tomas (2013). Modeling Extracellular Potentials in Microelectrode Array Recordings.
• Ness, Torbjørn Bækø; Hagen, Espen; Negwer, Moritz; Bakker, Rembrant; Schubert, Dirk & Einevoll, Gaute Tomas (2013). Modeling Extracellular Potentials in Microelectrode Array Recording.
• Ness, Torbjørn Bækø; Hagen, Espen; Negwer, Moritz; Bakker, Rembrant; Schubert, Dirk & Einevoll, Gaute Tomas (2013). Modeling Extracellular Potentials in Microelectrode Array Recordings.
• Hagen, Espen; Ness, Torbjørn Bækø; Khosrowshahi, Amir; Sørensen, Christina; Fyhn, Torkel Hafting & Franke, Felix [Vis alle 7 forfattere av denne artikkelen] (2013). Generation of biophysically realistic model extracellular recordings for evaluation of spike sorting algorithms.
• Hagen, Espen; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom; van Albada, Sacha & Dahmen, David [Vis alle 9 forfattere av denne artikkelen] (2013). Hybrid scheme for modeling LFPs from spiking cortical network models.
• Ness, Torbjørn Bækø; Hagen, Espen; Negwer, Moritz; Bakker, Rembrandt; Schubert, Dirk & Einevoll, Gaute Tomas (2012). Modeling Extracellular Spikes and Local Field Potentials Recorded in MEAs.
• Hagen, Espen; Leski, Szymon; Linden, Henrik; Norheim, Eivind Skjønsberg; Pettersen, Klas Henning & Einevoll, Gaute Tomas (2012). LFPy-tutorial CNS2012.
• Hagen, Espen; Khosrowshahi, Amir & Ness, Torbjørn Bækø (2012). Modeling realistic extracellular recordings of neuronal populations for the purpose of evaluating automatic spike-sorting algorithms.
• Hagen, Espen & Ness, Torbjørn Bækø (2012). Modeling the Effect of Neuronal Dendritic Structure on Potentials Recorded by Multi-Electrode Arrays (MEAs).
• Meier, Philipp; Franke, Felix; Hagen, Espen; Einevoll, Gaute Tomas & Obermayer, Klaus (2012). A simulation framework for acute extracellular recordings.
• Franke, Felix; Meier, Philipp; Sobolev, Andrey; Hagen, Espen; Hierlemann, Andreas & Obermayer, Klaus [Vis alle 8 forfattere av denne artikkelen] (2012). Automatic spike sorting evaluation: A website based community approach.
• Meier, Philipp; Franke, Felix; Hagen, Espen; Einevoll, Gaute Tomas & Obermayer, Klaus (2012). A simulation framework for acute extracellular recordings.
• Hagen, Espen; Khosrowshahi, Amir; Ness, Torbjørn Bækø; Franke, Felix & Einevoll, Gaute Tomas (2012). Modeling realistic extracellular recordings of neuronal populations for the purpose of evaluating automatic spike-sorting algorithms.
• Hagen, Espen; Khosrowshahi, Amir; Ness, Torbjørn Bækø; Franke, Felix & Einevoll, Gaute Tomas (2012). Modeling realistic extracellular spiking activity in populations of neurons for the purpose of evaluating automatic spike-sorting algorithms.
• Hagen, Espen & Linden, Henrik (2011). Biophysically detailed test data from LFPy.
• Hagen, Espen; Fossum, Janne- Christine; Pettersen, Klas Henning; Alonso, Jose-Manuel; Swadlow, Harvey A. & Einevoll, Gaute Tomas (2011). Modeling the local-field potential footprint of thalamocortical inputs to sensory cortex.
• Linden, Henrik; Hagen, Espen; Leski, Szymon; Norheim, Eivind Skjønsberg; Pettersen, Klas Henning & Einevoll, Gaute Tomas (2011). LFPy: A tool for simulation of extracellular potentials.
• Hagen, Espen; Fossum, Janne- Christine; Pettersen, Klas Henning; Alonso, Jose-Manuel; Swadlow, Harvey A. & Einevoll, Gaute Tomas (2011). Modeling the LFP footprint of unitary thalamic inputs to sensory cortex.
• Hagen, Espen; Pettersen, Klas Henning; Fossum, Janne- Christine; Alonso, Jose-Manuel; Swadlow, Harvey A. & Einevoll, Gaute Tomas (2011). Modeling the spatiotemporal local-field potential footprint of unitary thalamocortical inputs to sensory cortex.
• Franke, Felix; Meier, Philipp; Natora, Michal; Hagen, Espen; Pettersen, Klas & Lindén, Henrik [Vis alle 9 forfattere av denne artikkelen] (2010). A simulated extracellular recording environment for the evaluation of automated electrode positioning systems and spike sorting algorithms.
• Franke, Felix; Meier, Philipp; Natora, Michal; Hagen, Espen; Pettersen, Klas & Lindén, Henrik [Vis alle 9 forfattere av denne artikkelen] (2010). Online3D simulation of extracellular recordings with morphologically reconstructed neurons.

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